From c38fbcc6a66cc88e9032c84084ac3d8bc1de8c61 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <nobody@tensorflow.org>
Date: Thu, 26 May 2016 05:20:40 -0800
Subject: Update generated Python Op docs. Change: 123313010

---
 .../shard0/tf.AggregationMethod.md                 |  10 +
 .../functions_and_classes/shard0/tf.Assert.md      |  29 +
 .../functions_and_classes/shard0/tf.FIFOQueue.md   |  41 +
 .../shard0/tf.FixedLenFeature.md                   |  31 +
 .../shard0/tf.FixedLengthRecordReader.md           | 148 ----
 .../functions_and_classes/shard0/tf.NoGradient.md  |  23 -
 .../shard0/tf.RegisterGradient.md                  |  36 -
 .../shard0/tf.SparseTensorValue.md                 |  22 -
 .../functions_and_classes/shard0/tf.TensorShape.md | 316 --------
 .../shard0/tf.accumulate_n.md                      |  37 -
 .../shard0/tf.assert_equal.md                      |  35 -
 .../functions_and_classes/shard0/tf.assert_less.md |  35 -
 .../shard0/tf.assert_negative.md                   |  33 -
 .../shard0/tf.assert_non_negative.md               |  34 -
 .../shard0/tf.assert_rank_at_least.md              |  37 -
 .../functions_and_classes/shard0/tf.assert_type.md |  15 +
 .../shard0/tf.batch_ifft2d.md                      |  18 +
 .../shard0/tf.batch_matrix_band_part.md            |  60 ++
 .../shard0/tf.batch_to_space.md                    |  37 -
 .../functions_and_classes/shard0/tf.bytes.md       |   4 +
 .../functions_and_classes/shard0/tf.concat.md      |  45 ++
 .../shard0/tf.contrib.copy_graph.get_copied_op.md  |  18 +
 .../tf.contrib.distributions.BaseDistribution.md   | 195 -----
 .../shard0/tf.contrib.distributions.Chi2.md        | 260 +++++++
 .../shard0/tf.contrib.layers.l2_regularizer.md     |  22 -
 .../shard0/tf.contrib.layers.optimize_loss.md      |  43 ++
 .../shard0/tf.contrib.layers.xavier_initializer.md |  29 +
 .../shard0/tf.contrib.learn.BaseEstimator.md       | 189 -----
 .../tf.contrib.learn.TensorFlowClassifier.md       | 279 -------
 .../tf.contrib.learn.TensorFlowRNNClassifier.md    | 312 ++++++++
 .../shard0/tf.contrib.learn.evaluate.md            |  44 ++
 .../shard0/tf.contrib.learn.extract_dask_data.md   |   4 +
 .../shard0/tf.contrib.learn.extract_pandas_data.md |   4 +
 .../shard0/tf.contrib.learn.read_batch_examples.md |  39 -
 .../shard0/tf.contrib.learn.run_feeds.md           |  28 -
 .../shard0/tf.contrib.learn.train.md               |  62 ++
 .../tf.contrib.metrics.streaming_accuracy.md       |  51 ++
 ...ontrib.metrics.streaming_mean_absolute_error.md |  48 ++
 ...trib.metrics.streaming_sparse_precision_at_k.md |  60 --
 ...contrib.metrics.streaming_sparse_recall_at_k.md |  59 --
 .../shard0/tf.decode_json_example.md               |  25 -
 .../shard0/tf.delete_session_tensor.md             |  19 -
 .../python/functions_and_classes/shard0/tf.div.md  |  15 +
 .../shard0/tf.dynamic_stitch.md                    |  53 --
 .../python/functions_and_classes/shard0/tf.erfc.md |  14 +
 .../shard0/tf.errors.AlreadyExistsError.md         |  14 +
 .../shard0/tf.errors.InvalidArgumentError.md       |  17 +
 .../shard0/tf.errors.UnavailableError.md           |  11 +
 .../functions_and_classes/shard0/tf.greater.md     |  15 -
 .../functions_and_classes/shard0/tf.group.md       |  25 +
 .../shard0/tf.image.encode_jpeg.md                 |  51 --
 .../shard0/tf.image.extract_glimpse.md             |  31 -
 .../shard0/tf.image.random_brightness.md           |  25 -
 .../shard0/tf.image.random_contrast.md             |  26 +
 .../shard0/tf.image.resize_area.md                 |  24 +
 .../shard0/tf.image.resize_images.md               |  43 --
 .../shard0/tf.image.transpose_image.md             |  20 -
 .../shard0/tf.initialize_local_variables.md        |  10 +
 .../python/functions_and_classes/shard0/tf.inv.md  |  16 +
 .../shard0/tf.matching_files.md                    |  17 -
 .../shard0/tf.matrix_solve_ls.md                   |  47 ++
 .../shard0/tf.merge_all_summaries.md               |  16 +
 .../shard0/tf.nn.batch_normalization.md            |  46 ++
 .../functions_and_classes/shard0/tf.nn.nce_loss.md |  53 --
 .../functions_and_classes/shard0/tf.nn.relu.md     |  14 -
 .../tf.nn.softmax_cross_entropy_with_logits.md     |  36 -
 .../functions_and_classes/shard0/tf.nn.softplus.md |  14 -
 .../functions_and_classes/shard0/tf.nn.top_k.md    |  31 -
 .../shard0/tf.nn.uniform_candidate_sampler.md      |  49 --
 .../functions_and_classes/shard0/tf.no_op.md       |  13 +
 .../shard0/tf.parse_single_example.md              |  35 -
 .../functions_and_classes/shard0/tf.polygamma.md   |  22 +
 .../python/functions_and_classes/shard0/tf.pow.md  |  24 -
 .../functions_and_classes/shard0/tf.reduce_any.md  |  35 -
 .../functions_and_classes/shard0/tf.reduce_prod.md |  25 -
 .../shard0/tf.report_uninitialized_variables.md    |  19 +
 .../shard0/tf.reset_default_graph.md               |  10 -
 .../shard0/tf.reverse_sequence.md                  |  76 ++
 .../functions_and_classes/shard0/tf.round.md       |  21 -
 .../shard0/tf.self_adjoint_eig.md                  |  21 +
 .../shard0/tf.set_random_seed.md                   |  98 ---
 .../functions_and_classes/shard0/tf.shape.md       |  23 +
 .../python/functions_and_classes/shard0/tf.sign.md |  18 -
 .../shard0/tf.space_to_batch.md                    |  44 --
 .../shard0/tf.sparse_merge.md                      |  73 --
 .../shard0/tf.sparse_segment_sqrt_n_grad.md        |  24 -
 .../shard0/tf.sparse_to_dense.md                   |  45 --
 .../shard0/tf.squared_difference.md                |  15 -
 .../functions_and_classes/shard0/tf.squeeze.md     |  38 -
 .../shard0/tf.string_to_hash_bucket_fast.md        |  23 +
 .../shard0/tf.string_to_number.md                  |  20 -
 .../python/functions_and_classes/shard0/tf.tanh.md |  16 -
 .../functions_and_classes/shard0/tf.test.main.md   |   4 +
 .../functions_and_classes/shard0/tf.to_double.md   |  19 +
 .../shard0/tf.train.AdadeltaOptimizer.md           |  23 +
 .../shard0/tf.train.GradientDescentOptimizer.md    |  18 -
 .../shard0/tf.train.LooperThread.loop.md           |  22 +
 .../shard0/tf.train.QueueRunner.md                 | 161 ++++
 .../shard0/tf.train.RMSPropOptimizer.md            |  23 +
 .../shard0/tf.train.batch_join.md                  |  79 --
 .../tf.train.generate_checkpoint_state_proto.md    |  20 +
 .../shard0/tf.train.limit_epochs.md                |  21 +
 .../shard0/tf.truncated_normal.md                  |  27 +
 .../shard0/tf.unique_with_counts.md                |  36 -
 .../shard0/tf.variable_axis_size_partitioner.md    |  37 +
 .../shard0/tf.variable_scope.md                    |  82 --
 .../functions_and_classes/shard0/tf.zeros_like.md  |  28 -
 .../functions_and_classes/shard1/tf.Assert.md      |  29 -
 .../shard1/tf.QueueBase.from_list.md               |  21 -
 .../functions_and_classes/shard1/tf.QueueBase.md   | 268 +++++++
 .../shard1/tf.RandomShuffleQueue.md                |  54 ++
 .../shard1/tf.TextLineReader.md                    | 148 ----
 .../shard1/tf.VariableScope.md                     | 105 +++
 .../shard1/tf.assert_negative.md                   |  33 +
 .../functions_and_classes/shard1/tf.assert_rank.md |  36 -
 .../shard1/tf.batch_matrix_determinant.md          |  19 -
 .../shard1/tf.batch_matrix_diag.md                 |  42 +
 .../shard1/tf.batch_matrix_solve.md                |  27 -
 .../shard1/tf.batch_matrix_solve_ls.md             |  56 ++
 .../shard1/tf.batch_self_adjoint_eig.md            |  22 +
 .../shard1/tf.cholesky_solve.md                    |  35 -
 .../functions_and_classes/shard1/tf.complex_abs.md |  26 -
 .../functions_and_classes/shard1/tf.constant.md    |  50 --
 .../shard1/tf.constant_initializer.md              |  20 +
 .../shard1/tf.contrib.distributions.Exponential.md | 260 -------
 .../tf.contrib.distributions.MultivariateNormal.md | 218 ------
 .../tf.contrib.layers.summarize_collection.md      |   4 -
 .../shard1/tf.contrib.layers.summarize_tensors.md  |   4 +
 ....contrib.layers.variance_scaling_initializer.md |  47 --
 .../shard1/tf.contrib.learn.Estimator.md           | 215 ------
 .../tf.contrib.learn.TensorFlowClassifier.md       | 279 +++++++
 .../shard1/tf.contrib.learn.TensorFlowEstimator.md | 295 +++++++
 .../tf.contrib.learn.TensorFlowLinearRegressor.md  | 279 -------
 .../shard1/tf.contrib.learn.extract_pandas_data.md |   4 -
 .../tf.contrib.learn.extract_pandas_matrix.md      |   4 +
 .../shard1/tf.contrib.learn.read_batch_features.md |  43 ++
 .../tf.contrib.metrics.streaming_accuracy.md       |  51 --
 ...ontrib.metrics.streaming_mean_relative_error.md |  49 ++
 ...contrib.metrics.streaming_mean_squared_error.md |  48 ++
 .../tf.contrib.metrics.streaming_recall_at_k.md    |  52 ++
 .../shard1/tf.contrib.util.constant_value.md       |  31 -
 .../functions_and_classes/shard1/tf.cross.md       |  22 +
 .../shard1/tf.decode_json_example.md               |  25 +
 .../shard1/tf.delete_session_tensor.md             |  19 +
 .../python/functions_and_classes/shard1/tf.div.md  |  15 -
 .../shard1/tf.errors.CancelledError.md             |  17 +
 .../shard1/tf.errors.DataLossError.md              |  13 -
 .../shard1/tf.errors.FailedPreconditionError.md    |  13 -
 .../python/functions_and_classes/shard1/tf.fft.md  |  14 +
 .../shard1/tf.get_collection.md                    |  25 +
 .../functions_and_classes/shard1/tf.group.md       |  25 -
 .../shard1/tf.histogram_summary.md                 |  25 -
 .../functions_and_classes/shard1/tf.igamma.md      |  29 -
 .../shard1/tf.image.convert_image_dtype.md         |  32 -
 .../shard1/tf.image.decode_jpeg.md                 |  41 +
 .../shard1/tf.image.flip_left_right.md             |  23 -
 .../shard1/tf.image.flip_up_down.md                |  23 +
 .../shard1/tf.image.hsv_to_rgb.md                  |  21 -
 .../shard1/tf.image.random_brightness.md           |  25 +
 .../shard1/tf.image.random_saturation.md           |  27 -
 .../shard1/tf.image.resize_area.md                 |  24 -
 .../shard1/tf.image.resize_bicubic.md              |  24 +
 .../shard1/tf.import_graph_def.md                  |  49 --
 .../shard1/tf.initialize_variables.md              |  24 -
 .../functions_and_classes/shard1/tf.is_inf.md      |  14 -
 .../functions_and_classes/shard1/tf.linspace.md    |  28 +
 .../functions_and_classes/shard1/tf.listdiff.md    |  40 +
 .../shard1/tf.local_variables.md                   |   8 -
 .../python/functions_and_classes/shard1/tf.log.md  |  16 +
 .../shard1/tf.matrix_solve.md                      |  21 +
 .../functions_and_classes/shard1/tf.maximum.md     |  15 +
 .../shard1/tf.merge_summary.md                     |  26 -
 .../python/functions_and_classes/shard1/tf.mul.md  |  15 +
 .../shard1/tf.nn.atrous_conv2d.md                  | 107 +++
 .../shard1/tf.nn.conv2d_transpose.md               |  34 +
 .../functions_and_classes/shard1/tf.nn.dropout.md  |  38 +
 .../functions_and_classes/shard1/tf.nn.l2_loss.md  |  19 -
 .../shard1/tf.nn.max_pool_with_argmax.md           |  30 -
 .../tf.nn.sigmoid_cross_entropy_with_logits.md     |  48 ++
 .../tf.nn.softmax_cross_entropy_with_logits.md     |  36 +
 ....nn.sparse_softmax_cross_entropy_with_logits.md |  38 +
 .../tf.nn.weighted_cross_entropy_with_logits.md    |  52 ++
 .../shard1/tf.no_regularizer.md                    |   4 -
 .../python/functions_and_classes/shard1/tf.pack.md |  23 +
 .../functions_and_classes/shard1/tf.placeholder.md |  34 -
 .../python/functions_and_classes/shard1/tf.pow.md  |  24 +
 .../functions_and_classes/shard1/tf.py_func.md     |  31 +
 .../shard1/tf.random_uniform.md                    |  41 -
 .../python/functions_and_classes/shard1/tf.rank.md |  28 -
 .../python/functions_and_classes/shard1/tf.real.md |  28 -
 .../functions_and_classes/shard1/tf.reduce_prod.md |  25 +
 .../shard1/tf.report_uninitialized_variables.md    |  19 -
 .../functions_and_classes/shard1/tf.reverse.md     |  61 ++
 .../python/functions_and_classes/shard1/tf.scan.md |  44 ++
 .../shard1/tf.segment_prod.md                      |  31 +
 .../shard1/tf.self_adjoint_eig.md                  |  21 -
 .../functions_and_classes/shard1/tf.shape.md       |  23 -
 .../functions_and_classes/shard1/tf.shape_n.md     |  16 +
 .../shard1/tf.squared_difference.md                |  15 +
 .../shard1/tf.string_to_hash_bucket.md             |  21 -
 .../python/functions_and_classes/shard1/tf.sub.md  |  15 +
 .../shard1/tf.test.get_temp_dir.md                 |  10 -
 .../shard1/tf.test.is_built_with_cuda.md           |   4 +
 .../python/functions_and_classes/shard1/tf.tile.md |  22 -
 .../functions_and_classes/shard1/tf.to_int64.md    |  19 +
 .../shard1/tf.train.AdadeltaOptimizer.md           |  23 -
 .../shard1/tf.train.AdagradOptimizer.md            |  26 +
 .../shard1/tf.train.GradientDescentOptimizer.md    |  18 +
 .../shard1/tf.train.LooperThread.loop.md           |  22 -
 .../shard1/tf.train.global_step.md                 |  27 +
 .../shard1/tf.train.match_filenames_once.md        |  14 -
 .../shard1/tf.train.replica_device_setter.md       |  50 --
 .../shard1/tf.train.slice_input_producer.md        |  35 -
 .../shard1/tf.train.start_queue_runners.md         |  24 +
 .../shard1/tf.train.string_input_producer.md       |  32 -
 .../shard1/tf.truncated_normal.md                  |  27 -
 .../shard1/tf.unique_with_counts.md                |  36 +
 .../shard1/tf.verify_tensor_all_finite.md          |  15 +
 .../shard2/tf.DeviceSpec.from_string.md            |  18 -
 .../functions_and_classes/shard2/tf.DeviceSpec.md  | 146 ----
 .../shard2/tf.IdentityReader.md                    | 148 ----
 .../shard2/tf.InteractiveSession.md                |  68 ++
 .../shard2/tf.QueueBase.from_list.md               |  21 +
 .../shard2/tf.RandomShuffleQueue.md                |  54 --
 .../shard2/tf.RegisterShape.md                     |  27 -
 .../functions_and_classes/shard2/tf.Session.md     | 236 ------
 .../shard2/tf.TFRecordReader.md                    | 145 ++++
 .../shard2/tf.Variable.from_proto.md               |   4 +
 .../shard2/tf.WholeFileReader.md                   | 148 ----
 .../python/functions_and_classes/shard2/tf.abs.md  |  22 -
 .../shard2/tf.all_variables.md                     |  12 +
 .../shard2/tf.assert_less_equal.md                 |  35 +
 .../shard2/tf.batch_cholesky_solve.md              |  35 -
 .../functions_and_classes/shard2/tf.batch_fft.md   |  18 +
 .../shard2/tf.batch_ifft3d.md                      |  18 -
 .../shard2/tf.batch_matrix_inverse.md              |  28 -
 .../shard2/tf.batch_self_adjoint_eig.md            |  22 -
 .../functions_and_classes/shard2/tf.bitcast.md     |  25 -
 .../shard2/tf.boolean_mask.md                      |  43 --
 .../python/functions_and_classes/shard2/tf.ceil.md |  14 -
 .../shard2/tf.check_numerics.md                    |  18 -
 .../shard2/tf.cholesky_solve.md                    |  35 +
 .../functions_and_classes/shard2/tf.complex.md     |  30 -
 .../functions_and_classes/shard2/tf.complex_abs.md |  26 +
 .../python/functions_and_classes/shard2/tf.cond.md |  54 ++
 .../python/functions_and_classes/shard2/tf.conj.md |  28 +
 .../functions_and_classes/shard2/tf.constant.md    |  50 ++
 .../tf.contrib.distributions.MultivariateNormal.md | 218 ++++++
 .../shard2/tf.contrib.layers.fully_connected.md    |  46 --
 .../tf.contrib.layers.summarize_activations.md     |   4 +
 .../tf.contrib.layers.xavier_initializer_conv2d.md |  29 +
 .../shard2/tf.contrib.learn.RunConfig.md           |  47 --
 .../tf.contrib.learn.TensorFlowDNNClassifier.md    | 302 ++++++++
 .../tf.contrib.learn.TensorFlowLinearClassifier.md | 279 +++++++
 .../tf.contrib.learn.extract_pandas_matrix.md      |   4 -
 .../shard2/tf.contrib.learn.infer.md               |   4 +
 .../tf.contrib.learn.read_batch_record_features.md |  33 +
 .../shard2/tf.contrib.metrics.set_difference.md    |  24 +
 ...ontrib.metrics.streaming_mean_relative_error.md |  49 --
 .../tf.contrib.metrics.streaming_recall_at_k.md    |  52 --
 .../functions_and_classes/shard2/tf.decode_csv.md  |  26 -
 .../shard2/tf.depth_to_space.md                    |  95 +++
 .../python/functions_and_classes/shard2/tf.erf.md  |  14 +
 .../shard2/tf.errors.InternalError.md              |  12 +
 .../shard2/tf.errors.UnauthenticatedError.md       |  11 +
 .../shard2/tf.errors.UnknownError.md               |  15 +
 .../python/functions_and_classes/shard2/tf.exp.md  |  14 +
 .../functions_and_classes/shard2/tf.fft2d.md       |  14 -
 .../functions_and_classes/shard2/tf.floor.md       |  14 -
 .../functions_and_classes/shard2/tf.floordiv.md    |  32 -
 .../functions_and_classes/shard2/tf.gather_nd.md   |  30 -
 .../shard2/tf.get_default_session.md               |  16 +
 .../shard2/tf.get_variable.md                      |  72 --
 .../python/functions_and_classes/shard2/tf.ifft.md |  15 -
 .../shard2/tf.image.decode_png.md                  |  30 -
 .../shard2/tf.image.per_image_whitening.md         |  29 -
 .../shard2/tf.image.random_flip_up_down.md         |  24 +
 .../shard2/tf.import_graph_def.md                  |  49 ++
 .../shard2/tf.initialize_all_variables.md          |  10 +
 .../shard2/tf.invert_permutation.md                |  30 -
 .../functions_and_classes/shard2/tf.is_nan.md      |  14 +
 .../shard2/tf.local_variables.md                   |   8 +
 .../python/functions_and_classes/shard2/tf.log.md  |  16 -
 .../functions_and_classes/shard2/tf.matmul.md      |  46 ++
 .../shard2/tf.matrix_inverse.md                    |  27 +
 .../shard2/tf.matrix_solve.md                      |  21 -
 .../shard2/tf.matrix_triangular_solve.md           |  35 +
 .../python/functions_and_classes/shard2/tf.mod.md  |  15 -
 .../functions_and_classes/shard2/tf.nn.dropout.md  |  38 -
 .../functions_and_classes/shard2/tf.nn.in_top_k.md |  33 +
 .../shard2/tf.nn.l2_normalize.md                   |  24 -
 .../tf.nn.learned_unigram_candidate_sampler.md     |  53 ++
 .../shard2/tf.nn.normalize_moments.md              |  20 -
 .../functions_and_classes/shard2/tf.nn.relu6.md    |  15 +
 .../python/functions_and_classes/shard2/tf.ones.md |  24 -
 .../python/functions_and_classes/shard2/tf.pack.md |  23 -
 .../shard2/tf.random_shuffle.md                    |  29 -
 .../shard2/tf.random_uniform_initializer.md        |  25 -
 .../functions_and_classes/shard2/tf.reduce_min.md  |  25 -
 .../functions_and_classes/shard2/tf.scalar_mul.md  |  23 -
 .../shard2/tf.scalar_summary.md                    |  21 -
 .../functions_and_classes/shard2/tf.segment_max.md |  30 -
 .../shard2/tf.segment_mean.md                      |  32 -
 .../functions_and_classes/shard2/tf.sigmoid.md     |  18 -
 .../shard2/tf.space_to_depth.md                    |  87 +++
 .../shard2/tf.sparse_segment_mean.md               |  27 -
 .../shard2/tf.stop_gradient.md                     |  34 +
 .../shard2/tf.test.assert_equal_graph_def.md       |  20 -
 .../shard2/tf.test.compute_gradient_error.md       |  36 +
 .../functions_and_classes/shard2/tf.to_bfloat16.md |  19 +
 .../functions_and_classes/shard2/tf.to_float.md    |  19 +
 .../shard2/tf.train.LooperThread.md                | 215 ------
 .../shard2/tf.train.MomentumOptimizer.md           |  18 +
 .../shard2/tf.train.Saver.from_proto.md            |   4 +
 .../functions_and_classes/shard2/tf.train.Saver.md | 315 ++++++++
 .../shard2/tf.train.SummaryWriter.md               | 170 +++++
 .../shard2/tf.train.export_meta_graph.md           |  24 +
 .../shard2/tf.train.range_input_producer.md        |  25 -
 .../shard2/tf.train.shuffle_batch.md               |  74 --
 .../shard2/tf.train.shuffle_batch_join.md          |  68 --
 .../shard2/tf.train.slice_input_producer.md        |  35 +
 .../shard2/tf.train.write_graph.md                 |  21 -
 .../functions_and_classes/shard2/tf.truediv.md     |  31 +
 .../shard2/tf.variable_op_scope.md                 |  56 ++
 .../functions_and_classes/shard3/tf.Graph.md       | 783 +++++++++++++++++++
 .../shard3/tf.IndexedSlices.md                     |  93 +++
 .../shard3/tf.SparseTensorValue.md                 |  22 +
 .../shard3/tf.WholeFileReader.md                   | 148 ++++
 .../functions_and_classes/shard3/tf.add_n.md       |  15 +
 .../shard3/tf.assert_rank_at_least.md              |  37 +
 .../shard3/tf.assert_variables_initialized.md      |  24 +
 .../shard3/tf.batch_cholesky.md                    |  20 +
 .../functions_and_classes/shard3/tf.batch_fft2d.md |  18 -
 .../functions_and_classes/shard3/tf.batch_fft3d.md |  18 +
 .../functions_and_classes/shard3/tf.batch_ifft.md  |  18 -
 .../shard3/tf.batch_matrix_band_part.md            |  60 --
 .../shard3/tf.batch_matrix_triangular_solve.md     |  39 +
 .../functions_and_classes/shard3/tf.bytes.md       |   4 -
 .../python/functions_and_classes/shard3/tf.cast.md |  30 +
 .../functions_and_classes/shard3/tf.complex.md     |  30 +
 .../python/functions_and_classes/shard3/tf.cond.md |  54 --
 ...f.contrib.distributions.DiscreteDistribution.md | 139 ++++
 .../shard3/tf.contrib.distributions.Uniform.md     | 216 ++++++
 ...ions.normal_conjugates_known_sigma_posterior.md |  48 --
 .../shard3/tf.contrib.ffmpeg.decode_audio.md       |  25 -
 .../tf.contrib.layers.apply_regularization.md      |  27 +
 .../shard3/tf.contrib.layers.sum_regularizer.md    |  14 +
 .../shard3/tf.contrib.layers.summarize_tensor.md   |  18 +
 .../tf.contrib.layers.xavier_initializer_conv2d.md |  29 -
 .../shard3/tf.contrib.learn.ModeKeys.md            |   7 -
 .../tf.contrib.learn.TensorFlowDNNRegressor.md     | 302 --------
 .../tf.contrib.learn.TensorFlowRNNRegressor.md     | 312 --------
 .../shard3/tf.contrib.learn.extract_dask_labels.md |   4 +
 .../shard3/tf.contrib.learn.read_batch_examples.md |  39 +
 .../tf.contrib.learn.read_batch_record_features.md |  33 -
 .../shard3/tf.contrib.metrics.accuracy.md          |  23 +
 .../tf.contrib.metrics.auc_using_histogram.md      |  38 -
 .../shard3/tf.contrib.metrics.set_union.md         |  23 +
 .../shard3/tf.contrib.metrics.streaming_auc.md     |  58 ++
 .../shard3/tf.contrib.util.make_ndarray.md         |  20 -
 .../tf.contrib.util.ops_used_by_graph_def.md       |  15 -
 .../tf.contrib.util.stripped_op_list_for_graph.md  |  23 +
 .../tf.convert_to_tensor_or_indexed_slices.md      |  27 -
 .../shard3/tf.depth_to_space.md                    |  95 ---
 .../python/functions_and_classes/shard3/tf.erf.md  |  14 -
 .../shard3/tf.errors.DeadlineExceededError.md      |  11 -
 .../shard3/tf.errors.NotFoundError.md              |  14 -
 .../python/functions_and_classes/shard3/tf.exp.md  |  14 -
 .../functions_and_classes/shard3/tf.gather_nd.md   |  30 +
 .../shard3/tf.get_collection_ref.md                |  20 +
 .../functions_and_classes/shard3/tf.gradients.md   |  48 --
 .../functions_and_classes/shard3/tf.greater.md     |  15 +
 .../functions_and_classes/shard3/tf.identity.md    |  14 -
 .../functions_and_classes/shard3/tf.ifft3d.md      |  15 -
 .../shard3/tf.image.adjust_contrast.md             |  29 -
 .../shard3/tf.image.adjust_hue.md                  |  26 +
 .../shard3/tf.image.adjust_saturation.md           |  25 -
 .../shard3/tf.image.extract_glimpse.md             |  31 +
 .../shard3/tf.image.grayscale_to_rgb.md            |  17 -
 .../shard3/tf.image.random_hue.md                  |  28 +
 .../shard3/tf.image_summary.md                     |  48 ++
 .../shard3/tf.is_non_decreasing.md                 |  25 +
 .../shard3/tf.is_strictly_increasing.md            |  26 +
 .../functions_and_classes/shard3/tf.less_equal.md  |  15 -
 .../shard3/tf.make_template.md                     | 105 ---
 .../functions_and_classes/shard3/tf.multinomial.md |  28 -
 .../shard3/tf.nn.avg_pool3d.md                     |  24 +
 .../functions_and_classes/shard3/tf.nn.bias_add.md |  24 -
 .../shard3/tf.nn.depthwise_conv2d.md               |  37 +
 .../shard3/tf.nn.embedding_lookup_sparse.md        |  66 ++
 .../tf.nn.fixed_unigram_candidate_sampler.md       |  75 ++
 .../shard3/tf.nn.l2_normalize.md                   |  24 +
 .../shard3/tf.nn.local_response_normalization.md   |  34 -
 .../shard3/tf.nn.log_uniform_candidate_sampler.md  |  56 --
 .../shard3/tf.nn.normalize_moments.md              |  20 +
 .../shard3/tf.nn.sampled_softmax_loss.md           |  49 --
 .../shard3/tf.nn.separable_conv2d.md               |  40 +
 .../functions_and_classes/shard3/tf.ones_like.md   |  28 -
 .../python/functions_and_classes/shard3/tf.pad.md  |  57 --
 .../shard3/tf.parse_single_example.md              |  35 +
 .../shard3/tf.placeholder_with_default.md          |  17 -
 .../functions_and_classes/shard3/tf.read_file.md   |  14 -
 .../functions_and_classes/shard3/tf.reduce_mean.md |  35 +
 .../shard3/tf.scalar_summary.md                    |  21 +
 .../functions_and_classes/shard3/tf.scatter_add.md |  46 ++
 .../functions_and_classes/shard3/tf.scatter_sub.md |  44 --
 .../functions_and_classes/shard3/tf.segment_max.md |  30 +
 .../shard3/tf.segment_mean.md                      |  32 +
 .../functions_and_classes/shard3/tf.segment_sum.md |  30 -
 .../functions_and_classes/shard3/tf.sparse_add.md  |  55 --
 .../shard3/tf.sparse_concat.md                     | 100 ---
 .../shard3/tf.sparse_fill_empty_rows.md            |  54 --
 .../shard3/tf.sparse_merge.md                      |  73 ++
 .../shard3/tf.sparse_placeholder.md                |  43 --
 .../shard3/tf.sparse_reset_shape.md                |  60 --
 .../shard3/tf.sparse_segment_mean.md               |  27 +
 .../shard3/tf.sparse_softmax.md                    |  51 ++
 .../functions_and_classes/shard3/tf.split.md       |  29 +
 .../functions_and_classes/shard3/tf.square.md      |  16 +
 .../shard3/tf.test.assert_equal_graph_def.md       |  20 +
 .../functions_and_classes/shard3/tf.to_bfloat16.md |  19 -
 .../functions_and_classes/shard3/tf.to_int32.md    |  19 -
 .../shard3/tf.train.Coordinator.md                 | 223 ++++++
 .../functions_and_classes/shard3/tf.train.Saver.md | 315 --------
 .../shard3/tf.train.Server.create_local_server.md  |  19 +
 .../shard3/tf.train.exponential_decay.md           |  54 ++
 .../shard3/tf.train.get_checkpoint_state.md        |  19 +
 .../shard3/tf.train.range_input_producer.md        |  25 +
 .../shard3/tf.train.write_graph.md                 |  21 +
 .../functions_and_classes/shard3/tf.tuple.md       |  36 +
 .../shard3/tf.unsorted_segment_sum.md              |  38 -
 .../functions_and_classes/shard3/tf.while_loop.md  |  60 --
 .../shard3/tf.zeros_initializer.md                 |   4 +
 .../functions_and_classes/shard4/tf.DType.md       | 206 -----
 .../shard4/tf.IndexedSlices.md                     |  93 ---
 .../shard4/tf.VariableScope.md                     | 105 ---
 .../shard4/tf.add_check_numerics_ops.md            |  13 +
 .../shard4/tf.assert_variables_initialized.md      |  24 -
 .../functions_and_classes/shard4/tf.batch_fft3d.md |  18 -
 .../python/functions_and_classes/shard4/tf.cast.md |  30 -
 .../functions_and_classes/shard4/tf.cholesky.md    |  22 +
 .../shard4/tf.contrib.distributions.Uniform.md     | 216 ------
 ...ons.normal_congugates_known_sigma_predictive.md |  55 ++
 .../tf.contrib.layers.apply_regularization.md      |  27 -
 .../shard4/tf.contrib.layers.l1_regularizer.md     |  22 +
 .../shard4/tf.contrib.layers.sum_regularizer.md    |  14 -
 .../tf.contrib.layers.summarize_activation.md      |  16 +
 .../tf.contrib.layers.summarize_collection.md      |   4 +
 .../shard4/tf.contrib.layers.summarize_tensors.md  |   4 -
 .../shard4/tf.contrib.learn.TensorFlowEstimator.md | 295 -------
 .../shard4/tf.contrib.learn.extract_dask_labels.md |   4 -
 .../shard4/tf.contrib.metrics.confusion_matrix.md  |  45 ++
 .../shard4/tf.contrib.metrics.set_union.md         |  23 -
 ...ntrib.metrics.streaming_mean_cosine_distance.md |  48 ++
 ...contrib.metrics.streaming_mean_squared_error.md |  48 --
 .../shard4/tf.contrib.util.constant_value.md       |  31 +
 .../tf.contrib.util.ops_used_by_graph_def.md       |  15 +
 .../shard4/tf.convert_to_tensor.md                 |  47 ++
 .../functions_and_classes/shard4/tf.device.md      |  19 +
 .../shard4/tf.dynamic_partition.md                 |  50 --
 .../shard4/tf.errors.CancelledError.md             |  17 -
 .../shard4/tf.errors.DeadlineExceededError.md      |  11 +
 .../shard4/tf.errors.OutOfRangeError.md            |  15 -
 .../shard4/tf.errors.PermissionDeniedError.md      |  14 -
 .../shard4/tf.errors.UnimplementedError.md         |  15 -
 .../functions_and_classes/shard4/tf.fft3d.md       |  14 +
 .../functions_and_classes/shard4/tf.gather.md      |  35 -
 .../shard4/tf.get_collection_ref.md                |  20 -
 .../functions_and_classes/shard4/tf.get_seed.md    |  22 +
 .../shard4/tf.get_session_handle.md                |  38 +
 .../shard4/tf.get_session_tensor.md                |  22 -
 .../functions_and_classes/shard4/tf.global_norm.md |  27 -
 .../functions_and_classes/shard4/tf.identity.md    |  14 +
 .../functions_and_classes/shard4/tf.ifft2d.md      |  15 -
 .../functions_and_classes/shard4/tf.ifft3d.md      |  15 +
 .../functions_and_classes/shard4/tf.igammac.md     |  29 +
 .../python/functions_and_classes/shard4/tf.imag.md |  27 +
 .../shard4/tf.image.adjust_saturation.md           |  25 +
 .../shard4/tf.image.convert_image_dtype.md         |  32 +
 .../shard4/tf.image.flip_left_right.md             |  23 +
 .../shard4/tf.image.flip_up_down.md                |  23 -
 .../shard4/tf.image.pad_to_bounding_box.md         |  30 +
 .../tf.image.resize_image_with_crop_or_pad.md      |  30 +
 .../shard4/tf.image.resize_nearest_neighbor.md     |  22 +
 .../shard4/tf.image.rgb_to_grayscale.md            |  19 +
 .../shard4/tf.image.rgb_to_hsv.md                  |  23 +
 .../shard4/tf.is_non_decreasing.md                 |  25 -
 .../functions_and_classes/shard4/tf.lgamma.md      |  14 -
 .../functions_and_classes/shard4/tf.linspace.md    |  28 -
 .../functions_and_classes/shard4/tf.logical_and.md |  15 +
 .../functions_and_classes/shard4/tf.logical_or.md  |  15 -
 .../functions_and_classes/shard4/tf.map_fn.md      |  42 -
 .../functions_and_classes/shard4/tf.maximum.md     |  15 -
 .../shard4/tf.merge_summary.md                     |  26 +
 .../python/functions_and_classes/shard4/tf.mul.md  |  15 -
 .../python/functions_and_classes/shard4/tf.neg.md  |  16 -
 .../shard4/tf.nn.atrous_conv2d.md                  | 107 ---
 .../functions_and_classes/shard4/tf.nn.conv3d.md   |  29 +
 .../shard4/tf.nn.depthwise_conv2d.md               |  37 -
 .../functions_and_classes/shard4/tf.nn.max_pool.md |  21 +
 .../shard4/tf.nn.max_pool_with_argmax.md           |  30 +
 .../functions_and_classes/shard4/tf.nn.moments.md  |  30 -
 .../shard4/tf.nn.sampled_softmax_loss.md           |  49 ++
 .../functions_and_classes/shard4/tf.nn.softmax.md  |  19 +
 .../shard4/tf.nn.sufficient_statistics.md          |  27 +
 .../tf.nn.weighted_cross_entropy_with_logits.md    |  52 --
 .../functions_and_classes/shard4/tf.not_equal.md   |  15 -
 .../python/functions_and_classes/shard4/tf.pad.md  |  57 ++
 .../shard4/tf.parse_example.md                     | 153 ----
 .../functions_and_classes/shard4/tf.placeholder.md |  34 +
 .../shard4/tf.python_io.tf_record_iterator.md      |  18 +
 .../functions_and_classes/shard4/tf.read_file.md   |  14 +
 .../python/functions_and_classes/shard4/tf.real.md |  28 +
 .../functions_and_classes/shard4/tf.reshape.md     |  72 --
 .../shard4/tf.scatter_update.md                    |  46 ++
 .../shard4/tf.segment_prod.md                      |  31 -
 .../functions_and_classes/shard4/tf.select.md      |  56 ++
 .../python/functions_and_classes/shard4/tf.sin.md  |  14 -
 .../functions_and_classes/shard4/tf.slice.md       |  47 --
 .../shard4/tf.sparse_concat.md                     | 100 +++
 .../functions_and_classes/shard4/tf.sparse_mask.md |  39 +
 .../shard4/tf.sparse_to_indicator.md               |  52 --
 .../shard4/tf.string_to_hash_bucket_strong.md      |  30 -
 .../python/functions_and_classes/shard4/tf.sub.md  |  15 -
 .../shard4/tf.test.compute_gradient.md             |  40 -
 .../functions_and_classes/shard4/tf.to_int32.md    |  19 +
 .../functions_and_classes/shard4/tf.trace.md       |  29 +
 .../shard4/tf.train.ClusterSpec.md                 |  86 +++
 .../shard4/tf.train.SessionManager.md              | 187 -----
 .../shard4/tf.train.Supervisor.md                  | 845 +++++++++++++++++++++
 .../shard4/tf.train.get_checkpoint_state.md        |  19 -
 .../shard4/tf.train.input_producer.md              |  38 -
 .../shard4/tf.train.start_queue_runners.md         |  24 -
 .../shard4/tf.train.string_input_producer.md       |  32 +
 .../shard4/tf.train.update_checkpoint_state.md     |  24 -
 .../functions_and_classes/shard4/tf.transpose.md   |  49 ++
 .../shard4/tf.truncated_normal_initializer.md      |  31 -
 .../shard4/tf.uniform_unit_scaling_initializer.md  |  47 ++
 .../functions_and_classes/shard4/tf.unpack.md      |  32 +
 .../functions_and_classes/shard5/tf.DType.md       | 206 +++++
 .../functions_and_classes/shard5/tf.Dimension.md   |  83 ++
 .../shard5/tf.FixedLenSequenceFeature.md           |  31 +
 .../shard5/tf.FixedLengthRecordReader.md           | 148 ++++
 .../functions_and_classes/shard5/tf.GraphKeys.md   |  36 +
 .../functions_and_classes/shard5/tf.Session.md     | 236 ++++++
 .../shard5/tf.SparseTensor.md                      | 143 ++++
 .../shard5/tf.TFRecordReader.md                    | 145 ----
 .../shard5/tf.Variable.from_proto.md               |   4 -
 .../functions_and_classes/shard5/tf.Variable.md    | 460 -----------
 .../shard5/tf.accumulate_n.md                      |  37 +
 .../shard5/tf.assert_equal.md                      |  35 +
 .../shard5/tf.assert_non_positive.md               |  34 +
 .../shard5/tf.assert_positive.md                   |  33 +
 .../shard5/tf.batch_matmul.md                      |  41 +
 .../shard5/tf.clip_by_norm.md                      |  29 +
 .../functions_and_classes/shard5/tf.concat.md      |  45 --
 ...tf.contrib.copy_graph.copy_variable_to_graph.md |  20 +
 .../shard5/tf.contrib.distributions.Chi2.md        | 260 -------
 .../shard5/tf.contrib.distributions.Gamma.md       | 284 -------
 .../shard5/tf.contrib.layers.convolution2d.md      |  43 ++
 .../shard5/tf.contrib.layers.l2_regularizer.md     |  22 +
 .../shard5/tf.contrib.layers.xavier_initializer.md |  29 -
 .../shard5/tf.contrib.learn.RunConfig.md           |  47 ++
 .../shard5/tf.contrib.learn.evaluate.md            |  44 --
 .../shard5/tf.contrib.learn.infer.md               |   4 -
 .../shard5/tf.contrib.learn.run_feeds.md           |  28 +
 .../shard5/tf.contrib.metrics.streaming_mean.md    |  44 --
 ...ntrib.metrics.streaming_mean_cosine_distance.md |  48 --
 .../functions_and_classes/shard5/tf.decode_csv.md  |  26 +
 .../functions_and_classes/shard5/tf.digamma.md     |  16 +
 .../shard5/tf.dynamic_partition.md                 |  50 ++
 .../functions_and_classes/shard5/tf.equal.md       |  15 +
 .../shard5/tf.errors.AbortedError.md               |  15 +
 .../shard5/tf.errors.PermissionDeniedError.md      |  14 +
 .../shard5/tf.errors.ResourceExhaustedError.md     |  12 +
 .../shard5/tf.errors.UnauthenticatedError.md       |  11 -
 .../functions_and_classes/shard5/tf.expand_dims.md |  50 ++
 .../python/functions_and_classes/shard5/tf.fill.md |  26 +
 .../shard5/tf.get_default_graph.md                 |  17 -
 .../functions_and_classes/shard5/tf.get_seed.md    |  22 -
 .../shard5/tf.get_session_tensor.md                |  22 +
 .../functions_and_classes/shard5/tf.global_norm.md |  27 +
 .../functions_and_classes/shard5/tf.ifft2d.md      |  15 +
 .../shard5/tf.image.adjust_brightness.md           |  25 +
 .../shard5/tf.image.encode_jpeg.md                 |  51 ++
 .../shard5/tf.image.encode_png.md                  |  28 +
 .../shard5/tf.image.pad_to_bounding_box.md         |  30 -
 .../shard5/tf.image.random_flip_up_down.md         |  24 -
 .../shard5/tf.image.resize_bilinear.md             |  24 +
 .../shard5/tf.image.rgb_to_grayscale.md            |  19 -
 .../shard5/tf.initialize_local_variables.md        |  10 -
 .../python/functions_and_classes/shard5/tf.inv.md  |  16 -
 .../functions_and_classes/shard5/tf.is_finite.md   |  14 +
 .../shard5/tf.is_variable_initialized.md           |  14 +
 .../functions_and_classes/shard5/tf.lbeta.md       |  31 +
 .../python/functions_and_classes/shard5/tf.less.md |  15 +
 .../shard5/tf.load_file_system_library.md          |  23 +
 .../functions_and_classes/shard5/tf.logical_not.md |  14 -
 .../functions_and_classes/shard5/tf.logical_or.md  |  15 +
 .../functions_and_classes/shard5/tf.logical_xor.md |   4 -
 .../shard5/tf.matrix_inverse.md                    |  27 -
 .../shard5/tf.matrix_triangular_solve.md           |  35 -
 .../python/functions_and_classes/shard5/tf.mod.md  |  15 +
 .../python/functions_and_classes/shard5/tf.neg.md  |  16 +
 .../shard5/tf.nn.compute_accidental_hits.md        |  45 ++
 .../functions_and_classes/shard5/tf.nn.conv2d.md   |  49 ++
 .../functions_and_classes/shard5/tf.nn.conv3d.md   |  29 -
 .../shard5/tf.nn.depthwise_conv2d_native.md        |  37 -
 .../functions_and_classes/shard5/tf.nn.elu.md      |  17 -
 .../functions_and_classes/shard5/tf.nn.in_top_k.md |  33 -
 .../functions_and_classes/shard5/tf.nn.top_k.md    |  31 +
 .../shard5/tf.nn.uniform_candidate_sampler.md      |  49 ++
 .../functions_and_classes/shard5/tf.one_hot.md     | 129 ++++
 .../functions_and_classes/shard5/tf.op_scope.md    |  36 -
 .../shard5/tf.parse_example.md                     | 153 ++++
 .../shard5/tf.random_shuffle.md                    |  29 +
 .../functions_and_classes/shard5/tf.reduce_join.md |  49 --
 .../functions_and_classes/shard5/tf.reduce_max.md  |  25 +
 .../functions_and_classes/shard5/tf.reduce_min.md  |  25 +
 .../tf.register_tensor_conversion_function.md      |  42 -
 .../functions_and_classes/shard5/tf.reshape.md     |  72 ++
 .../functions_and_classes/shard5/tf.round.md       |  21 +
 .../shard5/tf.saturate_cast.md                     |  19 -
 .../shard5/tf.scatter_update.md                    |  46 --
 .../functions_and_classes/shard5/tf.segment_min.md |  31 +
 .../functions_and_classes/shard5/tf.select.md      |  56 --
 .../functions_and_classes/shard5/tf.sigmoid.md     |  18 +
 .../shard5/tf.space_to_batch.md                    |  44 ++
 .../shard5/tf.sparse_retain.md                     |  33 +
 .../shard5/tf.sparse_tensor_dense_matmul.md        | 163 ++++
 .../shard5/tf.sparse_tensor_to_dense.md            |  43 --
 .../shard5/tf.sparse_to_dense.md                   |  45 ++
 .../shard5/tf.string_to_hash_bucket_fast.md        |  23 -
 .../shard5/tf.string_to_number.md                  |  20 +
 .../shard5/tf.test.compute_gradient_error.md       |  36 -
 .../shard5/tf.train.SessionManager.md              | 187 +++++
 .../shard5/tf.train.add_queue_runner.md            |  18 -
 .../shard5/tf.train.batch_join.md                  |  79 ++
 .../shard5/tf.train.import_meta_graph.md           |  65 ++
 .../shard5/tf.train.shuffle_batch_join.md          |  68 ++
 .../functions_and_classes/shard5/tf.truediv.md     |  31 -
 .../shard5/tf.truncated_normal_initializer.md      |  31 +
 .../functions_and_classes/shard5/tf.unique.md      |  33 +
 .../functions_and_classes/shard5/tf.unpack.md      |  32 -
 .../shard5/tf.variable_op_scope.md                 |  56 --
 .../shard5/tf.variable_scope.md                    |  82 ++
 .../functions_and_classes/shard5/tf.zeros_like.md  |  28 +
 .../python/functions_and_classes/shard5/tf.zeta.md |  21 -
 .../functions_and_classes/shard6/tf.Operation.md   | 225 ++++++
 .../functions_and_classes/shard6/tf.QueueBase.md   | 268 -------
 .../functions_and_classes/shard6/tf.ReaderBase.md  | 156 ++++
 .../functions_and_classes/shard6/tf.Tensor.md      | 228 ------
 .../functions_and_classes/shard6/tf.Variable.md    | 460 +++++++++++
 .../python/functions_and_classes/shard6/tf.add.md  |  17 +
 .../shard6/tf.add_to_collection.md                 |  14 -
 .../functions_and_classes/shard6/tf.as_dtype.md    |  21 -
 .../shard6/tf.assert_integer.md                    |  30 -
 .../shard6/tf.assert_non_positive.md               |  34 -
 .../functions_and_classes/shard6/tf.assert_rank.md |  36 +
 .../shard6/tf.batch_cholesky.md                    |  20 -
 .../functions_and_classes/shard6/tf.batch_fft2d.md |  18 +
 .../shard6/tf.batch_matmul.md                      |  41 -
 .../shard6/tf.batch_matrix_determinant.md          |  19 +
 .../python/functions_and_classes/shard6/tf.case.md |  75 ++
 .../shard6/tf.clip_by_value.md                     |  21 -
 .../tf.contrib.copy_graph.copy_op_to_graph.md      |  29 +
 ...tf.contrib.copy_graph.copy_variable_to_graph.md |  20 -
 ...f.contrib.distributions.DiscreteDistribution.md | 139 ----
 .../shard6/tf.contrib.distributions.StudentT.md    | 245 ------
 .../shard6/tf.contrib.ffmpeg.encode_audio.md       |  19 +
 .../shard6/tf.contrib.layers.convolution2d.md      |  43 --
 .../shard6/tf.contrib.layers.summarize_tensor.md   |  18 -
 ....contrib.layers.variance_scaling_initializer.md |  47 ++
 .../shard6/tf.contrib.learn.Estimator.md           | 215 ++++++
 .../tf.contrib.learn.NanLossDuringTrainingError.md |   1 +
 .../tf.contrib.learn.TensorFlowDNNRegressor.md     | 302 ++++++++
 .../tf.contrib.learn.TensorFlowLinearRegressor.md  | 279 +++++++
 .../tf.contrib.learn.TensorFlowRNNRegressor.md     | 312 ++++++++
 .../shard6/tf.contrib.learn.TensorFlowRegressor.md | 279 -------
 .../shard6/tf.contrib.learn.read_batch_features.md |  43 --
 .../shard6/tf.contrib.learn.run_n.md               |  19 +
 .../tf.contrib.metrics.auc_using_histogram.md      |  38 +
 .../shard6/tf.contrib.metrics.set_size.md          |  22 +
 .../shard6/tf.contrib.metrics.streaming_auc.md     |  58 --
 ...tf.contrib.metrics.streaming_percentage_less.md |  47 ++
 ...ib.metrics.streaming_root_mean_squared_error.md |  48 --
 .../shard6/tf.contrib.util.make_ndarray.md         |  20 +
 .../shard6/tf.contrib.util.make_tensor_proto.md    |  44 ++
 .../tf.contrib.util.stripped_op_list_for_graph.md  |  23 -
 .../shard6/tf.control_dependencies.md              |  20 -
 .../functions_and_classes/shard6/tf.diag_part.md   |  34 +
 .../functions_and_classes/shard6/tf.digamma.md     |  16 -
 .../shard6/tf.edit_distance.md                     |  65 --
 .../functions_and_classes/shard6/tf.equal.md       |  15 -
 .../shard6/tf.errors.DataLossError.md              |  13 +
 .../shard6/tf.errors.NotFoundError.md              |  14 +
 .../shard6/tf.errors.ResourceExhaustedError.md     |  12 -
 .../functions_and_classes/shard6/tf.expand_dims.md |  50 --
 .../python/functions_and_classes/shard6/tf.fft.md  |  14 -
 .../python/functions_and_classes/shard6/tf.fill.md |  26 -
 .../functions_and_classes/shard6/tf.foldl.md       |  44 ++
 .../functions_and_classes/shard6/tf.foldr.md       |  44 ++
 .../shard6/tf.get_collection.md                    |  25 -
 .../shard6/tf.get_variable_scope.md                |   4 +
 .../shard6/tf.greater_equal.md                     |  15 +
 .../shard6/tf.histogram_fixed_width.md             |  38 -
 .../functions_and_classes/shard6/tf.igamma.md      |  29 +
 .../shard6/tf.image.adjust_brightness.md           |  25 -
 .../shard6/tf.image.draw_bounding_boxes.md         |  32 -
 .../shard6/tf.image.grayscale_to_rgb.md            |  17 +
 .../shard6/tf.image.random_flip_left_right.md      |  24 -
 .../shard6/tf.image.random_hue.md                  |  28 -
 .../shard6/tf.image.random_saturation.md           |  27 +
 .../functions_and_classes/shard6/tf.is_finite.md   |  14 -
 .../functions_and_classes/shard6/tf.is_inf.md      |  14 +
 .../shard6/tf.is_numeric_tensor.md                 |   4 -
 .../shard6/tf.is_strictly_increasing.md            |  26 -
 .../shard6/tf.is_variable_initialized.md           |  14 -
 .../functions_and_classes/shard6/tf.lbeta.md       |  31 -
 .../functions_and_classes/shard6/tf.listdiff.md    |  40 -
 .../shard6/tf.load_op_library.md                   |  24 -
 .../shard6/tf.matrix_determinant.md                |  16 -
 .../shard6/tf.moving_average_variables.md          |  13 +
 .../shard6/tf.nn.avg_pool3d.md                     |  24 -
 .../functions_and_classes/shard6/tf.nn.conv2d.md   |  49 --
 .../shard6/tf.nn.conv2d_transpose.md               |  34 -
 .../shard6/tf.nn.embedding_lookup.md               |  50 --
 .../shard6/tf.nn.embedding_lookup_sparse.md        |  66 --
 .../functions_and_classes/shard6/tf.nn.l2_loss.md  |  19 +
 .../shard6/tf.nn.log_softmax.md                    |  19 -
 .../shard6/tf.nn.log_uniform_candidate_sampler.md  |  56 ++
 .../shard6/tf.nn.max_pool3d.md                     |  23 -
 .../shard6/tf.nn.zero_fraction.md                  |  21 -
 .../functions_and_classes/shard6/tf.ones_like.md   |  28 +
 .../shard6/tf.placeholder_with_default.md          |  17 +
 .../shard6/tf.python_io.TFRecordWriter.md          |  41 -
 .../functions_and_classes/shard6/tf.random_crop.md |  25 -
 .../shard6/tf.random_uniform.md                    |  41 +
 .../functions_and_classes/shard6/tf.range.md       |  37 +
 .../python/functions_and_classes/shard6/tf.rank.md |  28 +
 .../functions_and_classes/shard6/tf.reduce_join.md |  49 ++
 .../functions_and_classes/shard6/tf.reduce_max.md  |  25 -
 .../functions_and_classes/shard6/tf.reduce_sum.md  |  37 +
 .../functions_and_classes/shard6/tf.reverse.md     |  61 --
 .../shard6/tf.saturate_cast.md                     |  19 +
 .../functions_and_classes/shard6/tf.scatter_add.md |  46 --
 .../python/functions_and_classes/shard6/tf.size.md |  24 -
 .../functions_and_classes/shard6/tf.sparse_add.md  |  55 ++
 .../shard6/tf.sparse_reset_shape.md                |  60 ++
 .../shard6/tf.sparse_retain.md                     |  33 -
 .../shard6/tf.sparse_segment_sqrt_n.md             |  26 -
 .../shard6/tf.sparse_softmax.md                    |  51 --
 .../shard6/tf.sparse_tensor_dense_matmul.md        | 163 ----
 .../shard6/tf.sparse_tensor_to_dense.md            |  43 ++
 .../functions_and_classes/shard6/tf.split.md       |  29 -
 .../python/functions_and_classes/shard6/tf.sqrt.md |  16 +
 .../functions_and_classes/shard6/tf.square.md      |  16 -
 .../shard6/tf.train.ExponentialMovingAverage.md    | 229 ++++++
 .../shard6/tf.train.FtrlOptimizer.md               |  32 +
 .../shard6/tf.train.Optimizer.md                   | 255 +++++++
 .../shard6/tf.train.QueueRunner.from_proto.md      |   4 +
 .../shard6/tf.train.Server.md                      | 113 ---
 .../functions_and_classes/shard6/tf.train.batch.md |  68 --
 .../shard6/tf.train.replica_device_setter.md       |  50 ++
 .../functions_and_classes/shard6/tf.tuple.md       |  36 -
 .../shard6/tf.unsorted_segment_sum.md              |  38 +
 .../functions_and_classes/shard6/tf.where.md       |  46 ++
 .../functions_and_classes/shard6/tf.while_loop.md  |  60 ++
 .../functions_and_classes/shard6/tf.zeros.md       |  24 +
 .../shard6/tf.zeros_initializer.md                 |   4 -
 .../shard7/tf.AggregationMethod.md                 |  10 -
 .../shard7/tf.DeviceSpec.from_string.md            |  18 +
 .../functions_and_classes/shard7/tf.FIFOQueue.md   |  41 -
 .../shard7/tf.IdentityReader.md                    | 148 ++++
 .../functions_and_classes/shard7/tf.NoGradient.md  |  23 +
 .../shard7/tf.RegisterGradient.md                  |  36 +
 .../functions_and_classes/shard7/tf.Tensor.md      | 228 ++++++
 .../shard7/tf.add_check_numerics_ops.md            |  13 -
 .../shard7/tf.add_to_collection.md                 |  14 +
 .../functions_and_classes/shard7/tf.argmax.md      |  17 -
 .../functions_and_classes/shard7/tf.argmin.md      |  17 -
 .../shard7/tf.assert_integer.md                    |  30 +
 .../shard7/tf.assert_less_equal.md                 |  35 -
 .../shard7/tf.assert_non_negative.md               |  34 +
 .../shard7/tf.assert_proper_iterable.md            |  18 -
 .../functions_and_classes/shard7/tf.assert_type.md |  15 -
 .../shard7/tf.audio_summary.md                     |  35 -
 .../shard7/tf.batch_ifft2d.md                      |  18 -
 .../shard7/tf.batch_matrix_inverse.md              |  28 +
 .../shard7/tf.batch_to_space.md                    |  37 +
 .../python/functions_and_classes/shard7/tf.ceil.md |  14 +
 .../shard7/tf.check_numerics.md                    |  18 +
 .../shard7/tf.clip_by_average_norm.md              |  29 +
 .../shard7/tf.clip_by_global_norm.md               |  50 --
 .../shard7/tf.clip_by_value.md                     |  21 +
 .../python/functions_and_classes/shard7/tf.conj.md |  28 -
 .../tf.contrib.copy_graph.copy_op_to_graph.md      |  29 -
 .../tf.contrib.distributions.BaseDistribution.md   | 195 +++++
 ...contrib.distributions.ContinuousDistribution.md | 153 ++++
 .../shard7/tf.contrib.distributions.StudentT.md    | 245 ++++++
 ...ons.normal_congugates_known_sigma_predictive.md |  55 --
 .../shard7/tf.contrib.layers.fully_connected.md    |  46 ++
 .../shard7/tf.contrib.layers.l1_regularizer.md     |  22 -
 .../tf.contrib.layers.summarize_activation.md      |  16 -
 .../tf.contrib.layers.summarize_activations.md     |   4 -
 .../tf.contrib.learn.NanLossDuringTrainingError.md |   1 -
 .../tf.contrib.learn.TensorFlowDNNClassifier.md    | 302 --------
 .../tf.contrib.learn.TensorFlowLinearClassifier.md | 279 -------
 .../shard7/tf.contrib.learn.run_n.md               |  19 -
 .../shard7/tf.contrib.learn.train.md               |  62 --
 .../shard7/tf.contrib.metrics.confusion_matrix.md  |  45 --
 .../shard7/tf.contrib.metrics.set_intersection.md  |  23 -
 ...tf.contrib.metrics.streaming_percentage_less.md |  47 --
 .../tf.contrib.metrics.streaming_precision.md      |  50 --
 ...trib.metrics.streaming_sparse_precision_at_k.md |  60 ++
 ...contrib.metrics.streaming_sparse_recall_at_k.md |  59 ++
 .../shard7/tf.contrib.util.make_tensor_proto.md    |  44 --
 .../shard7/tf.control_dependencies.md              |  20 +
 .../shard7/tf.convert_to_tensor.md                 |  47 --
 .../functions_and_classes/shard7/tf.count_up_to.md |  23 -
 .../functions_and_classes/shard7/tf.device.md      |  19 -
 .../python/functions_and_classes/shard7/tf.diag.md |  33 +
 .../functions_and_classes/shard7/tf.diag_part.md   |  34 -
 .../shard7/tf.errors.AlreadyExistsError.md         |  14 -
 .../shard7/tf.errors.InternalError.md              |  12 -
 .../shard7/tf.errors.OutOfRangeError.md            |  15 +
 .../shard7/tf.errors.UnavailableError.md           |  11 -
 .../functions_and_classes/shard7/tf.fft3d.md       |  14 -
 .../functions_and_classes/shard7/tf.floor.md       |  14 +
 .../functions_and_classes/shard7/tf.floordiv.md    |  32 +
 .../functions_and_classes/shard7/tf.foldl.md       |  44 --
 .../functions_and_classes/shard7/tf.gather.md      |  35 +
 .../shard7/tf.get_default_session.md               |  16 -
 .../shard7/tf.get_session_handle.md                |  38 -
 .../shard7/tf.get_variable_scope.md                |   4 -
 .../shard7/tf.greater_equal.md                     |  15 -
 .../shard7/tf.histogram_fixed_width.md             |  38 +
 .../python/functions_and_classes/shard7/tf.ifft.md |  15 +
 .../python/functions_and_classes/shard7/tf.imag.md |  27 -
 .../shard7/tf.image.central_crop.md                |  30 +
 .../shard7/tf.image.crop_to_bounding_box.md        |  30 -
 .../shard7/tf.image.decode_png.md                  |  30 +
 .../shard7/tf.image.draw_bounding_boxes.md         |  32 +
 .../shard7/tf.image.per_image_whitening.md         |  29 +
 .../shard7/tf.image.random_flip_left_right.md      |  24 +
 .../tf.image.resize_image_with_crop_or_pad.md      |  30 -
 .../shard7/tf.image.transpose_image.md             |  20 +
 .../shard7/tf.initialize_all_variables.md          |  10 -
 .../shard7/tf.invert_permutation.md                |  30 +
 .../functions_and_classes/shard7/tf.is_nan.md      |  14 -
 .../shard7/tf.is_numeric_tensor.md                 |   4 +
 .../shard7/tf.load_op_library.md                   |  24 +
 .../functions_and_classes/shard7/tf.logical_and.md |  15 -
 .../functions_and_classes/shard7/tf.map_fn.md      |  42 +
 .../functions_and_classes/shard7/tf.matmul.md      |  46 --
 .../shard7/tf.matrix_determinant.md                |  16 +
 .../shard7/tf.matrix_solve_ls.md                   |  47 --
 .../functions_and_classes/shard7/tf.minimum.md     |  15 +
 .../functions_and_classes/shard7/tf.nn.avg_pool.md |  25 -
 .../shard7/tf.nn.batch_normalization.md            |  46 --
 .../shard7/tf.nn.embedding_lookup.md               |  50 ++
 .../tf.nn.learned_unigram_candidate_sampler.md     |  53 --
 .../shard7/tf.nn.log_softmax.md                    |  19 +
 .../shard7/tf.nn.max_pool3d.md                     |  23 +
 .../functions_and_classes/shard7/tf.nn.moments.md  |  30 +
 .../functions_and_classes/shard7/tf.nn.relu.md     |  14 +
 .../functions_and_classes/shard7/tf.nn.softmax.md  |  19 -
 .../functions_and_classes/shard7/tf.nn.softsign.md |  14 -
 .../shard7/tf.nn.sufficient_statistics.md          |  27 -
 .../shard7/tf.nn.zero_fraction.md                  |  21 +
 .../python/functions_and_classes/shard7/tf.ones.md |  24 +
 .../shard7/tf.python_io.tf_record_iterator.md      |  18 -
 .../functions_and_classes/shard7/tf.random_crop.md |  25 +
 .../shard7/tf.random_uniform_initializer.md        |  25 +
 .../functions_and_classes/shard7/tf.range.md       |  37 -
 .../functions_and_classes/shard7/tf.reduce_all.md  |  35 -
 .../shard7/tf.reverse_sequence.md                  |  76 --
 .../python/functions_and_classes/shard7/tf.sign.md |  18 +
 .../python/functions_and_classes/shard7/tf.sin.md  |  14 +
 .../shard7/tf.space_to_depth.md                    |  87 ---
 .../functions_and_classes/shard7/tf.sparse_mask.md |  39 -
 .../shard7/tf.string_to_hash_bucket_strong.md      |  30 +
 .../python/functions_and_classes/shard7/tf.tanh.md |  16 +
 .../functions_and_classes/shard7/tf.test.main.md   |   4 -
 .../functions_and_classes/shard7/tf.to_double.md   |  19 -
 .../functions_and_classes/shard7/tf.to_float.md    |  19 -
 .../functions_and_classes/shard7/tf.trace.md       |  29 -
 .../shard7/tf.train.FtrlOptimizer.md               |  32 -
 .../shard7/tf.train.LooperThread.md                | 215 ++++++
 .../shard7/tf.train.MomentumOptimizer.md           |  18 -
 .../shard7/tf.train.RMSPropOptimizer.md            |  23 -
 .../shard7/tf.train.Saver.from_proto.md            |   4 -
 .../shard7/tf.train.Server.md                      | 113 +++
 .../shard7/tf.train.SummaryWriter.md               | 170 -----
 .../shard7/tf.train.Supervisor.md                  | 845 ---------------------
 .../tf.train.generate_checkpoint_state_proto.md    |  20 -
 .../shard7/tf.train.input_producer.md              |  38 +
 .../shard7/tf.train.limit_epochs.md                |  21 -
 .../shard7/tf.train.shuffle_batch.md               |  74 ++
 .../shard7/tf.train.summary_iterator.md            |  42 +
 .../shard7/tf.trainable_variables.md               |  13 +
 .../functions_and_classes/shard7/tf.transpose.md   |  49 --
 .../shard7/tf.variable_axis_size_partitioner.md    |  37 -
 .../functions_and_classes/shard8/tf.Dimension.md   |  83 --
 .../shard8/tf.FixedLenSequenceFeature.md           |  31 -
 .../functions_and_classes/shard8/tf.Graph.md       | 783 -------------------
 .../functions_and_classes/shard8/tf.GraphKeys.md   |  36 -
 .../functions_and_classes/shard8/tf.OpError.md     |  62 ++
 .../functions_and_classes/shard8/tf.Print.md       |  23 +
 .../shard8/tf.SparseTensor.md                      | 143 ----
 .../shard8/tf.TextLineReader.md                    | 148 ++++
 .../shard8/tf.VarLenFeature.md                     |  11 -
 .../functions_and_classes/shard8/tf.add_n.md       |  15 -
 .../functions_and_classes/shard8/tf.argmax.md      |  17 +
 .../functions_and_classes/shard8/tf.argmin.md      |  17 +
 .../shard8/tf.assert_positive.md                   |  33 -
 .../shard8/tf.assert_proper_iterable.md            |  18 +
 .../shard8/tf.audio_summary.md                     |  35 +
 .../functions_and_classes/shard8/tf.batch_ifft.md  |  18 +
 .../shard8/tf.batch_matrix_diag.md                 |  42 -
 .../shard8/tf.batch_matrix_diag_part.md            |  46 ++
 .../shard8/tf.batch_matrix_solve.md                |  27 +
 .../shard8/tf.batch_matrix_solve_ls.md             |  56 --
 .../shard8/tf.batch_matrix_triangular_solve.md     |  39 -
 .../shard8/tf.clip_by_average_norm.md              |  29 -
 .../shard8/tf.clip_by_global_norm.md               |  50 ++
 .../shard8/tf.clip_by_norm.md                      |  29 -
 .../shard8/tf.constant_initializer.md              |  20 -
 ...contrib.distributions.ContinuousDistribution.md | 153 ----
 ...f.contrib.distributions.DirichletMultinomial.md | 185 -----
 .../shard8/tf.contrib.distributions.Exponential.md | 260 +++++++
 .../shard8/tf.contrib.distributions.Gamma.md       | 284 +++++++
 .../shard8/tf.contrib.distributions.Normal.md      | 209 -----
 ...ions.normal_conjugates_known_sigma_posterior.md |  48 ++
 .../shard8/tf.contrib.ffmpeg.decode_audio.md       |  25 +
 .../shard8/tf.contrib.learn.ModeKeys.md            |   7 +
 .../tf.contrib.learn.extract_pandas_labels.md      |   4 -
 .../shard8/tf.contrib.metrics.accuracy.md          |  23 -
 .../shard8/tf.contrib.metrics.set_intersection.md  |  23 +
 .../shard8/tf.contrib.metrics.streaming_mean.md    |  44 ++
 .../tf.contrib.metrics.streaming_precision.md      |  50 ++
 .../shard8/tf.contrib.metrics.streaming_recall.md  |  50 ++
 .../tf.convert_to_tensor_or_indexed_slices.md      |  27 +
 .../python/functions_and_classes/shard8/tf.cos.md  |  14 +
 .../functions_and_classes/shard8/tf.count_up_to.md |  23 +
 .../functions_and_classes/shard8/tf.cross.md       |  22 -
 .../functions_and_classes/shard8/tf.decode_raw.md  |  23 -
 .../python/functions_and_classes/shard8/tf.diag.md |  33 -
 .../shard8/tf.errors.AbortedError.md               |  15 -
 .../shard8/tf.errors.FailedPreconditionError.md    |  13 +
 .../shard8/tf.get_default_graph.md                 |  17 +
 .../functions_and_classes/shard8/tf.gradients.md   |  48 ++
 .../shard8/tf.histogram_summary.md                 |  25 +
 .../shard8/tf.image.adjust_contrast.md             |  29 +
 .../shard8/tf.image.adjust_hue.md                  |  26 -
 .../shard8/tf.image.central_crop.md                |  30 -
 .../shard8/tf.image.crop_to_bounding_box.md        |  30 +
 .../shard8/tf.image.decode_jpeg.md                 |  41 -
 .../shard8/tf.image.encode_png.md                  |  28 -
 .../shard8/tf.image.hsv_to_rgb.md                  |  21 +
 .../shard8/tf.image.resize_bicubic.md              |  24 -
 .../shard8/tf.image.resize_bilinear.md             |  24 -
 .../tf.image.sample_distorted_bounding_box.md      |  85 +++
 .../shard8/tf.image_summary.md                     |  48 --
 .../shard8/tf.initialize_variables.md              |  24 +
 .../python/functions_and_classes/shard8/tf.less.md |  15 -
 .../functions_and_classes/shard8/tf.less_equal.md  |  15 +
 .../shard8/tf.load_file_system_library.md          |  23 -
 .../functions_and_classes/shard8/tf.logical_not.md |  14 +
 .../functions_and_classes/shard8/tf.logical_xor.md |   4 +
 .../shard8/tf.make_template.md                     | 105 +++
 .../functions_and_classes/shard8/tf.minimum.md     |  15 -
 .../functions_and_classes/shard8/tf.multinomial.md |  28 +
 .../functions_and_classes/shard8/tf.name_scope.md  |  18 +
 .../functions_and_classes/shard8/tf.nn.avg_pool.md |  25 +
 .../functions_and_classes/shard8/tf.nn.bias_add.md |  24 +
 .../shard8/tf.nn.compute_accidental_hits.md        |  45 --
 .../shard8/tf.nn.depthwise_conv2d_native.md        |  37 +
 .../functions_and_classes/shard8/tf.nn.elu.md      |  17 +
 .../tf.nn.fixed_unigram_candidate_sampler.md       |  75 --
 .../shard8/tf.nn.local_response_normalization.md   |  34 +
 .../shard8/tf.nn.separable_conv2d.md               |  40 -
 .../tf.nn.sigmoid_cross_entropy_with_logits.md     |  48 --
 .../functions_and_classes/shard8/tf.nn.softsign.md |  14 +
 ....nn.sparse_softmax_cross_entropy_with_logits.md |  38 -
 .../shard8/tf.no_regularizer.md                    |   4 +
 .../functions_and_classes/shard8/tf.one_hot.md     | 129 ----
 .../shard8/tf.ones_initializer.md                  |   4 +
 .../functions_and_classes/shard8/tf.op_scope.md    |  36 +
 .../functions_and_classes/shard8/tf.py_func.md     |  31 -
 .../shard8/tf.random_normal.md                     |  23 +
 .../shard8/tf.random_normal_initializer.md         |  25 -
 .../functions_and_classes/shard8/tf.reduce_all.md  |  35 +
 .../functions_and_classes/shard8/tf.reduce_mean.md |  35 -
 .../tf.register_tensor_conversion_function.md      |  42 +
 .../functions_and_classes/shard8/tf.rsqrt.md       |  16 -
 .../python/functions_and_classes/shard8/tf.scan.md |  44 --
 .../functions_and_classes/shard8/tf.scatter_sub.md |  44 ++
 .../functions_and_classes/shard8/tf.segment_min.md |  31 -
 .../functions_and_classes/shard8/tf.segment_sum.md |  30 +
 .../functions_and_classes/shard8/tf.shape_n.md     |  16 -
 .../shard8/tf.sparse_fill_empty_rows.md            |  54 ++
 .../shard8/tf.sparse_placeholder.md                |  43 ++
 .../shard8/tf.sparse_reorder.md                    |  41 +
 .../shard8/tf.sparse_segment_sum.md                |  50 ++
 .../shard8/tf.sparse_split.md                      |  40 -
 .../shard8/tf.string_to_hash_bucket.md             |  21 +
 .../shard8/tf.test.get_temp_dir.md                 |  10 +
 .../shard8/tf.test.is_built_with_cuda.md           |   4 -
 .../python/functions_and_classes/shard8/tf.tile.md |  22 +
 .../functions_and_classes/shard8/tf.to_int64.md    |  19 -
 .../shard8/tf.train.AdagradOptimizer.md            |  26 -
 .../shard8/tf.train.AdamOptimizer.md               |  49 --
 .../shard8/tf.train.Coordinator.md                 | 223 ------
 .../shard8/tf.train.Server.create_local_server.md  |  19 -
 .../shard8/tf.train.add_queue_runner.md            |  18 +
 .../shard8/tf.train.exponential_decay.md           |  54 --
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 .../shard8/tf.train.import_meta_graph.md           |  65 --
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.AggregationMethod.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.AggregationMethod.md
new file mode 100644
index 0000000000..ee655fbd25
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.AggregationMethod.md
@@ -0,0 +1,10 @@
+A class listing aggregation methods used to combine gradients.
+
+Computing partial derivatives can require aggregating gradient
+contributions. This class lists the various methods that can
+be used to combine gradients in the graph:
+
+*  `ADD_N`: All of the gradient terms are summed as part of one
+   operation using the "AddN" op. It has the property that all
+   gradients must be ready before any aggregation is performed.
+*  `DEFAULT`: The system-chosen default aggregation method.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.Assert.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.Assert.md
new file mode 100644
index 0000000000..6471b9aea4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.Assert.md
@@ -0,0 +1,29 @@
+### `tf.Assert(condition, data, summarize=None, name=None)` {#Assert}
+
+Asserts that the given condition is true.
+
+If `condition` evaluates to false, print the list of tensors in `data`.
+`summarize` determines how many entries of the tensors to print.
+
+NOTE: To ensure that Assert executes, one usually attaches a dependency:
+
+```python
+ # Ensure maximum element of x is smaller or equal to 1
+assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x])
+x = tf.with_dependencies([assert_op], x)
+```
+
+##### Args:
+
+
+*  <b>`condition`</b>: The condition to evaluate.
+*  <b>`data`</b>: The tensors to print out when condition is false.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+
+*  <b>`assert_op`</b>: An `Operation` that, when executed, raises a
+  `tf.errors.InvalidArgumentError` if `condition` is not true.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.FIFOQueue.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.FIFOQueue.md
new file mode 100644
index 0000000000..129107384f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.FIFOQueue.md
@@ -0,0 +1,41 @@
+A queue implementation that dequeues elements in first-in-first out order.
+
+See [`tf.QueueBase`](#QueueBase) for a description of the methods on
+this class.
+
+- - -
+
+#### `tf.FIFOQueue.__init__(capacity, dtypes, shapes=None, names=None, shared_name=None, name='fifo_queue')` {#FIFOQueue.__init__}
+
+Creates a queue that dequeues elements in a first-in first-out order.
+
+A `FIFOQueue` has bounded capacity; supports multiple concurrent
+producers and consumers; and provides exactly-once delivery.
+
+A `FIFOQueue` holds a list of up to `capacity` elements. Each
+element is a fixed-length tuple of tensors whose dtypes are
+described by `dtypes`, and whose shapes are optionally described
+by the `shapes` argument.
+
+If the `shapes` argument is specified, each component of a queue
+element must have the respective fixed shape. If it is
+unspecified, different queue elements may have different shapes,
+but the use of `dequeue_many` is disallowed.
+
+##### Args:
+
+
+*  <b>`capacity`</b>: An integer. The upper bound on the number of elements
+    that may be stored in this queue.
+*  <b>`dtypes`</b>: A list of `DType` objects. The length of `dtypes` must equal
+    the number of tensors in each queue element.
+*  <b>`shapes`</b>: (Optional.) A list of fully-defined `TensorShape` objects
+    with the same length as `dtypes`, or `None`.
+*  <b>`names`</b>: (Optional.) A list of string naming the components in the queue
+    with the same length as `dtypes`, or `None`.  If specified the dequeue
+    methods return a dictionary with the names as keys.
+*  <b>`shared_name`</b>: (Optional.) If non-empty, this queue will be shared under
+    the given name across multiple sessions.
+*  <b>`name`</b>: Optional name for the queue operation.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.FixedLenFeature.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.FixedLenFeature.md
new file mode 100644
index 0000000000..0ae54940a8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.FixedLenFeature.md
@@ -0,0 +1,31 @@
+Configuration for parsing a fixed-length input feature.
+
+To treat sparse input as dense, provide a `default_value`; otherwise,
+the parse functions will fail on any examples missing this feature.
+
+Fields:
+  shape: Shape of input data.
+  dtype: Data type of input.
+  default_value: Value to be used if an example is missing this feature. It
+      must be compatible with `dtype`.
+- - -
+
+#### `tf.FixedLenFeature.default_value` {#FixedLenFeature.default_value}
+
+Alias for field number 2
+
+
+- - -
+
+#### `tf.FixedLenFeature.dtype` {#FixedLenFeature.dtype}
+
+Alias for field number 1
+
+
+- - -
+
+#### `tf.FixedLenFeature.shape` {#FixedLenFeature.shape}
+
+Alias for field number 0
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.FixedLengthRecordReader.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.FixedLengthRecordReader.md
deleted file mode 100644
index e8a94cb825..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.FixedLengthRecordReader.md
+++ /dev/null
@@ -1,148 +0,0 @@
-A Reader that outputs fixed-length records from a file.
-
-See ReaderBase for supported methods.
-- - -
-
-#### `tf.FixedLengthRecordReader.__init__(record_bytes, header_bytes=None, footer_bytes=None, name=None)` {#FixedLengthRecordReader.__init__}
-
-Create a FixedLengthRecordReader.
-
-##### Args:
-
-
-*  <b>`record_bytes`</b>: An int.
-*  <b>`header_bytes`</b>: An optional int. Defaults to 0.
-*  <b>`footer_bytes`</b>: An optional int. Defaults to 0.
-*  <b>`name`</b>: A name for the operation (optional).
-
-
-- - -
-
-#### `tf.FixedLengthRecordReader.num_records_produced(name=None)` {#FixedLengthRecordReader.num_records_produced}
-
-Returns the number of records this reader has produced.
-
-This is the same as the number of Read executions that have
-succeeded.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.FixedLengthRecordReader.num_work_units_completed(name=None)` {#FixedLengthRecordReader.num_work_units_completed}
-
-Returns the number of work units this reader has finished processing.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.FixedLengthRecordReader.read(queue, name=None)` {#FixedLengthRecordReader.read}
-
-Returns the next record (key, value pair) produced by a reader.
-
-Will dequeue a work unit from queue if necessary (e.g. when the
-Reader needs to start reading from a new file since it has
-finished with the previous file).
-
-##### Args:
-
-
-*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
-    to a Queue, with string work items.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of Tensors (key, value).
-
-*  <b>`key`</b>: A string scalar Tensor.
-*  <b>`value`</b>: A string scalar Tensor.
-
-
-- - -
-
-#### `tf.FixedLengthRecordReader.reader_ref` {#FixedLengthRecordReader.reader_ref}
-
-Op that implements the reader.
-
-
-- - -
-
-#### `tf.FixedLengthRecordReader.reset(name=None)` {#FixedLengthRecordReader.reset}
-
-Restore a reader to its initial clean state.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.FixedLengthRecordReader.restore_state(state, name=None)` {#FixedLengthRecordReader.restore_state}
-
-Restore a reader to a previously saved state.
-
-Not all Readers support being restored, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`state`</b>: A string Tensor.
-    Result of a SerializeState of a Reader with matching type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.FixedLengthRecordReader.serialize_state(name=None)` {#FixedLengthRecordReader.serialize_state}
-
-Produce a string tensor that encodes the state of a reader.
-
-Not all Readers support being serialized, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A string Tensor.
-
-
-- - -
-
-#### `tf.FixedLengthRecordReader.supports_serialize` {#FixedLengthRecordReader.supports_serialize}
-
-Whether the Reader implementation can serialize its state.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.NoGradient.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.NoGradient.md
deleted file mode 100644
index 15c40e6828..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.NoGradient.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.NoGradient(op_type)` {#NoGradient}
-
-Specifies that ops of type `op_type` do not have a defined gradient.
-
-This function is only used when defining a new op type. It may be
-used for ops such as `tf.size()` that are not differentiable.  For
-example:
-
-```python
-tf.NoGradient("Size")
-```
-
-##### Args:
-
-
-*  <b>`op_type`</b>: The string type of an operation. This corresponds to the
-    `OpDef.name` field for the proto that defines the operation.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `op_type` is not a string.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.RegisterGradient.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.RegisterGradient.md
deleted file mode 100644
index 736bd5b4af..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.RegisterGradient.md
+++ /dev/null
@@ -1,36 +0,0 @@
-A decorator for registering the gradient function for an op type.
-
-This decorator is only used when defining a new op type. For an op
-with `m` inputs and `n` outputs, the gradient function is a function
-that takes the original `Operation` and `n` `Tensor` objects
-(representing the gradients with respect to each output of the op),
-and returns `m` `Tensor` objects (representing the partial gradients
-with respect to each input of the op).
-
-For example, assuming that operations of type `"Sub"` take two
-inputs `x` and `y`, and return a single output `x - y`, the
-following gradient function would be registered:
-
-```python
-@tf.RegisterGradient("Sub")
-def _sub_grad(unused_op, grad):
-  return grad, tf.neg(grad)
-```
-
-The decorator argument `op_type` is the string type of an
-operation. This corresponds to the `OpDef.name` field for the proto
-that defines the operation.
-
-- - -
-
-#### `tf.RegisterGradient.__init__(op_type)` {#RegisterGradient.__init__}
-
-Creates a new decorator with `op_type` as the Operation type.
-
-##### Args:
-
-
-*  <b>`op_type`</b>: The string type of an operation. This corresponds to the
-    `OpDef.name` field for the proto that defines the operation.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.SparseTensorValue.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.SparseTensorValue.md
deleted file mode 100644
index efa3314f23..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.SparseTensorValue.md
+++ /dev/null
@@ -1,22 +0,0 @@
-SparseTensorValue(indices, values, shape)
-- - -
-
-#### `tf.SparseTensorValue.indices` {#SparseTensorValue.indices}
-
-Alias for field number 0
-
-
-- - -
-
-#### `tf.SparseTensorValue.shape` {#SparseTensorValue.shape}
-
-Alias for field number 2
-
-
-- - -
-
-#### `tf.SparseTensorValue.values` {#SparseTensorValue.values}
-
-Alias for field number 1
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.TensorShape.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.TensorShape.md
deleted file mode 100644
index 506f44d838..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.TensorShape.md
+++ /dev/null
@@ -1,316 +0,0 @@
-Represents the shape of a `Tensor`.
-
-A `TensorShape` represents a possibly-partial shape specification for a
-`Tensor`. It may be one of the following:
-
-* *Fully-known shape:* has a known number of dimensions and a known size
-  for each dimension.
-* *Partially-known shape:* has a known number of dimensions, and an unknown
-  size for one or more dimension.
-* *Unknown shape:* has an unknown number of dimensions, and an unknown
-  size in all dimensions.
-
-If a tensor is produced by an operation of type `"Foo"`, its shape
-may be inferred if there is a registered shape function for
-`"Foo"`. See [`tf.RegisterShape()`](../../api_docs/python/framework.md#RegisterShape)
-for details of shape
-functions and how to register them. Alternatively, the shape may be set
-explicitly using [`Tensor.set_shape()`](../../api_docs/python/framework.md#Tensor.set_shape).
-
-- - -
-
-#### `tf.TensorShape.merge_with(other)` {#TensorShape.merge_with}
-
-Returns a `TensorShape` combining the information in `self` and `other`.
-
-The dimensions in `self` and `other` are merged elementwise,
-according to the rules defined for `Dimension.merge_with()`.
-
-##### Args:
-
-
-*  <b>`other`</b>: Another `TensorShape`.
-
-##### Returns:
-
-  A `TensorShape` containing the combined information of `self` and
-  `other`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `self` and `other` are not compatible.
-
-
-- - -
-
-#### `tf.TensorShape.concatenate(other)` {#TensorShape.concatenate}
-
-Returns the concatenation of the dimension in `self` and `other`.
-
-*N.B.* If either `self` or `other` is completely unknown,
-concatenation will discard information about the other shape. In
-future, we might support concatenation that preserves this
-information for use with slicing.
-
-##### Args:
-
-
-*  <b>`other`</b>: Another `TensorShape`.
-
-##### Returns:
-
-  A `TensorShape` whose dimensions are the concatenation of the
-  dimensions in `self` and `other`.
-
-
-
-- - -
-
-#### `tf.TensorShape.ndims` {#TensorShape.ndims}
-
-Returns the rank of this shape, or None if it is unspecified.
-
-
-- - -
-
-#### `tf.TensorShape.dims` {#TensorShape.dims}
-
-Returns a list of Dimensions, or None if the shape is unspecified.
-
-
-- - -
-
-#### `tf.TensorShape.as_list()` {#TensorShape.as_list}
-
-Returns a list of integers or None for each dimension.
-
-##### Returns:
-
-  A list of integers or None for each dimension.
-
-
-- - -
-
-#### `tf.TensorShape.as_proto()` {#TensorShape.as_proto}
-
-Returns this shape as a `TensorShapeProto`.
-
-
-- - -
-
-#### `tf.TensorShape.is_compatible_with(other)` {#TensorShape.is_compatible_with}
-
-Returns True iff `self` is compatible with `other`.
-
-Two possibly-partially-defined shapes are compatible if there
-exists a fully-defined shape that both shapes can represent. Thus,
-compatibility allows the shape inference code to reason about
-partially-defined shapes. For example:
-
-* TensorShape(None) is compatible with all shapes.
-
-* TensorShape([None, None]) is compatible with all two-dimensional
-  shapes, such as TensorShape([32, 784]), and also TensorShape(None). It is
-  not compatible with, for example, TensorShape([None]) or
-  TensorShape([None, None, None]).
-
-* TensorShape([32, None]) is compatible with all two-dimensional shapes
-  with size 32 in the 0th dimension, and also TensorShape([None, None])
-  and TensorShape(None). It is not compatible with, for example,
-  TensorShape([32]), TensorShape([32, None, 1]) or TensorShape([64, None]).
-
-* TensorShape([32, 784]) is compatible with itself, and also
-  TensorShape([32, None]), TensorShape([None, 784]), TensorShape([None,
-  None]) and TensorShape(None). It is not compatible with, for example,
-  TensorShape([32, 1, 784]) or TensorShape([None]).
-
-The compatibility relation is reflexive and symmetric, but not
-transitive. For example, TensorShape([32, 784]) is compatible with
-TensorShape(None), and TensorShape(None) is compatible with
-TensorShape([4, 4]), but TensorShape([32, 784]) is not compatible with
-TensorShape([4, 4]).
-
-##### Args:
-
-
-*  <b>`other`</b>: Another TensorShape.
-
-##### Returns:
-
-  True iff `self` is compatible with `other`.
-
-
-- - -
-
-#### `tf.TensorShape.is_fully_defined()` {#TensorShape.is_fully_defined}
-
-Returns True iff `self` is fully defined in every dimension.
-
-
-
-- - -
-
-#### `tf.TensorShape.with_rank(rank)` {#TensorShape.with_rank}
-
-Returns a shape based on `self` with the given rank.
-
-This method promotes a completely unknown shape to one with a
-known rank.
-
-##### Args:
-
-
-*  <b>`rank`</b>: An integer.
-
-##### Returns:
-
-  A shape that is at least as specific as `self` with the given rank.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `self` does not represent a shape with the given `rank`.
-
-
-- - -
-
-#### `tf.TensorShape.with_rank_at_least(rank)` {#TensorShape.with_rank_at_least}
-
-Returns a shape based on `self` with at least the given rank.
-
-##### Args:
-
-
-*  <b>`rank`</b>: An integer.
-
-##### Returns:
-
-  A shape that is at least as specific as `self` with at least the given
-  rank.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `self` does not represent a shape with at least the given
-    `rank`.
-
-
-- - -
-
-#### `tf.TensorShape.with_rank_at_most(rank)` {#TensorShape.with_rank_at_most}
-
-Returns a shape based on `self` with at most the given rank.
-
-##### Args:
-
-
-*  <b>`rank`</b>: An integer.
-
-##### Returns:
-
-  A shape that is at least as specific as `self` with at most the given
-  rank.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `self` does not represent a shape with at most the given
-    `rank`.
-
-
-
-- - -
-
-#### `tf.TensorShape.assert_has_rank(rank)` {#TensorShape.assert_has_rank}
-
-Raises an exception if `self` is not compatible with the given `rank`.
-
-##### Args:
-
-
-*  <b>`rank`</b>: An integer.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `self` does not represent a shape with the given `rank`.
-
-
-- - -
-
-#### `tf.TensorShape.assert_same_rank(other)` {#TensorShape.assert_same_rank}
-
-Raises an exception if `self` and `other` do not have compatible ranks.
-
-##### Args:
-
-
-*  <b>`other`</b>: Another `TensorShape`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `self` and `other` do not represent shapes with the
-    same rank.
-
-
-- - -
-
-#### `tf.TensorShape.assert_is_compatible_with(other)` {#TensorShape.assert_is_compatible_with}
-
-Raises exception if `self` and `other` do not represent the same shape.
-
-This method can be used to assert that there exists a shape that both
-`self` and `other` represent.
-
-##### Args:
-
-
-*  <b>`other`</b>: Another TensorShape.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `self` and `other` do not represent the same shape.
-
-
-- - -
-
-#### `tf.TensorShape.assert_is_fully_defined()` {#TensorShape.assert_is_fully_defined}
-
-Raises an exception if `self` is not fully defined in every dimension.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `self` does not have a known value for every dimension.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.TensorShape.__init__(dims)` {#TensorShape.__init__}
-
-Creates a new TensorShape with the given dimensions.
-
-##### Args:
-
-
-*  <b>`dims`</b>: A list of Dimensions, or None if the shape is unspecified.
-*  <b>`DEPRECATED`</b>: A single integer is treated as a singleton list.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If dims cannot be converted to a list of dimensions.
-
-
-- - -
-
-#### `tf.TensorShape.num_elements()` {#TensorShape.num_elements}
-
-Returns the total number of elements, or none for incomplete shapes.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.accumulate_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.accumulate_n.md
deleted file mode 100644
index a85d0d7f87..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.accumulate_n.md
+++ /dev/null
@@ -1,37 +0,0 @@
-### `tf.accumulate_n(inputs, shape=None, tensor_dtype=None, name=None)` {#accumulate_n}
-
-Returns the element-wise sum of a list of tensors.
-
-Optionally, pass `shape` and `tensor_dtype` for shape and type checking,
-otherwise, these are inferred.
-
-For example:
-
-```python
-# tensor 'a' is [[1, 2], [3, 4]]
-# tensor `b` is [[5, 0], [0, 6]]
-tf.accumulate_n([a, b, a]) ==> [[7, 4], [6, 14]]
-
-# Explicitly pass shape and type
-tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
-  ==> [[7, 4], [6, 14]]
-```
-
-##### Args:
-
-
-*  <b>`inputs`</b>: A list of `Tensor` objects, each with same shape and type.
-*  <b>`shape`</b>: Shape of elements of `inputs`.
-*  <b>`tensor_dtype`</b>: The type of `inputs`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of same shape and type as the elements of `inputs`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `inputs` don't all have same shape and dtype or the shape
-  cannot be inferred.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_equal.md
deleted file mode 100644
index ea4fd3a1fd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_equal.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.assert_equal(x, y, data=None, summarize=None, name=None)` {#assert_equal}
-
-Assert the condition `x == y` holds element-wise.
-
-Example of adding a dependency to an operation:
-
-```python
-with tf.control_dependencies([tf.assert_equal(x, y)]):
-  output = tf.reduce_sum(x)
-```
-
-Example of adding dependency to the tensor being checked:
-
-```python
-x = tf.with_dependencies([tf.assert_equal(x, y)], x)
-```
-
-This condition holds if for every pair of (possibly broadcast) elements
-`x[i]`, `y[i]`, we have `x[i] == y[i]`.
-If both `x` and `y` are empty, this is trivially satisfied.
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`y`</b>: Numeric `Tensor`, same dtype as and broadcastable to `x`.
-*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
-    error message and first few entries of `x`, `y`.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_equal".
-
-##### Returns:
-
-  Op that raises `InvalidArgumentError` if `x == y` is False.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_less.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_less.md
deleted file mode 100644
index eb43a62444..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_less.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.assert_less(x, y, data=None, summarize=None, name=None)` {#assert_less}
-
-Assert the condition `x < y` holds element-wise.
-
-Example of adding a dependency to an operation:
-
-```python
-with tf.control_dependencies([tf.assert_less(x, y)]):
-  output = tf.reduce_sum(x)
-```
-
-Example of adding dependency to the tensor being checked:
-
-```python
-x = tf.with_dependencies([tf.assert_less(x, y)], x)
-```
-
-This condition holds if for every pair of (possibly broadcast) elements
-`x[i]`, `y[i]`, we have `x[i] < y[i]`.
-If both `x` and `y` are empty, this is trivially satisfied.
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`y`</b>: Numeric `Tensor`, same dtype as and broadcastable to `x`.
-*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
-    error message and first few entries of `x`, `y`.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_less".
-
-##### Returns:
-
-  Op that raises `InvalidArgumentError` if `x < y` is False.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_negative.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_negative.md
deleted file mode 100644
index 81daebec0d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_negative.md
+++ /dev/null
@@ -1,33 +0,0 @@
-### `tf.assert_negative(x, data=None, summarize=None, name=None)` {#assert_negative}
-
-Assert the condition `x < 0` holds element-wise.
-
-Example of adding a dependency to an operation:
-
-```python
-with tf.control_dependencies([tf.assert_negative(x)]):
-  output = tf.reduce_sum(x)
-```
-
-Example of adding dependency to the tensor being checked:
-
-```python
-x = tf.with_dependencies([tf.assert_negative(x)], x)
-```
-
-Negative means, for every element `x[i]` of `x`, we have `x[i] < 0`.
-If `x` is empty this is trivially satisfied.
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
-    error message and first few entries of `x`.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_negative".
-
-##### Returns:
-
-  Op raising `InvalidArgumentError` unless `x` is all negative.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_non_negative.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_non_negative.md
deleted file mode 100644
index 47f07a698a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_non_negative.md
+++ /dev/null
@@ -1,34 +0,0 @@
-### `tf.assert_non_negative(x, data=None, summarize=None, name=None)` {#assert_non_negative}
-
-Assert the condition `x >= 0` holds element-wise.
-
-Example of adding a dependency to an operation:
-
-```python
-with tf.control_dependencies([tf.assert_non_negative(x)]):
-  output = tf.reduce_sum(x)
-```
-
-Example of adding dependency to the tensor being checked:
-
-```python
-x = tf.with_dependencies([tf.assert_non_negative(x)], x)
-```
-
-Non-negative means, for every element `x[i]` of `x`, we have `x[i] >= 0`.
-If `x` is empty this is trivially satisfied.
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
-    error message and first few entries of `x`.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).
-    Defaults to "assert_non_negative".
-
-##### Returns:
-
-  Op raising `InvalidArgumentError` unless `x` is all non-negative.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_rank_at_least.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_rank_at_least.md
deleted file mode 100644
index 1b33f3401b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_rank_at_least.md
+++ /dev/null
@@ -1,37 +0,0 @@
-### `tf.assert_rank_at_least(x, rank, data=None, summarize=None, name=None)` {#assert_rank_at_least}
-
-Assert `x` has rank equal to `rank` or higher.
-
-Example of adding a dependency to an operation:
-
-```python
-with tf.control_dependencies([tf.assert_rank_at_least(x, 2)]):
-  output = tf.reduce_sum(x)
-```
-
-Example of adding dependency to the tensor being checked:
-
-```python
-x = tf.with_dependencies([tf.assert_rank_at_least(x, 2)], x)
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`rank`</b>: Scalar `Tensor`.
-*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
-    error message and first few entries of `x`.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).
-    Defaults to "assert_rank_at_least".
-
-##### Returns:
-
-  Op raising `InvalidArgumentError` unless `x` has specified rank or higher.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If static checks determine `x` has wrong rank.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_type.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_type.md
new file mode 100644
index 0000000000..e98b9dc4af
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.assert_type.md
@@ -0,0 +1,15 @@
+### `tf.assert_type(tensor, tf_type)` {#assert_type}
+
+Asserts that the given `Tensor` is of the specified type.
+
+##### Args:
+
+
+*  <b>`tensor`</b>: A tensorflow `Tensor`.
+*  <b>`tf_type`</b>: A tensorflow type (dtypes.float32, tf.int64, dtypes.bool, etc).
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the tensors data type doesn't match tf_type.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.batch_ifft2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.batch_ifft2d.md
new file mode 100644
index 0000000000..4476637122
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.batch_ifft2d.md
@@ -0,0 +1,18 @@
+### `tf.batch_ifft2d(input, name=None)` {#batch_ifft2d}
+
+Compute the inverse 2-dimensional discrete Fourier Transform over the inner-most
+
+2 dimensions of `input`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`.
+  A complex64 tensor of the same shape as `input`. The inner-most 2
+  dimensions of `input` are replaced with their inverse 2D Fourier Transform.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.batch_matrix_band_part.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.batch_matrix_band_part.md
new file mode 100644
index 0000000000..d9c208a460
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.batch_matrix_band_part.md
@@ -0,0 +1,60 @@
+### `tf.batch_matrix_band_part(input, num_lower, num_upper, name=None)` {#batch_matrix_band_part}
+
+Copy a tensor setting everything outside a central band in each innermost matrix
+
+to zero.
+
+The `band` part is computed as follows:
+Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a
+tensor with the same shape where
+
+`band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
+
+The indicator function 'in_band(m, n)` is one if
+`(num_lower < 0 || (m-n) <= num_lower)) &&
+(num_upper < 0 || (n-m) <= num_upper)`, and zero otherwise.
+
+For example:
+
+```prettyprint
+# if 'input' is [[ 0,  1,  2, 3]
+                 [-1,  0,  1, 2]
+                 [-2, -1,  0, 1]
+                 [-3, -2, -1, 0]],
+
+tf.batch_matrix_band_part(input, 1, -1) ==> [[ 0,  1,  2, 3]
+                                             [-1,  0,  1, 2]
+                                             [ 0, -1,  0, 1]
+                                             [ 0,  0, -1, 0]],
+
+tf.batch_matrix_band_part(input, 2, 1) ==> [[ 0,  1,  0, 0]
+                                            [-1,  0,  1, 0]
+                                            [-2, -1,  0, 1]
+                                            [ 0, -2, -1, 0]]
+```
+
+Useful special cases:
+
+```prettyprint
+ tf.batch_matrix_band_part(input, 0, -1) ==> Upper triangular part.
+ tf.batch_matrix_band_part(input, -1, 0) ==> Lower triangular part.
+ tf.batch_matrix_band_part(input, 0, 0) ==> Diagonal.
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Rank `k` tensor.
+*  <b>`num_lower`</b>: A `Tensor` of type `int64`.
+    0-D tensor. Number of subdiagonals to keep. If negative, keep entire
+    lower triangle.
+*  <b>`num_upper`</b>: A `Tensor` of type `int64`.
+    0-D tensor. Number of superdiagonals to keep. If negative, keep
+    entire upper triangle.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+  Rank `k` tensor of the same shape as input. The extracted banded tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.batch_to_space.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.batch_to_space.md
deleted file mode 100644
index d4a66ac8e0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.batch_to_space.md
+++ /dev/null
@@ -1,37 +0,0 @@
-### `tf.batch_to_space(input, crops, block_size, name=None)` {#batch_to_space}
-
-BatchToSpace for 4-D tensors of type T.
-
-Rearranges (permutes) data from batch into blocks of spatial data, followed by
-cropping. This is the reverse transformation of SpaceToBatch. More specifically,
-this op outputs a copy of the input tensor where values from the `batch`
-dimension are moved in spatial blocks to the `height` and `width` dimensions,
-followed by cropping along the `height` and `width` dimensions.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. 4-D tensor with shape
-    `[batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
-      depth]`. Note that the batch size of the input tensor must be divisible by
-    `block_size * block_size`.
-*  <b>`crops`</b>: A `Tensor` of type `int32`.
-    2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
-    how many elements to crop from the intermediate result across the spatial
-    dimensions as follows:
-
-        crops = [[crop_top, crop_bottom], [crop_left, crop_right]]
-
-*  <b>`block_size`</b>: An `int`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-  4-D with shape `[batch, height, width, depth]`, where:
-
-        height = height_pad - crop_top - crop_bottom
-        width = width_pad - crop_left - crop_right
-
-  The attr `block_size` must be greater than one. It indicates the block size.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.bytes.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.bytes.md
new file mode 100644
index 0000000000..5353507e39
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.bytes.md
@@ -0,0 +1,4 @@
+str(object='') -> string
+
+Return a nice string representation of the object.
+If the argument is a string, the return value is the same object.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.concat.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.concat.md
new file mode 100644
index 0000000000..c54a7503da
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.concat.md
@@ -0,0 +1,45 @@
+### `tf.concat(concat_dim, values, name='concat')` {#concat}
+
+Concatenates tensors along one dimension.
+
+Concatenates the list of tensors `values` along dimension `concat_dim`.  If
+`values[i].shape = [D0, D1, ... Dconcat_dim(i), ...Dn]`, the concatenated
+result has shape
+
+    [D0, D1, ... Rconcat_dim, ...Dn]
+
+where
+
+    Rconcat_dim = sum(Dconcat_dim(i))
+
+That is, the data from the input tensors is joined along the `concat_dim`
+dimension.
+
+The number of dimensions of the input tensors must match, and all dimensions
+except `concat_dim` must be equal.
+
+For example:
+
+```python
+t1 = [[1, 2, 3], [4, 5, 6]]
+t2 = [[7, 8, 9], [10, 11, 12]]
+tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
+tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
+
+# tensor t3 with shape [2, 3]
+# tensor t4 with shape [2, 3]
+tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3]
+tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]
+```
+
+##### Args:
+
+
+*  <b>`concat_dim`</b>: 0-D `int32` `Tensor`.  Dimension along which to concatenate.
+*  <b>`values`</b>: A list of `Tensor` objects or a single `Tensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` resulting from concatenation of the input tensors.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.copy_graph.get_copied_op.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.copy_graph.get_copied_op.md
new file mode 100644
index 0000000000..9e5a2118fd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.copy_graph.get_copied_op.md
@@ -0,0 +1,18 @@
+### `tf.contrib.copy_graph.get_copied_op(org_instance, graph, scope='')` {#get_copied_op}
+
+Given an `Operation` instance from some `Graph`, returns
+its namesake from `graph`, under the specified scope
+(default `""`).
+
+If a copy of `org_instance` is present in `graph` under the given
+`scope`, it will be returned.
+
+Args:
+org_instance: An `Operation` from some `Graph`.
+graph: The `Graph` to be searched for a copr of `org_instance`.
+scope: The scope `org_instance` is present in.
+
+##### Returns:
+
+    The `Operation` copy from `graph`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.BaseDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.BaseDistribution.md
deleted file mode 100644
index 65b516af08..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.BaseDistribution.md
+++ /dev/null
@@ -1,195 +0,0 @@
-Abstract base class for probability distributions.
-
-This class, along with `ContinuousDistribution` and `DiscreteDistribution`,
-defines the API for probability distributions.
-
-Users will never instantiate a `BaseDistribution`, but will instead
-instantiate subclasses of either `ContinuousDistribution` or
-`DiscreteDistribution`.
-
-Developers of new distributions should prefer to subclass
-`ContinuousDistribution` or `DiscreteDistribution`.
-
-### API
-
-The key methods for probability distributions are defined here. The likelihood
-functions (`pdf`, `log_pdf`) and (`pmf`, `log_pmf`) are defined in
-`ContinuousDistribution` and `DiscreteDistribution`, respectively.
-
-To keep ops generated by the distribution tied together by name, subclasses
-should override `name` and use it to preprend names of ops in other methods
-(see `cdf` for an example).
-
-Subclasses that wish to support `cdf` and `log_cdf` can override `log_cdf`
-and use the base class's implementation for `cdf`.
-
-### Broadcasting, batching, and shapes
-
-All distributions support batches of independent distributions of that type.
-The batch shape is determined by broadcasting together the parameters.
-
-The shape of arguments to `__init__`, `cdf`, `log_cdf`, and the likelihood
-functions defined in `ContinuousDistribution` and `DiscreteDistribution`
-reflect this broadcasting, as does the return value of `sample`.
-
-`sample_shape = (n,) + batch_shape + event_shape`, where `sample_shape` is the
-shape of the `Tensor` returned from `sample`, `n` is the number of samples,
-`batch_shape` defines how many independent distributions there are, and
-`event_shape` defines the shape of samples from each of those independent
-distributions. Samples are independent along the `batch_shape` dimensions,
-but not necessarily so along the `event_shape` dimensions (dependending on
-the particulars of the underlying distribution).
-
-Using the `Uniform` distribution as an example:
-
-```python
-minval = 3.0
-maxval = [[4.0, 6.0],
-          [10.0, 12.0]]
-
-# Broadcasting:
-# This instance represents 4 Uniform distributions. Each has a lower bound at
-# 3.0 as the `minval` parameter was broadcasted to match `maxval`'s shape.
-u = Uniform(minval, maxval)
-
-# `event_shape` is `TensorShape([])`.
-event_shape = u.get_event_shape()
-# `event_shape_t` is a `Tensor` which will evaluate to a scalar 1.
-event_shape_t = u.event_shape
-
-# Sampling returns a sample per distribution.  `samples` has shape
-# (5, 2, 2), which is (n,) + batch_shape + event_shape, where n=5,
-# batch_shape=(2, 2), and event_shape=().
-samples = u.sample(5)
-
-# The broadcasting holds across methods. Here we use `cdf` as an example. The
-# same holds for `log_cdf` and the likelihood functions.
-
-# `cum_prob` has shape (2, 2) as the `value` argument was broadcasted to the
-# shape of the `Uniform` instance.
-cum_prob_broadcast = u.cdf(4.0)
-
-# `cum_prob`'s shape is (2, 2), one per distribution. No broadcasting
-# occurred.
-cum_prob_per_dist = u.cdf([[4.0, 5.0],
-                           [6.0, 7.0]])
-
-# INVALID as the `value` argument is not broadcastable to the distribution's
-# shape.
-cum_prob_invalid = u.cdf([4.0, 5.0, 6.0])
-```
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.batch_shape(name=None)` {#BaseDistribution.batch_shape}
-
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-The product of the dimensions of the `batch_shape` is the number of
-independent distributions of this kind the instance represents.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `batch_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.cdf(value, name='cdf')` {#BaseDistribution.cdf}
-
-Cumulative distribution function.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.dtype` {#BaseDistribution.dtype}
-
-dtype of samples from this distribution.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.entropy(name=None)` {#BaseDistribution.entropy}
-
-Entropy of the distribution in nats.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.event_shape(name=None)` {#BaseDistribution.event_shape}
-
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `event_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.get_batch_shape()` {#BaseDistribution.get_batch_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `batch_shape`. May be only partially defined.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.get_event_shape()` {#BaseDistribution.get_event_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `event_shape`. May be only partially defined.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.log_cdf(value, name='log_cdf')` {#BaseDistribution.log_cdf}
-
-Log CDF.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.mean` {#BaseDistribution.mean}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.name` {#BaseDistribution.name}
-
-Name to prepend to all ops.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.sample(n, seed=None, name=None)` {#BaseDistribution.sample}
-
-Generate `n` samples.
-
-##### Args:
-
-
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
-
-##### Returns:
-
-
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2.md
new file mode 100644
index 0000000000..61ca5fb9d3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Chi2.md
@@ -0,0 +1,260 @@
+The Chi2 distribution with degrees of freedom df.
+
+The PDF of this distribution is:
+
+```pdf(x) = (x^(df/2 - 1)e^(-x/2))/(2^(k/2)Gamma(k/2)), x > 0```
+
+Note that the Chi2 distribution is a special case of the Gamma distribution,
+with Chi2(df) = Gamma(df/2, 1/2).
+- - -
+
+#### `tf.contrib.distributions.Chi2.__init__(df, name='Chi2')` {#Chi2.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.alpha` {#Chi2.alpha}
+
+Shape parameter.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.batch_shape(name='batch_shape')` {#Chi2.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.beta` {#Chi2.beta}
+
+Inverse scale parameter.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.cdf(x, name='cdf')` {#Chi2.cdf}
+
+CDF of observations `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.df` {#Chi2.df}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.dtype` {#Chi2.dtype}
+
+dtype of samples from this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.entropy(name='entropy')` {#Chi2.entropy}
+
+The entropy of Gamma distribution(s).
+
+This is defined to be
+
+```
+entropy = alpha - log(beta) + log(Gamma(alpha))
+             + (1-alpha)digamma(alpha)
+```
+
+where digamma(alpha) is the digamma function.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.event_shape(name='event_shape')` {#Chi2.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.get_batch_shape()` {#Chi2.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+  `TensorShape` object.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.get_event_shape()` {#Chi2.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+  `TensorShape` object.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.is_reparameterized` {#Chi2.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.log_cdf(x, name='log_cdf')` {#Chi2.log_cdf}
+
+Log CDF of observations `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.log_pdf(x, name='log_pdf')` {#Chi2.log_pdf}
+
+Log pdf of observations in `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.mean` {#Chi2.mean}
+
+Mean of each batch member.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.name` {#Chi2.name}
+
+Name to prepend to all ops.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.pdf(x, name='pdf')` {#Chi2.pdf}
+
+Pdf of observations in `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: tensor of dtype `dtype`, the PDFs of `x`
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.sample(n, seed=None, name=None)` {#Chi2.sample}
+
+Generate `n` samples.
+
+##### Args:
+
+
+*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
+      with values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.variance` {#Chi2.variance}
+
+Variance of each batch member.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.layers.l2_regularizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.layers.l2_regularizer.md
deleted file mode 100644
index 9c3d06393b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.layers.l2_regularizer.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.contrib.layers.l2_regularizer(scale)` {#l2_regularizer}
-
-Returns a function that can be used to apply L2 regularization to weights.
-
-Small values of L2 can help prevent overfitting the training data.
-
-##### Args:
-
-
-*  <b>`scale`</b>: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
-
-##### Returns:
-
-  A function with signature `l2(weights, name=None)` that applies L2
-  regularization.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If scale is outside of the range [0.0, 1.0] or if scale is not a
-  float.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.layers.optimize_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.layers.optimize_loss.md
new file mode 100644
index 0000000000..db0b01186a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.layers.optimize_loss.md
@@ -0,0 +1,43 @@
+### `tf.contrib.layers.optimize_loss(loss, global_step, learning_rate, optimizer, gradient_noise_scale=None, gradient_multipliers=None, clip_gradients=None, moving_average_decay=0.9, learning_rate_decay_fn=None, variables=None, name=None)` {#optimize_loss}
+
+Given loss and parameters for optimizer, returns a training op.
+
+##### Args:
+
+
+*  <b>`loss`</b>: Tensor, 0 dimensional.
+*  <b>`global_step`</b>: Tensor, step counter for each update.
+*  <b>`learning_rate`</b>: float or Tensor, magnitude of update per each training step.
+*  <b>`optimizer`</b>: string, class or optimizer instance, used as trainer.
+             string should be name of optimizer, like 'SGD',
+               'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant.
+             class should be sub-class of tf.Optimizer that implements
+               `compute_gradients` and `apply_gradients` functions.
+             optimizer instance should be instantion of tf.Optimizer sub-class
+               and have `compute_gradients` and `apply_gradients` functions.
+*  <b>`gradient_noise_scale`</b>: float or None, adds 0-mean normal noise scaled by this
+                        value.
+*  <b>`gradient_multipliers`</b>: dict of variables or variable names to floats.
+                        If present, gradients for specified
+                        variables will be multiplied by given constant.
+*  <b>`clip_gradients`</b>: float or `None`, clips gradients by this value.
+*  <b>`moving_average_decay`</b>: float or None, takes into account previous loss
+                        to make learning smoother due to outliers.
+*  <b>`learning_rate_decay_fn`</b>: function, takes `learning_rate` and `global_step`
+                          `Tensor`s, returns `Tensor`.
+                          Can be used to implement any learning rate decay
+                          functions.
+                          For example: tf.train.exponential_decay.
+*  <b>`variables`</b>: list of variables to optimize or
+             `None` to use all trainable variables.
+*  <b>`name`</b>: The name for this operation is used to scope operations and summaries.
+
+##### Returns:
+
+  Training op.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if optimizer is wrong type.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.layers.xavier_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.layers.xavier_initializer.md
new file mode 100644
index 0000000000..55631e4b05
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.layers.xavier_initializer.md
@@ -0,0 +1,29 @@
+### `tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32)` {#xavier_initializer}
+
+Returns an initializer performing "Xavier" initialization for weights.
+
+This function implements the weight initialization from:
+
+Xavier Glorot and Yoshua Bengio (2010):
+         Understanding the difficulty of training deep feedforward neural
+         networks. International conference on artificial intelligence and
+         statistics.
+
+This initializer is designed to keep the scale of the gradients roughly the
+same in all layers. In uniform distribution this ends up being the range:
+`x = sqrt(6. / (in + out)); [-x, x]` and for normal distribution a standard
+deviation of `sqrt(3. / (in + out))` is used.
+
+##### Args:
+
+
+*  <b>`uniform`</b>: Whether to use uniform or normal distributed random initialization.
+*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`dtype`</b>: The data type. Only floating point types are supported.
+
+##### Returns:
+
+  An initializer for a weight matrix.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.BaseEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.BaseEstimator.md
deleted file mode 100644
index 034af231a1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.BaseEstimator.md
+++ /dev/null
@@ -1,189 +0,0 @@
-Abstract BaseEstimator class to train and evaluate TensorFlow models.
-
-Concrete implementation of this class should provide following functions:
-  * _get_train_ops
-  * _get_eval_ops
-  * _get_predict_ops
-It may override _get_default_metric_functions.
-
-`Estimator` implemented below is a good example of how to use this class.
-
-Parameters:
-  model_dir: Directory to save model parameters, graph and etc.
-- - -
-
-#### `tf.contrib.learn.BaseEstimator.__init__(model_dir=None, config=None)` {#BaseEstimator.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.BaseEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=32, steps=None, metrics=None, name=None)` {#BaseEstimator.evaluate}
-
-Evaluates given model with provided evaluation data.
-
-##### Args:
-
-
-*  <b>`x`</b>: features.
-*  <b>`y`</b>: targets.
-*  <b>`input_fn`</b>: Input function. If set, x and y must be None.
-*  <b>`feed_fn`</b>: Function creating a feed dict every time it is called. Called
-    once per iteration.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32. Ignored
-    if input_fn is set.
-*  <b>`steps`</b>: Number of steps to evalute for.
-*  <b>`metrics`</b>: Dict of metric ops to run. If None, the default metric functions
-    are used; if {}, no metrics are used.
-*  <b>`name`</b>: Name of the evaluation if user needs to run multiple evaluation on
-    different data sets, such as evaluate on training data vs test data.
-
-##### Returns:
-
-  Returns self.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If x or y are not None while input_fn or feed_fn is not None.
-
-
-- - -
-
-#### `tf.contrib.learn.BaseEstimator.fit(x, y, steps, batch_size=32, monitors=None)` {#BaseEstimator.fit}
-
-Trains a model given training data X and y.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-     iterator that returns arrays of features. The training input
-     samples for fitting the model.
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-     iterator that returns array of targets. The training target values
-     (class labels in classification, real numbers in regression).
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.BaseEstimator.get_params(deep=True)` {#BaseEstimator.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.BaseEstimator.model_dir` {#BaseEstimator.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.BaseEstimator.partial_fit(x, y, steps=1, batch_size=32, monitors=None)` {#BaseEstimator.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-    iterator that returns arrays of features. The training input
-    samples for fitting the model.
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-    iterator that returns array of targets. The training target values
-    (class label in classification, real numbers in regression).
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.BaseEstimator.predict(x=None, input_fn=None, batch_size=None)` {#BaseEstimator.predict}
-
-Returns predictions for given features.
-
-##### Args:
-
-
-*  <b>`x`</b>: features.
-*  <b>`input_fn`</b>: Input function. If set, x must be None.
-*  <b>`batch_size`</b>: Override default batch size.
-
-##### Returns:
-
-  Numpy array of predicted classes or regression values.
-
-
-- - -
-
-#### `tf.contrib.learn.BaseEstimator.set_params(**params)` {#BaseEstimator.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.BaseEstimator.train(input_fn, steps, monitors=None)` {#BaseEstimator.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.TensorFlowClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.TensorFlowClassifier.md
deleted file mode 100644
index 63588166ec..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.TensorFlowClassifier.md
+++ /dev/null
@@ -1,279 +0,0 @@
-TensorFlow Linear Classifier model.
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.__init__(n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowClassifier.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.bias_` {#TensorFlowClassifier.bias_}
-
-Returns weights of the linear classifier.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowClassifier.evaluate}
-
-See base class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowClassifier.fit}
-
-Builds a neural network model given provided `model_fn` and training
-data X and y.
-
-Note: called first time constructs the graph and initializers
-variables. Consecutives times it will continue training the same model.
-This logic follows partial_fit() interface in scikit-learn.
-
-To restart learning, create new estimator.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class labels in classification, real numbers in regression).
-
-*  <b>`steps`</b>: int, number of steps to train.
-         If None or 0, train for `self.steps`.
-*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
-    invoke early stopping.
-*  <b>`logdir`</b>: the directory to save the log file that can be used for
-  optional visualization.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.get_params(deep=True)` {#TensorFlowClassifier.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.get_tensor(name)` {#TensorFlowClassifier.get_tensor}
-
-Returns tensor by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.get_tensor_value(name)` {#TensorFlowClassifier.get_tensor_value}
-
-Returns value of the tensor give by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.get_variable_names()` {#TensorFlowClassifier.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.model_dir` {#TensorFlowClassifier.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.partial_fit(x, y)` {#TensorFlowClassifier.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class label in classification, real numbers in regression).
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.predict(x, axis=1, batch_size=None)` {#TensorFlowClassifier.predict}
-
-Predict class or regression for X.
-
-For a classification model, the predicted class for each sample in X is
-returned. For a regression model, the predicted value based on X is
-returned.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`axis`</b>: Which axis to argmax for classification.
-    By default axis 1 (next after batch) is used.
-    Use 2 for sequence predictions.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member
-    variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
-  value.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.predict_proba(x, batch_size=None)` {#TensorFlowClassifier.predict_proba}
-
-Predict class probability of the input samples X.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
-  probabilities for each class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.restore(cls, path, config=None)` {#TensorFlowClassifier.restore}
-
-Restores model from give path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Path to the checkpoints and other model information.
-*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
-      reconfigured.
-
-##### Returns:
-
-  Estimator, object of the subclass of TensorFlowEstimator.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.save(path)` {#TensorFlowClassifier.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.set_params(**params)` {#TensorFlowClassifier.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.train(input_fn, steps, monitors=None)` {#TensorFlowClassifier.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.weights_` {#TensorFlowClassifier.weights_}
-
-Returns weights of the linear classifier.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.TensorFlowRNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.TensorFlowRNNClassifier.md
new file mode 100644
index 0000000000..130b2706de
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.TensorFlowRNNClassifier.md
@@ -0,0 +1,312 @@
+TensorFlow RNN Classifier model.
+
+Parameters:
+  rnn_size: The size for rnn cell, e.g. size of your word embeddings.
+  cell_type: The type of rnn cell, including rnn, gru, and lstm.
+  num_layers: The number of layers of the rnn model.
+  input_op_fn: Function that will transform the input tensor, such as
+    creating word embeddings, byte list, etc. This takes
+    an argument X for input and returns transformed X.
+  bidirectional: boolean, Whether this is a bidirectional rnn.
+  sequence_length: If sequence_length is provided, dynamic calculation is
+    performed. This saves computational time when unrolling past max sequence
+    length.
+  initial_state: An initial state for the RNN. This must be a tensor of
+    appropriate type and shape [batch_size x cell.state_size].
+  n_classes: Number of classes in the target.
+  batch_size: Mini batch size.
+  steps: Number of steps to run over data.
+  optimizer: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
+  learning_rate: If this is constant float value, no decay function is
+    used. Instead, a customized decay function can be passed that accepts
+    global_step as parameter and returns a Tensor.
+    e.g. exponential decay function:
+    def exp_decay(global_step):
+        return tf.train.exponential_decay(
+            learning_rate=0.1, global_step,
+            decay_steps=2, decay_rate=0.001)
+  class_weight: None or list of n_classes floats. Weight associated with
+    classes for loss computation. If not given, all classes are
+    supposed to have weight one.
+  continue_training: when continue_training is True, once initialized
+    model will be continuely trained on every call of fit.
+  config: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc.
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.__init__(rnn_size, n_classes, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None, batch_size=32, steps=50, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowRNNClassifier.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.bias_` {#TensorFlowRNNClassifier.bias_}
+
+Returns bias of the rnn layer.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowRNNClassifier.evaluate}
+
+See base class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowRNNClassifier.fit}
+
+Builds a neural network model given provided `model_fn` and training
+data X and y.
+
+Note: called first time constructs the graph and initializers
+variables. Consecutives times it will continue training the same model.
+This logic follows partial_fit() interface in scikit-learn.
+
+To restart learning, create new estimator.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class labels in classification, real numbers in regression).
+
+*  <b>`steps`</b>: int, number of steps to train.
+         If None or 0, train for `self.steps`.
+*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
+    invoke early stopping.
+*  <b>`logdir`</b>: the directory to save the log file that can be used for
+  optional visualization.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.get_params(deep=True)` {#TensorFlowRNNClassifier.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.get_tensor(name)` {#TensorFlowRNNClassifier.get_tensor}
+
+Returns tensor by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.get_tensor_value(name)` {#TensorFlowRNNClassifier.get_tensor_value}
+
+Returns value of the tensor give by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Numpy array - value of the tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.get_variable_names()` {#TensorFlowRNNClassifier.get_variable_names}
+
+Returns list of all variable names in this model.
+
+##### Returns:
+
+  List of names.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.model_dir` {#TensorFlowRNNClassifier.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.partial_fit(x, y)` {#TensorFlowRNNClassifier.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class label in classification, real numbers in regression).
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.predict(x, axis=1, batch_size=None)` {#TensorFlowRNNClassifier.predict}
+
+Predict class or regression for X.
+
+For a classification model, the predicted class for each sample in X is
+returned. For a regression model, the predicted value based on X is
+returned.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`axis`</b>: Which axis to argmax for classification.
+    By default axis 1 (next after batch) is used.
+    Use 2 for sequence predictions.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member
+    variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
+  value.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.predict_proba(x, batch_size=None)` {#TensorFlowRNNClassifier.predict_proba}
+
+Predict class probability of the input samples X.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
+  probabilities for each class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.restore(cls, path, config=None)` {#TensorFlowRNNClassifier.restore}
+
+Restores model from give path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Path to the checkpoints and other model information.
+*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
+      reconfigured.
+
+##### Returns:
+
+  Estimator, object of the subclass of TensorFlowEstimator.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.save(path)` {#TensorFlowRNNClassifier.save}
+
+Saves checkpoints and graph to given path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Folder to save model to.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.set_params(**params)` {#TensorFlowRNNClassifier.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.train(input_fn, steps, monitors=None)` {#TensorFlowRNNClassifier.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.weights_` {#TensorFlowRNNClassifier.weights_}
+
+Returns weights of the rnn layer.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.evaluate.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.evaluate.md
new file mode 100644
index 0000000000..022662c3f6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.evaluate.md
@@ -0,0 +1,44 @@
+### `tf.contrib.learn.evaluate(graph, output_dir, checkpoint_path, eval_dict, update_op=None, global_step_tensor=None, supervisor_master='', log_every_steps=10, feed_fn=None, max_steps=None)` {#evaluate}
+
+Evaluate a model loaded from a checkpoint.
+
+Given `graph`, a directory to write summaries to (`output_dir`), a checkpoint
+to restore variables from, and a `dict` of `Tensor`s to evaluate, run an eval
+loop for `max_steps` steps.
+
+In each step of evaluation, all tensors in the `eval_dict` are evaluated, and
+every `log_every_steps` steps, they are logged. At the very end of evaluation,
+a summary is evaluated (finding the summary ops using `Supervisor`'s logic)
+and written to `output_dir`.
+
+##### Args:
+
+
+*  <b>`graph`</b>: A `Graph` to train. It is expected that this graph is not in use
+    elsewhere.
+*  <b>`output_dir`</b>: A string containing the directory to write a summary to.
+*  <b>`checkpoint_path`</b>: A string containing the path to a checkpoint to restore.
+    Can be `None` if the graph doesn't require loading any variables.
+*  <b>`eval_dict`</b>: A `dict` mapping string names to tensors to evaluate. It is
+    evaluated in every logging step. The result of the final evaluation is
+    returned. If update_op is None, then it's evaluated in every step.
+*  <b>`update_op`</b>: A `Tensor` which is run in every step.
+*  <b>`global_step_tensor`</b>: A `Variable` containing the global step. If `None`,
+    one is extracted from the graph using the same logic as in `Supervisor`.
+    Used to place eval summaries on training curves.
+*  <b>`supervisor_master`</b>: The master string to use when preparing the session.
+*  <b>`log_every_steps`</b>: Integer. Output logs every `log_every_steps` evaluation
+    steps. The logs contain the `eval_dict` and timing information.
+*  <b>`feed_fn`</b>: A function that is called every iteration to produce a `feed_dict`
+    passed to `session.run` calls. Optional.
+*  <b>`max_steps`</b>: Integer. Evaluate `eval_dict` this many times.
+
+##### Returns:
+
+  A tuple `(eval_results, global_step)`:
+
+*  <b>`eval_results`</b>: A `dict` mapping `string` to numeric values (`int`, `float`)
+    that are the result of running eval_dict in the last step. `None` if no
+    eval steps were run.
+*  <b>`global_step`</b>: The global step this evaluation corresponds to.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.extract_dask_data.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.extract_dask_data.md
new file mode 100644
index 0000000000..a14a51ff56
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.extract_dask_data.md
@@ -0,0 +1,4 @@
+### `tf.contrib.learn.extract_dask_data(data)` {#extract_dask_data}
+
+Extract data from dask.Series or dask.DataFrame for predictors
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.extract_pandas_data.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.extract_pandas_data.md
new file mode 100644
index 0000000000..82703a8097
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.extract_pandas_data.md
@@ -0,0 +1,4 @@
+### `tf.contrib.learn.extract_pandas_data(data)` {#extract_pandas_data}
+
+Extract data from pandas.DataFrame for predictors
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.read_batch_examples.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.read_batch_examples.md
deleted file mode 100644
index c5cec0542a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.read_batch_examples.md
+++ /dev/null
@@ -1,39 +0,0 @@
-### `tf.contrib.learn.read_batch_examples(file_pattern, batch_size, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, num_threads=1, name=None)` {#read_batch_examples}
-
-Adds operations to read, queue, batch `Example` protos.
-
-Given file pattern (or list of files), will setup a queue for file names,
-read `Example` proto using provided `reader`, use batch queue to create
-batches of examples of size `batch_size`.
-
-All queue runners are added to the queue runners collection, and may be
-started via `start_queue_runners`.
-
-All ops are added to the default graph.
-
-##### Args:
-
-
-*  <b>`file_pattern`</b>: List of files or pattern of file paths containing
-      `Example` records. See `tf.gfile.Glob` for pattern rules.
-*  <b>`batch_size`</b>: An int or scalar `Tensor` specifying the batch size to use.
-*  <b>`reader`</b>: A function or class that returns an object with
-    `read` method, (filename tensor) -> (example tensor).
-*  <b>`randomize_input`</b>: Whether the input should be randomized.
-*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
-    dataset. If `None`, cycles through the dataset forever.
-    NOTE - If specified, creates a variable that must be initialized, so call
-    `tf.initialize_all_variables()` as shown in the tests.
-*  <b>`queue_capacity`</b>: Capacity for input queue.
-*  <b>`num_threads`</b>: The number of threads enqueuing examples.
-*  <b>`name`</b>: Name of resulting op.
-
-##### Returns:
-
-  String `Tensor` of batched `Example` proto.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: for invalid inputs.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.run_feeds.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.run_feeds.md
deleted file mode 100644
index f5c3e977d0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.run_feeds.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.contrib.learn.run_feeds(output_dict, feed_dicts, restore_checkpoint_path=None)` {#run_feeds}
-
-Run `output_dict` tensors with each input in `feed_dicts`.
-
-If `checkpoint_path` is supplied, restore from checkpoint. Otherwise, init all
-variables.
-
-##### Args:
-
-
-*  <b>`output_dict`</b>: A `dict` mapping string names to `Tensor` objects to run.
-    Tensors must all be from the same graph.
-*  <b>`feed_dicts`</b>: Iterable of `dict` objects of input values to feed.
-*  <b>`restore_checkpoint_path`</b>: A string containing the path to a checkpoint to
-    restore.
-
-##### Returns:
-
-  A list of dicts of values read from `output_dict` tensors, one item in the
-  list for each item in `feed_dicts`. Keys are the same as `output_dict`,
-  values are the results read from the corresponding `Tensor` in
-  `output_dict`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `output_dict` or `feed_dicts` is None or empty.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.train.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.train.md
new file mode 100644
index 0000000000..65057636ce
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.train.md
@@ -0,0 +1,62 @@
+### `tf.contrib.learn.train(graph, output_dir, train_op, loss_op, global_step_tensor=None, init_op=None, init_feed_dict=None, init_fn=None, log_every_steps=10, supervisor_is_chief=True, supervisor_master='', supervisor_save_model_secs=600, supervisor_save_summaries_steps=100, feed_fn=None, max_steps=None, fail_on_nan_loss=True, monitors=None)` {#train}
+
+Train a model.
+
+Given `graph`, a directory to write outputs to (`output_dir`), and some ops,
+run a training loop. The given `train_op` performs one step of training on the
+model. The `loss_op` represents the objective function of the training. It is
+expected to increment the `global_step_tensor`, a scalar integer tensor
+counting training steps. This function uses `Supervisor` to initialize the
+graph (from a checkpoint if one is available in `output_dir`), write summaries
+defined in the graph, and write regular checkpoints as defined by
+`supervisor_save_model_secs`.
+
+Training continues until `global_step_tensor` evaluates to `max_steps`, or, if
+`fail_on_nan_loss`, until `loss_op` evaluates to `NaN`. In that case the
+program is terminated with exit code 1.
+
+##### Args:
+
+
+*  <b>`graph`</b>: A graph to train. It is expected that this graph is not in use
+    elsewhere.
+*  <b>`output_dir`</b>: A directory to write outputs to.
+*  <b>`train_op`</b>: An op that performs one training step when run.
+*  <b>`loss_op`</b>: A scalar loss tensor.
+*  <b>`global_step_tensor`</b>: A tensor representing the global step. If none is given,
+    one is extracted from the graph using the same logic as in `Supervisor`.
+*  <b>`init_op`</b>: An op that initializes the graph. If `None`, use `Supervisor`'s
+    default.
+*  <b>`init_feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
+    This feed dictionary will be used when `init_op` is evaluated.
+*  <b>`init_fn`</b>: Optional callable passed to Supervisor to initialize the model.
+*  <b>`log_every_steps`</b>: Output logs regularly. The logs contain timing data and the
+    current loss.
+*  <b>`supervisor_is_chief`</b>: Whether the current process is the chief supervisor in
+    charge of restoring the model and running standard services.
+*  <b>`supervisor_master`</b>: The master string to use when preparing the session.
+*  <b>`supervisor_save_model_secs`</b>: Save a checkpoint every
+    `supervisor_save_model_secs` seconds when training.
+*  <b>`supervisor_save_summaries_steps`</b>: Save summaries every
+    `supervisor_save_summaries_steps` seconds when training.
+*  <b>`feed_fn`</b>: A function that is called every iteration to produce a `feed_dict`
+    passed to `session.run` calls. Optional.
+*  <b>`max_steps`</b>: Train until `global_step_tensor` evaluates to this value.
+*  <b>`fail_on_nan_loss`</b>: If true, raise `NanLossDuringTrainingError` if `loss_op`
+    evaluates to `NaN`. If false, continue training as if nothing happened.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+    inside the training loop.
+
+##### Returns:
+
+  The final loss value.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `global_step_tensor` is not provided. See
+      `tf.contrib.framework.get_global_step` for how we look it up if not
+      provided explicitly.
+*  <b>`NanLossDuringTrainingError`</b>: If `fail_on_nan_loss` is `True`, and loss ever
+      evaluates to `NaN`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_accuracy.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_accuracy.md
new file mode 100644
index 0000000000..684d1849d3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_accuracy.md
@@ -0,0 +1,51 @@
+### `tf.contrib.metrics.streaming_accuracy(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_accuracy}
+
+Calculates how often `predictions` matches `labels`.
+
+The `streaming_accuracy` function creates two local variables, `total` and
+`count` that are used to compute the frequency with which `predictions`
+matches `labels`. This frequency is ultimately returned as `accuracy`: an
+idempotent operation that simply divides `total` by `count`.
+To facilitate the estimation of the accuracy over a stream of data, the
+function utilizes two operations. First, an `is_correct` operation that
+computes a tensor whose shape matches `predictions` and whose elements are
+set to 1.0 when the corresponding values of `predictions` and `labels match
+and 0.0 otherwise. Second, an `update_op` operation whose behavior is
+dependent on the value of `weights`. If `weights` is None, then `update_op`
+increments `total` with the number of elements of `predictions` that match
+`labels` and increments `count` with the number of elements in `values`. If
+`weights` is not `None`, then `update_op` increments `total` with the reduced
+sum of the product of `weights` and `is_correct` and increments `count` with
+the reduced sum of `weights`. In addition to performing the updates,
+`update_op` also returns the `accuracy` value.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: The predicted values, a `Tensor` of any shape.
+*  <b>`labels`</b>: The ground truth values, a `Tensor` whose shape matches
+    `predictions`.
+*  <b>`weights`</b>: An optional set of weights whose shape matches `predictions`
+    which, when not `None`, produces a weighted mean accuracy.
+*  <b>`metrics_collections`</b>: An optional list of collections that `accuracy` should
+    be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
+    be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`accuracy`</b>: A tensor representing the accuracy, the value of `total` divided
+    by `count`.
+*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
+    appropriately and whose value matches `accuracy`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the dimensions of `predictions` and `labels` don't match or
+    if `weight` is not `None` and its shape doesn't match `predictions` or
+    if either `metrics_collections` or `updates_collections` are not
+    a list or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_mean_absolute_error.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_mean_absolute_error.md
new file mode 100644
index 0000000000..b4ecd6e916
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_mean_absolute_error.md
@@ -0,0 +1,48 @@
+### `tf.contrib.metrics.streaming_mean_absolute_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean_absolute_error}
+
+Computes the mean absolute error between the labels and predictions.
+
+The `streaming_mean_absolute_error` function creates two local variables,
+`total` and `count` that are used to compute the mean absolute error. This
+average is ultimately returned as `mean_absolute_error`: an idempotent
+operation that simply divides `total` by `count`. To facilitate the estimation
+of the mean absolute error over a stream of data, the function utilizes two
+operations. First, an `absolute_errors` operation computes the absolute value
+of the differences between `predictions` and `labels`. Second, an `update_op`
+operation whose behavior is dependent on the value of `weights`. If `weights`
+is None, then `update_op` increments `total` with the reduced sum of
+`absolute_errors` and increments `count` with the number of elements in
+`absolute_errors`. If `weights` is not `None`, then `update_op` increments
+`total` with the reduced sum of the product of `weights` and `absolute_errors`
+and increments `count` with the reduced sum of `weights`. In addition to
+performing the updates, `update_op` also returns the `mean_absolute_error`
+value.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: A `Tensor` of arbitrary shape.
+*  <b>`labels`</b>: A `Tensor` of the same shape as `predictions`.
+*  <b>`weights`</b>: An optional set of weights of the same shape as `predictions`. If
+    `weights` is not None, the function computes a weighted mean.
+*  <b>`metrics_collections`</b>: An optional list of collections that
+    `mean_absolute_error` should be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
+    be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`mean_absolute_error`</b>: A tensor representing the current mean, the value of
+    `total` divided by `count`.
+*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
+    appropriately and whose value matches `mean_absolute_error`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `weights` is not `None` and its shape doesn't match
+    `predictions` or if either `metrics_collections` or `updates_collections`
+    are not a list or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_sparse_precision_at_k.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_sparse_precision_at_k.md
deleted file mode 100644
index ad24dd742a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_sparse_precision_at_k.md
+++ /dev/null
@@ -1,60 +0,0 @@
-### `tf.contrib.metrics.streaming_sparse_precision_at_k(predictions, labels, k, class_id=None, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_sparse_precision_at_k}
-
-Computes precision@k of the predictions with respect to sparse labels.
-
-If `class_id` is specified, we calculate precision by considering only the
-    entries in the batch for which `class_id` is in the top-k highest
-    `predictions`, and computing the fraction of them for which `class_id` is
-    indeed a correct label.
-If `class_id` is not specified, we'll calculate precision as how often on
-    average a class among the top-k classes with the highest predicted values
-    of a batch entry is correct and can be found in the label for that entry.
-
-`streaming_sparse_precision_at_k` creates two local variables,
-`true_positive_at_<k>` and `false_positive_at_<k>`, that are used to compute
-the precision@k frequency. This frequency is ultimately returned as
-`recall_at_<k>`: an idempotent operation that simply divides
-`true_positive_at_<k>` by total (`true_positive_at_<k>` + `recall_at_<k>`). To
-facilitate the estimation of precision@k over a stream of data, the function
-utilizes three steps.
-* A `top_k` operation computes a tensor whose elements indicate the top `k`
-  predictions of the `predictions` `Tensor`.
-* Set operations are applied to `top_k` and `labels` to calculate true
-  positives and false positives.
-* An `update_op` operation increments `true_positive_at_<k>` and
-  `false_positive_at_<k>`. It also returns the recall value.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: Float `Tensor` with shape [D1, ... DN, num_classes] where
-    N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes].
-    The final dimension contains the logit values for each class. [D1, ... DN]
-    must match `labels`.
-*  <b>`labels`</b>: `int64` `Tensor` or `SparseTensor` with shape
-    [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
-    target classes for the associated prediction. Commonly, N=1 and `labels`
-    has shape [batch_size, num_labels]. [D1, ... DN] must match
-    `predictions_idx`. Values should be in range [0, num_classes], where
-    num_classes is the last dimension of `predictions`.
-*  <b>`k`</b>: Integer, k for @k metric.
-*  <b>`class_id`</b>: Integer class ID for which we want binary metrics. This should be
-    in range [0, num_classes], where num_classes is the last dimension of
-    `predictions`.
-*  <b>`ignore_mask`</b>: An optional, binary tensor whose shape is broadcastable to the
-    the first [D1, ... DN] dimensions of `predictions_idx` and `labels`.
-*  <b>`metrics_collections`</b>: An optional list of collections that values should
-    be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that updates should
-    be added to.
-*  <b>`name`</b>: Name of new update operation, and namespace for other dependant ops.
-
-##### Returns:
-
-
-*  <b>`precision`</b>: Scalar `float64` `Tensor` with the value of `true_positives`
-    divided by the sum of `true_positives` and `false_positives`.
-*  <b>`update_op`</b>: `Operation` that increments `true_positives` and
-    `false_positives` variables appropriately, and whose value matches
-    `precision`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_sparse_recall_at_k.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_sparse_recall_at_k.md
deleted file mode 100644
index d09b288089..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.metrics.streaming_sparse_recall_at_k.md
+++ /dev/null
@@ -1,59 +0,0 @@
-### `tf.contrib.metrics.streaming_sparse_recall_at_k(predictions, labels, k, class_id=None, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_sparse_recall_at_k}
-
-Computes recall@k of the predictions with respect to sparse labels.
-
-If `class_id` is specified, we calculate recall by considering only the
-    entries in the batch for which `class_id` is in the label, and computing
-    the fraction of them for which `class_id` is in the top-k `predictions`.
-If `class_id` is not specified, we'll calculate recall as how often on
-    average a class among the labels of a batch entry is in the top-k
-    `predictions`.
-
-`streaming_sparse_recall_at_k` creates two local variables,
-`true_positive_at_<k>` and `false_negative_at_<k>`, that are used to compute
-the recall_at_k frequency. This frequency is ultimately returned as
-`recall_at_<k>`: an idempotent operation that simply divides
-`true_positive_at_<k>` by total (`true_positive_at_<k>` + `recall_at_<k>`). To
-facilitate the estimation of recall@k over a stream of data, the function
-utilizes three steps.
-* A `top_k` operation computes a tensor whose elements indicate the top `k`
-  predictions of the `predictions` `Tensor`.
-* Set operations are applied to `top_k` and `labels` to calculate true
-  positives and false negatives.
-* An `update_op` operation increments `true_positive_at_<k>` and
-  `false_negative_at_<k>`. It also returns the recall value.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: Float `Tensor` with shape [D1, ... DN, num_classes] where
-    N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes].
-    The final dimension contains the logit values for each class. [D1, ... DN]
-    must match `labels`.
-*  <b>`labels`</b>: `int64` `Tensor` or `SparseTensor` with shape
-    [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
-    target classes for the associated prediction. Commonly, N=1 and `labels`
-    has shape [batch_size, num_labels]. [D1, ... DN] must match `labels`.
-    Values should be in range [0, num_classes], where num_classes is the last
-    dimension of `predictions`.
-*  <b>`k`</b>: Integer, k for @k metric.
-*  <b>`class_id`</b>: Integer class ID for which we want binary metrics. This should be
-    in range [0, num_classes], where num_classes is the last dimension of
-    `predictions`.
-*  <b>`ignore_mask`</b>: An optional, binary tensor whose shape is broadcastable to the
-    the first [D1, ... DN] dimensions of `predictions_idx` and `labels`.
-*  <b>`metrics_collections`</b>: An optional list of collections that values should
-    be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that updates should
-    be added to.
-*  <b>`name`</b>: Name of new update operation, and namespace for other dependant ops.
-
-##### Returns:
-
-
-*  <b>`recall`</b>: Scalar `float64` `Tensor` with the value of `true_positives` divided
-    by the sum of `true_positives` and `false_negatives`.
-*  <b>`update_op`</b>: `Operation` that increments `true_positives` and
-    `false_negatives` variables appropriately, and whose value matches
-    `recall`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.decode_json_example.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.decode_json_example.md
deleted file mode 100644
index bf5184c40a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.decode_json_example.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.decode_json_example(json_examples, name=None)` {#decode_json_example}
-
-Convert JSON-encoded Example records to binary protocol buffer strings.
-
-This op translates a tensor containing Example records, encoded using
-the [standard JSON
-mapping](https://developers.google.com/protocol-buffers/docs/proto3#json),
-into a tensor containing the same records encoded as binary protocol
-buffers. The resulting tensor can then be fed to any of the other
-Example-parsing ops.
-
-##### Args:
-
-
-*  <b>`json_examples`</b>: A `Tensor` of type `string`.
-    Each string is a JSON object serialized according to the JSON
-    mapping of the Example proto.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `string`.
-  Each string is a binary Example protocol buffer corresponding
-  to the respective element of `json_examples`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.delete_session_tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.delete_session_tensor.md
deleted file mode 100644
index 2f52941c5f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.delete_session_tensor.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.delete_session_tensor(name=None)` {#delete_session_tensor}
-
-Delete the tensor by feeding a tensor handle.
-
-This is EXPERIMENTAL and subject to change.
-
-Delete the tensor of a given tensor handle. The tensor is produced
-in a previous run() and stored in the state of the session.
-
-##### Args:
-
-
-*  <b>`name`</b>: Optional name prefix for the return tensor.
-
-##### Returns:
-
-  A pair of graph elements. The first is a placeholder for feeding a
-  tensor handle and the second is a deletion operation.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.div.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.div.md
new file mode 100644
index 0000000000..92eba7927a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.div.md
@@ -0,0 +1,15 @@
+### `tf.div(x, y, name=None)` {#div}
+
+Returns x / y element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.dynamic_stitch.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.dynamic_stitch.md
deleted file mode 100644
index 6bb1f8dd10..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.dynamic_stitch.md
+++ /dev/null
@@ -1,53 +0,0 @@
-### `tf.dynamic_stitch(indices, data, name=None)` {#dynamic_stitch}
-
-Interleave the values from the `data` tensors into a single tensor.
-
-Builds a merged tensor such that
-
-    merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
-
-For example, if each `indices[m]` is scalar or vector, we have
-
-    # Scalar indices
-    merged[indices[m], ...] = data[m][...]
-
-    # Vector indices
-    merged[indices[m][i], ...] = data[m][i, ...]
-
-Each `data[i].shape` must start with the corresponding `indices[i].shape`,
-and the rest of `data[i].shape` must be constant w.r.t. `i`.  That is, we
-must have `data[i].shape = indices[i].shape + constant`.  In terms of this
-`constant`, the output shape is
-
-    merged.shape = [max(indices)] + constant
-
-Values are merged in order, so if an index appears in both `indices[m][i]` and
-`indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the
-merged result.
-
-For example:
-
-    indices[0] = 6
-    indices[1] = [4, 1]
-    indices[2] = [[5, 2], [0, 3]]
-    data[0] = [61, 62]
-    data[1] = [[41, 42], [11, 12]]
-    data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
-    merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
-              [51, 52], [61, 62]]
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/DynamicStitch.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`indices`</b>: A list of at least 2 `Tensor` objects of type `int32`.
-*  <b>`data`</b>: A list with the same number of `Tensor` objects as `indices` of `Tensor` objects of the same type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `data`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.erfc.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.erfc.md
new file mode 100644
index 0000000000..d2ac7952e0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.erfc.md
@@ -0,0 +1,14 @@
+### `tf.erfc(x, name=None)` {#erfc}
+
+Computes the complementary error function of `x` element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.errors.AlreadyExistsError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.errors.AlreadyExistsError.md
new file mode 100644
index 0000000000..85425df298
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.errors.AlreadyExistsError.md
@@ -0,0 +1,14 @@
+Raised when an entity that we attempted to create already exists.
+
+For example, running an operation that saves a file
+(e.g. [`tf.train.Saver.save()`](../../api_docs/python/train.md#Saver.save))
+could potentially raise this exception if an explicit filename for an
+existing file was passed.
+
+- - -
+
+#### `tf.errors.AlreadyExistsError.__init__(node_def, op, message)` {#AlreadyExistsError.__init__}
+
+Creates an `AlreadyExistsError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.errors.InvalidArgumentError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.errors.InvalidArgumentError.md
new file mode 100644
index 0000000000..877325fe0b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.errors.InvalidArgumentError.md
@@ -0,0 +1,17 @@
+Raised when an operation receives an invalid argument.
+
+This may occur, for example, if an operation is receives an input
+tensor that has an invalid value or shape. For example, the
+[`tf.matmul()`](../../api_docs/python/math_ops.md#matmul) op will raise this
+error if it receives an input that is not a matrix, and the
+[`tf.reshape()`](../../api_docs/python/array_ops.md#reshape) op will raise
+this error if the new shape does not match the number of elements in the input
+tensor.
+
+- - -
+
+#### `tf.errors.InvalidArgumentError.__init__(node_def, op, message)` {#InvalidArgumentError.__init__}
+
+Creates an `InvalidArgumentError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.errors.UnavailableError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.errors.UnavailableError.md
new file mode 100644
index 0000000000..e212ae94ec
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.errors.UnavailableError.md
@@ -0,0 +1,11 @@
+Raised when the runtime is currently unavailable.
+
+This exception is not currently used.
+
+- - -
+
+#### `tf.errors.UnavailableError.__init__(node_def, op, message)` {#UnavailableError.__init__}
+
+Creates an `UnavailableError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.greater.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.greater.md
deleted file mode 100644
index c629a0286f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.greater.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.greater(x, y, name=None)` {#greater}
-
-Returns the truth value of (x > y) element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.group.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.group.md
new file mode 100644
index 0000000000..7958cf9e58
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.group.md
@@ -0,0 +1,25 @@
+### `tf.group(*inputs, **kwargs)` {#group}
+
+Create an op that groups multiple operations.
+
+When this op finishes, all ops in `input` have finished. This op has no
+output.
+
+See also `tuple` and `with_dependencies`.
+
+##### Args:
+
+
+*  <b>`*inputs`</b>: Zero or more tensors to group.
+*  <b>`**kwargs`</b>: Optional parameters to pass when constructing the NodeDef.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  An Operation that executes all its inputs.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If an unknown keyword argument is provided.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.encode_jpeg.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.encode_jpeg.md
deleted file mode 100644
index 24b1886c10..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.encode_jpeg.md
+++ /dev/null
@@ -1,51 +0,0 @@
-### `tf.image.encode_jpeg(image, format=None, quality=None, progressive=None, optimize_size=None, chroma_downsampling=None, density_unit=None, x_density=None, y_density=None, xmp_metadata=None, name=None)` {#encode_jpeg}
-
-JPEG-encode an image.
-
-`image` is a 3-D uint8 Tensor of shape `[height, width, channels]`.
-
-The attr `format` can be used to override the color format of the encoded
-output.  Values can be:
-
-*   `''`: Use a default format based on the number of channels in the image.
-*   `grayscale`: Output a grayscale JPEG image.  The `channels` dimension
-    of `image` must be 1.
-*   `rgb`: Output an RGB JPEG image. The `channels` dimension
-    of `image` must be 3.
-
-If `format` is not specified or is the empty string, a default format is picked
-in function of the number of channels in `image`:
-
-*   1: Output a grayscale image.
-*   3: Output an RGB image.
-
-##### Args:
-
-
-*  <b>`image`</b>: A `Tensor` of type `uint8`.
-    3-D with shape `[height, width, channels]`.
-*  <b>`format`</b>: An optional `string` from: `"", "grayscale", "rgb"`. Defaults to `""`.
-    Per pixel image format.
-*  <b>`quality`</b>: An optional `int`. Defaults to `95`.
-    Quality of the compression from 0 to 100 (higher is better and slower).
-*  <b>`progressive`</b>: An optional `bool`. Defaults to `False`.
-    If True, create a JPEG that loads progressively (coarse to fine).
-*  <b>`optimize_size`</b>: An optional `bool`. Defaults to `False`.
-    If True, spend CPU/RAM to reduce size with no quality change.
-*  <b>`chroma_downsampling`</b>: An optional `bool`. Defaults to `True`.
-    See http://en.wikipedia.org/wiki/Chroma_subsampling.
-*  <b>`density_unit`</b>: An optional `string` from: `"in", "cm"`. Defaults to `"in"`.
-    Unit used to specify `x_density` and `y_density`:
-    pixels per inch (`'in'`) or centimeter (`'cm'`).
-*  <b>`x_density`</b>: An optional `int`. Defaults to `300`.
-    Horizontal pixels per density unit.
-*  <b>`y_density`</b>: An optional `int`. Defaults to `300`.
-    Vertical pixels per density unit.
-*  <b>`xmp_metadata`</b>: An optional `string`. Defaults to `""`.
-    If not empty, embed this XMP metadata in the image header.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `string`. 0-D. JPEG-encoded image.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.extract_glimpse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.extract_glimpse.md
deleted file mode 100644
index e0ca72e2c5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.extract_glimpse.md
+++ /dev/null
@@ -1,31 +0,0 @@
-### `tf.image.extract_glimpse(input, size, offsets, centered=None, normalized=None, uniform_noise=None, name=None)` {#extract_glimpse}
-
-Extracts a glimpse from the input tensor.
-
-Returns a set of windows called glimpses extracted at location
-`offsets` from the input tensor. If the windows only partially
-overlaps the inputs, the non overlapping areas will be filled with
-random noise.
-
-The result is a 4-D tensor of shape `[batch_size, glimpse_height,
-glimpse_width, channels]`. The channels and batch dimensions are the
-same as that of the input tensor. The height and width of the output
-windows are specified in the `size` parameter.
-
-The argument `normalized` and `centered` controls how the windows are
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `float32`.
-*  <b>`size`</b>: A `Tensor` of type `int32`.
-*  <b>`offsets`</b>: A `Tensor` of type `float32`.
-*  <b>`centered`</b>: An optional `bool`. Defaults to `True`.
-*  <b>`normalized`</b>: An optional `bool`. Defaults to `True`.
-*  <b>`uniform_noise`</b>: An optional `bool`. Defaults to `True`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `float32`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.random_brightness.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.random_brightness.md
deleted file mode 100644
index 6c773b6985..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.random_brightness.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.image.random_brightness(image, max_delta, seed=None)` {#random_brightness}
-
-Adjust the brightness of images by a random factor.
-
-Equivalent to `adjust_brightness()` using a `delta` randomly picked in the
-interval `[-max_delta, max_delta)`.
-
-##### Args:
-
-
-*  <b>`image`</b>: An image.
-*  <b>`max_delta`</b>: float, must be non-negative.
-*  <b>`seed`</b>: A Python integer. Used to create a random seed. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-
-##### Returns:
-
-  The brightness-adjusted image.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `max_delta` is negative.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.random_contrast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.random_contrast.md
new file mode 100644
index 0000000000..76cd2292cf
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.random_contrast.md
@@ -0,0 +1,26 @@
+### `tf.image.random_contrast(image, lower, upper, seed=None)` {#random_contrast}
+
+Adjust the contrast of an image by a random factor.
+
+Equivalent to `adjust_contrast()` but uses a `contrast_factor` randomly
+picked in the interval `[lower, upper]`.
+
+##### Args:
+
+
+*  <b>`image`</b>: An image tensor with 3 or more dimensions.
+*  <b>`lower`</b>: float.  Lower bound for the random contrast factor.
+*  <b>`upper`</b>: float.  Upper bound for the random contrast factor.
+*  <b>`seed`</b>: A Python integer. Used to create a random seed. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+
+##### Returns:
+
+  The contrast-adjusted tensor.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `upper <= lower` or if `lower < 0`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.resize_area.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.resize_area.md
new file mode 100644
index 0000000000..dbc6fd1bcd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.resize_area.md
@@ -0,0 +1,24 @@
+### `tf.image.resize_area(images, size, align_corners=None, name=None)` {#resize_area}
+
+Resize `images` to `size` using area interpolation.
+
+Input images can be of different types but output images are always float.
+
+##### Args:
+
+
+*  <b>`images`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
+    4-D with shape `[batch, height, width, channels]`.
+*  <b>`size`</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
+    new size for the images.
+*  <b>`align_corners`</b>: An optional `bool`. Defaults to `False`.
+    If true, rescale input by (new_height - 1) / (height - 1), which
+    exactly aligns the 4 corners of images and resized images. If false, rescale
+    by new_height / height. Treat similarly the width dimension.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `float32`. 4-D with shape
+  `[batch, new_height, new_width, channels]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.resize_images.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.resize_images.md
deleted file mode 100644
index d010cac831..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.resize_images.md
+++ /dev/null
@@ -1,43 +0,0 @@
-### `tf.image.resize_images(images, new_height, new_width, method=0, align_corners=False)` {#resize_images}
-
-Resize `images` to `new_width`, `new_height` using the specified `method`.
-
-Resized images will be distorted if their original aspect ratio is not
-the same as `new_width`, `new_height`.  To avoid distortions see
-[`resize_image_with_crop_or_pad`](#resize_image_with_crop_or_pad).
-
-`method` can be one of:
-
-*   <b>`ResizeMethod.BILINEAR`</b>: [Bilinear interpolation.]
-    (https://en.wikipedia.org/wiki/Bilinear_interpolation)
-*   <b>`ResizeMethod.NEAREST_NEIGHBOR`</b>: [Nearest neighbor interpolation.]
-    (https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
-*   <b>`ResizeMethod.BICUBIC`</b>: [Bicubic interpolation.]
-    (https://en.wikipedia.org/wiki/Bicubic_interpolation)
-*   <b>`ResizeMethod.AREA`</b>: Area interpolation.
-
-##### Args:
-
-
-*  <b>`images`</b>: 4-D Tensor of shape `[batch, height, width, channels]` or
-          3-D Tensor of shape `[height, width, channels]`.
-*  <b>`new_height`</b>: integer.
-*  <b>`new_width`</b>: integer.
-*  <b>`method`</b>: ResizeMethod.  Defaults to `ResizeMethod.BILINEAR`.
-*  <b>`align_corners`</b>: bool. If true, exactly align all 4 corners of the input and
-                 output. Defaults to `false`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if the shape of `images` is incompatible with the
-    shape arguments to this function
-*  <b>`ValueError`</b>: if an unsupported resize method is specified.
-
-##### Returns:
-
-  If `images` was 4-D, a 4-D float Tensor of shape
-  `[batch, new_height, new_width, channels]`.
-  If `images` was 3-D, a 3-D float Tensor of shape
-  `[new_height, new_width, channels]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.transpose_image.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.transpose_image.md
deleted file mode 100644
index 1cc527d345..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.image.transpose_image.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.image.transpose_image(image)` {#transpose_image}
-
-Transpose an image by swapping the first and second dimension.
-
-See also `transpose()`.
-
-##### Args:
-
-
-*  <b>`image`</b>: 3-D tensor of shape `[height, width, channels]`
-
-##### Returns:
-
-  A 3-D tensor of shape `[width, height, channels]`
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if the shape of `image` not supported.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.initialize_local_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.initialize_local_variables.md
new file mode 100644
index 0000000000..2a56dbb9d6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.initialize_local_variables.md
@@ -0,0 +1,10 @@
+### `tf.initialize_local_variables()` {#initialize_local_variables}
+
+Returns an Op that initializes all local variables.
+
+This is just a shortcut for `initialize_variables(local_variables())`
+
+##### Returns:
+
+  An Op that initializes all local variables in the graph.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.inv.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.inv.md
new file mode 100644
index 0000000000..dfff52be12
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.inv.md
@@ -0,0 +1,16 @@
+### `tf.inv(x, name=None)` {#inv}
+
+Computes the reciprocal of x element-wise.
+
+I.e., \\(y = 1 / x\\).
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.matching_files.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.matching_files.md
deleted file mode 100644
index 297462d580..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.matching_files.md
+++ /dev/null
@@ -1,17 +0,0 @@
-### `tf.matching_files(pattern, name=None)` {#matching_files}
-
-Returns the set of files matching a pattern.
-
-Note that this routine only supports wildcard characters in the
-basename portion of the pattern, not in the directory portion.
-
-##### Args:
-
-
-*  <b>`pattern`</b>: A `Tensor` of type `string`. A (scalar) shell wildcard pattern.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `string`. A vector of matching filenames.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.matrix_solve_ls.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.matrix_solve_ls.md
new file mode 100644
index 0000000000..8f5548d2cb
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.matrix_solve_ls.md
@@ -0,0 +1,47 @@
+### `tf.matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None)` {#matrix_solve_ls}
+
+Solves a linear least-squares problem.
+
+Below we will use the following notation
+`matrix`=\\(A \in \Re^{m \times n}\\),
+`rhs`=\\(B  \in \Re^{m \times k}\\),
+`output`=\\(X  \in \Re^{n \times k}\\),
+`l2_regularizer`=\\(\lambda\\).
+
+If `fast` is `True`, then the solution is computed by solving the normal
+equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then
+\\(X = (A^T A + \lambda I)^{-1} A^T B\\), which solves the regularized
+least-squares problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k}}
+||A Z - B||_F^2 + \lambda ||Z||_F^2\\). If \\(m \lt n\\) then `output` is
+computed as \\(X = A^T (A A^T + \lambda I)^{-1} B\\),
+which (for \\(\lambda = 0\\)) is the minimum-norm solution to the
+under-determined linear system, i.e.
+\\(X = \mathrm{argmin}_{Z \in \Re^{n \times k}} ||Z||_F^2 \\),
+subject to \\(A Z = B\\).
+Notice that the fast path is only numerically stable when \\(A\\) is
+numerically full rank and has a condition number
+\\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach}}}\\)
+or \\(\lambda\\) is sufficiently large.
+
+If `fast` is `False` then the solution is computed using the rank revealing
+QR decomposition with column pivoting. This will always compute a
+least-squares solution that minimizes the residual norm
+\\(||A X - B||_F^2 \\), even when \\(A\\) is rank deficient or
+ill-conditioned. Notice: The current version does not compute a minimum norm
+solution. If `fast` is `False` then `l2_regularizer` is ignored.
+
+##### Args:
+
+
+*  <b>`matrix`</b>: 2-D `Tensor` of shape `[M, N]`.
+*  <b>`rhs`</b>: 2-D `Tensor` of shape is `[M, K]`.
+*  <b>`l2_regularizer`</b>: 0-D  `double` `Tensor`. Ignored if `fast=False`.
+*  <b>`fast`</b>: bool. Defaults to `True`.
+*  <b>`name`</b>: string, optional name of the operation.
+
+##### Returns:
+
+
+*  <b>`output`</b>: Matrix of shape `[N, K]` containing the matrix that solves
+    `matrix * output = rhs` in the least-squares sense.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.merge_all_summaries.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.merge_all_summaries.md
new file mode 100644
index 0000000000..40143de15d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.merge_all_summaries.md
@@ -0,0 +1,16 @@
+### `tf.merge_all_summaries(key='summaries')` {#merge_all_summaries}
+
+Merges all summaries collected in the default graph.
+
+##### Args:
+
+
+*  <b>`key`</b>: `GraphKey` used to collect the summaries.  Defaults to
+    `GraphKeys.SUMMARIES`.
+
+##### Returns:
+
+  If no summaries were collected, returns None.  Otherwise returns a scalar
+  `Tensor` of type `string` containing the serialized `Summary` protocol
+  buffer resulting from the merging.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.batch_normalization.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.batch_normalization.md
new file mode 100644
index 0000000000..eda1d7d053
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.batch_normalization.md
@@ -0,0 +1,46 @@
+### `tf.nn.batch_normalization(x, mean, variance, offset, scale, variance_epsilon, name=None)` {#batch_normalization}
+
+Batch normalization.
+
+As described in http://arxiv.org/abs/1502.03167.
+Normalizes a tensor by `mean` and `variance`, and applies (optionally) a
+`scale` \\(\gamma\\) to it, as well as an `offset` \\(\beta\\):
+
+\\(\frac{\gamma(x-\mu)}{\sigma}+\beta\\)
+
+`mean`, `variance`, `offset` and `scale` are all expected to be of one of two
+shapes:
+  * In all generality, they can have the same number of dimensions as the
+    input `x`, with identical sizes as `x` for the dimensions that are not
+    normalized over (the 'depth' dimension(s)), and dimension 1 for the
+    others which are being normalized over.
+    `mean` and `variance` in this case would typically be the outputs of
+    `tf.nn.moments(..., keep_dims=True)` during training, or running averages
+    thereof during inference.
+  * In the common case where the 'depth' dimension is the last dimension in
+    the input tensor `x`, they may be one dimensional tensors of the same
+    size as the 'depth' dimension.
+    This is the case for example for the common `[batch, depth]` layout of
+    fully-connected layers, and `[batch, height, width, depth]` for
+    convolutions.
+    `mean` and `variance` in this case would typically be the outputs of
+    `tf.nn.moments(..., keep_dims=False)` during training, or running averages
+    thereof during inference.
+
+##### Args:
+
+
+*  <b>`x`</b>: Input `Tensor` of arbitrary dimensionality.
+*  <b>`mean`</b>: A mean `Tensor`.
+*  <b>`variance`</b>: A variance `Tensor`.
+*  <b>`offset`</b>: An offset `Tensor`, often denoted \\(\beta\\) in equations, or
+    None. If present, will be added to the normalized tensor.
+*  <b>`scale`</b>: A scale `Tensor`, often denoted \\(\gamma\\) in equations, or
+    `None`. If present, the scale is applied to the normalized tensor.
+*  <b>`variance_epsilon`</b>: A small float number to avoid dividing by 0.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  the normalized, scaled, offset tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.nce_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.nce_loss.md
deleted file mode 100644
index 2fc7ab6b65..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.nce_loss.md
+++ /dev/null
@@ -1,53 +0,0 @@
-### `tf.nn.nce_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=False, partition_strategy='mod', name='nce_loss')` {#nce_loss}
-
-Computes and returns the noise-contrastive estimation training loss.
-
-See [Noise-contrastive estimation: A new estimation principle for
-unnormalized statistical models]
-(http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf).
-Also see our [Candidate Sampling Algorithms Reference]
-(../../extras/candidate_sampling.pdf)
-
-Note: In the case where `num_true` > 1, we assign to each target class
-the target probability 1 / `num_true` so that the target probabilities
-sum to 1 per-example.
-
-Note: It would be useful to allow a variable number of target classes per
-example.  We hope to provide this functionality in a future release.
-For now, if you have a variable number of target classes, you can pad them
-out to a constant number by either repeating them or by padding
-with an otherwise unused class.
-
-##### Args:
-
-
-*  <b>`weights`</b>: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
-      objects whose concatenation along dimension 0 has shape
-      [num_classes, dim].  The (possibly-partitioned) class embeddings.
-*  <b>`biases`</b>: A `Tensor` of shape `[num_classes]`.  The class biases.
-*  <b>`inputs`</b>: A `Tensor` of shape `[batch_size, dim]`.  The forward
-      activations of the input network.
-*  <b>`labels`</b>: A `Tensor` of type `int64` and shape `[batch_size,
-      num_true]`. The target classes.
-*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
-*  <b>`num_classes`</b>: An `int`. The number of possible classes.
-*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
-*  <b>`sampled_values`</b>: a tuple of (`sampled_candidates`, `true_expected_count`,
-      `sampled_expected_count`) returned by a `*_candidate_sampler` function.
-      (if None, we default to `log_uniform_candidate_sampler`)
-*  <b>`remove_accidental_hits`</b>: A `bool`.  Whether to remove "accidental hits"
-      where a sampled class equals one of the target classes.  If set to
-      `True`, this is a "Sampled Logistic" loss instead of NCE, and we are
-      learning to generate log-odds instead of log probabilities.  See
-      our [Candidate Sampling Algorithms Reference]
-      (../../extras/candidate_sampling.pdf).
-      Default is False.
-*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy, relevant
-      if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
-      Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `batch_size` 1-D tensor of per-example NCE losses.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.relu.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.relu.md
deleted file mode 100644
index 5811a1da96..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.relu.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.nn.relu(features, name=None)` {#relu}
-
-Computes rectified linear: `max(features, 0)`.
-
-##### Args:
-
-
-*  <b>`features`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `features`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.softmax_cross_entropy_with_logits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.softmax_cross_entropy_with_logits.md
deleted file mode 100644
index d6054c49ac..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.softmax_cross_entropy_with_logits.md
+++ /dev/null
@@ -1,36 +0,0 @@
-### `tf.nn.softmax_cross_entropy_with_logits(logits, labels, name=None)` {#softmax_cross_entropy_with_logits}
-
-Computes softmax cross entropy between `logits` and `labels`.
-
-Measures the probability error in discrete classification tasks in which the
-classes are mutually exclusive (each entry is in exactly one class).  For
-example, each CIFAR-10 image is labeled with one and only one label: an image
-can be a dog or a truck, but not both.
-
-**NOTE:**  While the classes are mutually exclusive, their probabilities
-need not be.  All that is required is that each row of `labels` is
-a valid probability distribution.  If they are not, the computation of the
-gradient will be incorrect.
-
-If using exclusive `labels` (wherein one and only
-one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`.
-
-**WARNING:** This op expects unscaled logits, since it performs a `softmax`
-on `logits` internally for efficiency.  Do not call this op with the
-output of `softmax`, as it will produce incorrect results.
-
-`logits` and `labels` must have the same shape `[batch_size, num_classes]`
-and the same dtype (either `float32` or `float64`).
-
-##### Args:
-
-
-*  <b>`logits`</b>: Unscaled log probabilities.
-*  <b>`labels`</b>: Each row `labels[i]` must be a valid probability distribution.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the
-  softmax cross entropy loss.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.softplus.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.softplus.md
deleted file mode 100644
index c0faef9687..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.softplus.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.nn.softplus(features, name=None)` {#softplus}
-
-Computes softplus: `log(exp(features) + 1)`.
-
-##### Args:
-
-
-*  <b>`features`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `features`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.top_k.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.top_k.md
deleted file mode 100644
index 819c0ad068..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.top_k.md
+++ /dev/null
@@ -1,31 +0,0 @@
-### `tf.nn.top_k(input, k=1, sorted=True, name=None)` {#top_k}
-
-Finds values and indices of the `k` largest entries for the last dimension.
-
-If the input is a vector (rank-1), finds the `k` largest entries in the vector
-and outputs their values and indices as vectors.  Thus `values[j]` is the
-`j`-th largest entry in `input`, and its index is `indices[j]`.
-
-For matrices (resp. higher rank input), computes the top `k` entries in each
-row (resp. vector along the last dimension).  Thus,
-
-    values.shape = indices.shape = input.shape[:-1] + [k]
-
-If two elements are equal, the lower-index element appears first.
-
-##### Args:
-
-
-*  <b>`input`</b>: 1-D or higher `Tensor` with last dimension at least `k`.
-*  <b>`k`</b>: 0-D `int32` `Tensor`.  Number of top elements to look for along the last
-    dimension (along each row for matrices).
-*  <b>`sorted`</b>: If true the resulting `k` elements will be sorted by the values in
-    descending order.
-*  <b>`name`</b>: Optional name for the operation.
-
-##### Returns:
-
-
-*  <b>`values`</b>: The `k` largest elements along each last dimensional slice.
-*  <b>`indices`</b>: The indices of `values` within the last dimension of `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.uniform_candidate_sampler.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.uniform_candidate_sampler.md
deleted file mode 100644
index c34056dc84..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.uniform_candidate_sampler.md
+++ /dev/null
@@ -1,49 +0,0 @@
-### `tf.nn.uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)` {#uniform_candidate_sampler}
-
-Samples a set of classes using a uniform base distribution.
-
-This operation randomly samples a tensor of sampled classes
-(`sampled_candidates`) from the range of integers `[0, range_max)`.
-
-The elements of `sampled_candidates` are drawn without replacement
-(if `unique=True`) or with replacement (if `unique=False`) from
-the base distribution.
-
-The base distribution for this operation is the uniform distribution
-over the range of integers `[0, range_max)`.
-
-In addition, this operation returns tensors `true_expected_count`
-and `sampled_expected_count` representing the number of times each
-of the target classes (`true_classes`) and the sampled
-classes (`sampled_candidates`) is expected to occur in an average
-tensor of sampled classes.  These values correspond to `Q(y|x)`
-defined in [this
-document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
-If `unique=True`, then these are post-rejection probabilities and we
-compute them approximately.
-
-##### Args:
-
-
-*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
-    num_true]`. The target classes.
-*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
-*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
-*  <b>`unique`</b>: A `bool`. Determines whether all sampled classes in a batch are
-    unique.
-*  <b>`range_max`</b>: An `int`. The number of possible classes.
-*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-
-*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
-    The sampled classes.
-*  <b>`true_expected_count`</b>: A tensor of type `float`.  Same shape as
-    `true_classes`. The expected counts under the sampling distribution
-    of each of `true_classes`.
-*  <b>`sampled_expected_count`</b>: A tensor of type `float`. Same shape as
-    `sampled_candidates`. The expected counts under the sampling distribution
-    of each of `sampled_candidates`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.no_op.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.no_op.md
new file mode 100644
index 0000000000..c1b5c0824b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.no_op.md
@@ -0,0 +1,13 @@
+### `tf.no_op(name=None)` {#no_op}
+
+Does nothing. Only useful as a placeholder for control edges.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.parse_single_example.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.parse_single_example.md
deleted file mode 100644
index e0bce09137..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.parse_single_example.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.parse_single_example(serialized, features, name=None, example_names=None)` {#parse_single_example}
-
-Parses a single `Example` proto.
-
-Similar to `parse_example`, except:
-
-For dense tensors, the returned `Tensor` is identical to the output of
-`parse_example`, except there is no batch dimension, the output shape is the
-same as the shape given in `dense_shape`.
-
-For `SparseTensor`s, the first (batch) column of the indices matrix is removed
-(the indices matrix is a column vector), the values vector is unchanged, and
-the first (`batch_size`) entry of the shape vector is removed (it is now a
-single element vector).
-
-##### Args:
-
-
-*  <b>`serialized`</b>: A scalar string Tensor, a single serialized Example.
-    See `_parse_single_example_raw` documentation for more details.
-*  <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
-    `VarLenFeature` values.
-*  <b>`name`</b>: A name for this operation (optional).
-*  <b>`example_names`</b>: (Optional) A scalar string Tensor, the associated name.
-    See `_parse_single_example_raw` documentation for more details.
-
-##### Returns:
-
-  A `dict` mapping feature keys to `Tensor` and `SparseTensor` values.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if any feature is invalid.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.polygamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.polygamma.md
new file mode 100644
index 0000000000..c8b5b2578a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.polygamma.md
@@ -0,0 +1,22 @@
+### `tf.polygamma(a, x, name=None)` {#polygamma}
+
+Compute the polygamma function \\(\psi^{(n)}(x)\\).
+
+The polygamma function is defined as:
+
+```
+\psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x)
+```
+where \\(\psi(x)\\) is the digamma function.
+
+##### Args:
+
+
+*  <b>`a`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`x`</b>: A `Tensor`. Must have the same type as `a`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `a`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.pow.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.pow.md
deleted file mode 100644
index 8588b72fb8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.pow.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.pow(x, y, name=None)` {#pow}
-
-Computes the power of one value to another.
-
-Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for
-corresponding elements in `x` and `y`. For example:
-
-```
-# tensor 'x' is [[2, 2], [3, 3]]
-# tensor 'y' is [[8, 16], [2, 3]]
-tf.pow(x, y) ==> [[256, 65536], [9, 27]]
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` of type `float`, `double`, `int32`, `complex64`, or `int64`.
-*  <b>`y`</b>: A `Tensor` of type `float`, `double`, `int32`, `complex64`, or `int64`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reduce_any.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reduce_any.md
deleted file mode 100644
index 58a911a8cf..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reduce_any.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.reduce_any(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_any}
-
-Computes the "logical or" of elements across dimensions of a tensor.
-
-Reduces `input_tensor` along the dimensions given in `reduction_indices`.
-Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
-entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
-are retained with length 1.
-
-If `reduction_indices` has no entries, all dimensions are reduced, and a
-tensor with a single element is returned.
-
-For example:
-
-```python
-# 'x' is [[True,  True]
-#         [False, False]]
-tf.reduce_any(x) ==> True
-tf.reduce_any(x, 0) ==> [True, True]
-tf.reduce_any(x, 1) ==> [True, False]
-```
-
-##### Args:
-
-
-*  <b>`input_tensor`</b>: The boolean tensor to reduce.
-*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
-    reduces all dimensions.
-*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The reduced tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reduce_prod.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reduce_prod.md
deleted file mode 100644
index a87daa33fb..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reduce_prod.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.reduce_prod(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_prod}
-
-Computes the product of elements across dimensions of a tensor.
-
-Reduces `input_tensor` along the dimensions given in `reduction_indices`.
-Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
-entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
-are retained with length 1.
-
-If `reduction_indices` has no entries, all dimensions are reduced, and a
-tensor with a single element is returned.
-
-##### Args:
-
-
-*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
-*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
-    reduces all dimensions.
-*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The reduced tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.report_uninitialized_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.report_uninitialized_variables.md
new file mode 100644
index 0000000000..35536e65d9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.report_uninitialized_variables.md
@@ -0,0 +1,19 @@
+### `tf.report_uninitialized_variables(var_list=None, name='report_uninitialized_variables')` {#report_uninitialized_variables}
+
+Adds ops to list the names of uninitialized variables.
+
+When run, it returns a 1-D tensor containing the names of uninitialized
+variables if there are any, or an empty array if there are none.
+
+##### Args:
+
+
+*  <b>`var_list`</b>: List of `Variable` objects to check. Defaults to the
+    value of `all_variables() + local_variables()`
+*  <b>`name`</b>: Optional name of the `Operation`.
+
+##### Returns:
+
+  A 1-D tensor containing names of the unintialized variables, or an empty 1-D
+  tensor if there are no variables or no uninitialized variables.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reset_default_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reset_default_graph.md
deleted file mode 100644
index ae5a906a0d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reset_default_graph.md
+++ /dev/null
@@ -1,10 +0,0 @@
-### `tf.reset_default_graph()` {#reset_default_graph}
-
-Clears the default graph stack and resets the global default graph.
-
-NOTE: The default graph is a property of the current thread. This
-function applies only to the current thread.  Calling this function while
-a `tf.Session` or `tf.InteractiveSession` is active will result in undefined
-behavior. Using any previously created `tf.Operation` or `tf.Tensor` objects
-after calling this function will result in undefined behavior.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reverse_sequence.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reverse_sequence.md
new file mode 100644
index 0000000000..fac4ac2ebe
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.reverse_sequence.md
@@ -0,0 +1,76 @@
+### `tf.reverse_sequence(input, seq_lengths, seq_dim, batch_dim=None, name=None)` {#reverse_sequence}
+
+Reverses variable length slices.
+
+This op first slices `input` along the dimension `batch_dim`, and for each
+slice `i`, reverses the first `seq_lengths[i]` elements along
+the dimension `seq_dim`.
+
+The elements of `seq_lengths` must obey `seq_lengths[i] < input.dims[seq_dim]`,
+and `seq_lengths` must be a vector of length `input.dims[batch_dim]`.
+
+The output slice `i` along dimension `batch_dim` is then given by input
+slice `i`, with the first `seq_lengths[i]` slices along dimension
+`seq_dim` reversed.
+
+For example:
+
+```prettyprint
+# Given this:
+batch_dim = 0
+seq_dim = 1
+input.dims = (4, 8, ...)
+seq_lengths = [7, 2, 3, 5]
+
+# then slices of input are reversed on seq_dim, but only up to seq_lengths:
+output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
+output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
+output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
+output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]
+
+# while entries past seq_lens are copied through:
+output[0, 7:, :, ...] = input[0, 7:, :, ...]
+output[1, 2:, :, ...] = input[1, 2:, :, ...]
+output[2, 3:, :, ...] = input[2, 3:, :, ...]
+output[3, 2:, :, ...] = input[3, 2:, :, ...]
+```
+
+In contrast, if:
+
+```prettyprint
+# Given this:
+batch_dim = 2
+seq_dim = 0
+input.dims = (8, ?, 4, ...)
+seq_lengths = [7, 2, 3, 5]
+
+# then slices of input are reversed on seq_dim, but only up to seq_lengths:
+output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
+output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
+output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
+output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]
+
+# while entries past seq_lens are copied through:
+output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
+output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
+output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
+output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. The input to reverse.
+*  <b>`seq_lengths`</b>: A `Tensor` of type `int64`.
+    1-D with length `input.dims(batch_dim)` and
+    `max(seq_lengths) < input.dims(seq_dim)`
+*  <b>`seq_dim`</b>: An `int`. The dimension which is partially reversed.
+*  <b>`batch_dim`</b>: An optional `int`. Defaults to `0`.
+    The dimension along which reversal is performed.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+  The partially reversed input. It has the same shape as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.round.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.round.md
deleted file mode 100644
index 8d2ce32921..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.round.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.round(x, name=None)` {#round}
-
-Rounds the values of a tensor to the nearest integer, element-wise.
-
-For example:
-
-```python
-# 'a' is [0.9, 2.5, 2.3, -4.4]
-tf.round(a) ==> [ 1.0, 3.0, 2.0, -4.0 ]
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` of type `float` or `double`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of same shape and type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.self_adjoint_eig.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.self_adjoint_eig.md
new file mode 100644
index 0000000000..efbc0cd3be
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.self_adjoint_eig.md
@@ -0,0 +1,21 @@
+### `tf.self_adjoint_eig(input, name=None)` {#self_adjoint_eig}
+
+Calculates the Eigen Decomposition of a square Self-Adjoint matrix.
+
+Only the lower-triangular part of the input will be used in this case. The
+upper-triangular part will not be read.
+
+The result is a M+1 x M matrix whose first row is the eigenvalues, and
+subsequent rows are eigenvectors.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
+    Shape is `[M, M]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`. Shape is `[M+1, M]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.set_random_seed.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.set_random_seed.md
deleted file mode 100644
index af817dbafa..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.set_random_seed.md
+++ /dev/null
@@ -1,98 +0,0 @@
-### `tf.set_random_seed(seed)` {#set_random_seed}
-
-Sets the graph-level random seed.
-
-Operations that rely on a random seed actually derive it from two seeds:
-the graph-level and operation-level seeds. This sets the graph-level seed.
-
-Its interactions with operation-level seeds is as follows:
-
-  1. If neither the graph-level nor the operation seed is set:
-    A random seed is used for this op.
-  2. If the graph-level seed is set, but the operation seed is not:
-    The system deterministically picks an operation seed in conjunction
-    with the graph-level seed so that it gets a unique random sequence.
-  3. If the graph-level seed is not set, but the operation seed is set:
-    A default graph-level seed and the specified operation seed are used to
-    determine the random sequence.
-  4. If both the graph-level and the operation seed are set:
-    Both seeds are used in conjunction to determine the random sequence.
-
-To illustrate the user-visible effects, consider these examples:
-
-To generate different sequences across sessions, set neither
-graph-level nor op-level seeds:
-
-```python
-a = tf.random_uniform([1])
-b = tf.random_normal([1])
-
-print("Session 1")
-with tf.Session() as sess1:
-  print(sess1.run(a))  # generates 'A1'
-  print(sess1.run(a))  # generates 'A2'
-  print(sess1.run(b))  # generates 'B1'
-  print(sess1.run(b))  # generates 'B2'
-
-print("Session 2")
-with tf.Session() as sess2:
-  print(sess2.run(a))  # generates 'A3'
-  print(sess2.run(a))  # generates 'A4'
-  print(sess2.run(b))  # generates 'B3'
-  print(sess2.run(b))  # generates 'B4'
-```
-
-To generate the same repeatable sequence for an op across sessions, set the
-seed for the op:
-
-```python
-a = tf.random_uniform([1], seed=1)
-b = tf.random_normal([1])
-
-# Repeatedly running this block with the same graph will generate the same
-# sequence of values for 'a', but different sequences of values for 'b'.
-print("Session 1")
-with tf.Session() as sess1:
-  print(sess1.run(a))  # generates 'A1'
-  print(sess1.run(a))  # generates 'A2'
-  print(sess1.run(b))  # generates 'B1'
-  print(sess1.run(b))  # generates 'B2'
-
-print("Session 2")
-with tf.Session() as sess2:
-  print(sess2.run(a))  # generates 'A1'
-  print(sess2.run(a))  # generates 'A2'
-  print(sess2.run(b))  # generates 'B3'
-  print(sess2.run(b))  # generates 'B4'
-```
-
-To make the random sequences generated by all ops be repeatable across
-sessions, set a graph-level seed:
-
-```python
-tf.set_random_seed(1234)
-a = tf.random_uniform([1])
-b = tf.random_normal([1])
-
-# Repeatedly running this block with the same graph will generate different
-# sequences of 'a' and 'b'.
-print("Session 1")
-with tf.Session() as sess1:
-  print(sess1.run(a))  # generates 'A1'
-  print(sess1.run(a))  # generates 'A2'
-  print(sess1.run(b))  # generates 'B1'
-  print(sess1.run(b))  # generates 'B2'
-
-print("Session 2")
-with tf.Session() as sess2:
-  print(sess2.run(a))  # generates 'A1'
-  print(sess2.run(a))  # generates 'A2'
-  print(sess2.run(b))  # generates 'B1'
-  print(sess2.run(b))  # generates 'B2'
-```
-
-##### Args:
-
-
-*  <b>`seed`</b>: integer.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.shape.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.shape.md
new file mode 100644
index 0000000000..4262f41a3d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.shape.md
@@ -0,0 +1,23 @@
+### `tf.shape(input, name=None)` {#shape}
+
+Returns the shape of a tensor.
+
+This operation returns a 1-D integer tensor representing the shape of `input`.
+
+For example:
+
+```prettyprint
+# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
+shape(t) ==> [2, 2, 3]
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `int32`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sign.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sign.md
deleted file mode 100644
index f0c021a741..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sign.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.sign(x, name=None)` {#sign}
-
-Returns an element-wise indication of the sign of a number.
-
-`y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`.
-
-For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.space_to_batch.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.space_to_batch.md
deleted file mode 100644
index 1999f21ea3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.space_to_batch.md
+++ /dev/null
@@ -1,44 +0,0 @@
-### `tf.space_to_batch(input, paddings, block_size, name=None)` {#space_to_batch}
-
-SpaceToBatch for 4-D tensors of type T.
-
-Zero-pads and then rearranges (permutes) blocks of spatial data into batch.
-More specifically, this op outputs a copy of the input tensor where values from
-the `height` and `width` dimensions are moved to the `batch` dimension. After
-the zero-padding, both `height` and `width` of the input must be divisible by the
-block size.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. 4-D with shape `[batch, height, width, depth]`.
-*  <b>`paddings`</b>: A `Tensor` of type `int32`.
-    2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
-      the padding of the input with zeros across the spatial dimensions as follows:
-
-          paddings = [[pad_top, pad_bottom], [pad_left, pad_right]]
-
-      The effective spatial dimensions of the zero-padded input tensor will be:
-
-          height_pad = pad_top + height + pad_bottom
-          width_pad = pad_left + width + pad_right
-
-    The attr `block_size` must be greater than one. It indicates the block size.
-
-      * Non-overlapping blocks of size `block_size x block size` in the height and
-        width dimensions are rearranged into the batch dimension at each location.
-      * The batch of the output tensor is `batch * block_size * block_size`.
-      * Both height_pad and width_pad must be divisible by block_size.
-
-    The shape of the output will be:
-
-        [batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
-         depth]
-
-*  <b>`block_size`</b>: An `int`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sparse_merge.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sparse_merge.md
deleted file mode 100644
index 38742123d6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sparse_merge.md
+++ /dev/null
@@ -1,73 +0,0 @@
-### `tf.sparse_merge(sp_ids, sp_values, vocab_size, name=None)` {#sparse_merge}
-
-Combines a batch of feature ids and values into a single `SparseTensor`.
-
-The most common use case for this function occurs when feature ids and
-their corresponding values are stored in `Example` protos on disk.
-`parse_example` will return a batch of ids and a batch of values, and this
-function joins them into a single logical `SparseTensor` for use in
-functions such as `sparse_tensor_dense_matmul`, `sparse_to_dense`, etc.
-
-The `SparseTensor` returned by this function has the following properties:
-
-  - `indices` is equivalent to `sp_ids.indices` with the last
-    dimension discarded and replaced with `sp_ids.values`.
-  - `values` is simply `sp_values.values`.
-  - If `sp_ids.shape = [D0, D1, ..., Dn, K]`, then
-    `output.shape = [D0, D1, ..., Dn, vocab_size]`.
-
-For example, consider the following feature vectors:
-
-  vector1 = [-3, 0, 0, 0, 0, 0]
-  vector2 = [ 0, 1, 0, 4, 1, 0]
-  vector3 = [ 5, 0, 0, 9, 0, 0]
-
-These might be stored sparsely in the following Example protos by storing
-only the feature ids (column number if the vectors are treated as a matrix)
-of the non-zero elements and the corresponding values:
-
-  examples = [Example(features={
-                  "ids": Feature(int64_list=Int64List(value=[0])),
-                  "values": Feature(float_list=FloatList(value=[-3]))}),
-              Example(features={
-                  "ids": Feature(int64_list=Int64List(value=[1, 4, 3])),
-                  "values": Feature(float_list=FloatList(value=[1, 1, 4]))}),
-              Example(features={
-                  "ids": Feature(int64_list=Int64List(value=[0, 3])),
-                  "values": Feature(float_list=FloatList(value=[5, 9]))})]
-
-The result of calling parse_example on these examples will produce a
-dictionary with entries for "ids" and "values". Passing those two objects
-to this function along with vocab_size=6, will produce a `SparseTensor` that
-sparsely represents all three instances. Namely, the `indices` property will
-contain the coordinates of the non-zero entries in the feature matrix (the
-first dimension is the row number in the matrix, i.e., the index within the
-batch, and the second dimension is the column number, i.e., the feature id);
-`values` will contain the actual values. `shape` will be the shape of the
-original matrix, i.e., (3, 6). For our example above, the output will be
-equal to:
-
-  SparseTensor(indices=[[0, 0], [1, 1], [1, 3], [1, 4], [2, 0], [2, 3]],
-               values=[-3, 1, 4, 1, 5, 9],
-               shape=[3, 6])
-
-##### Args:
-
-
-*  <b>`sp_ids`</b>: A `SparseTensor` with `values` property of type `int32`
-    or `int64`.
-*  <b>`sp_values`</b>: A`SparseTensor` of any type.
-*  <b>`vocab_size`</b>: A scalar `int64` Tensor (or Python int) containing the new size
-    of the last dimension, `all(0 <= sp_ids.values < vocab_size)`.
-*  <b>`name`</b>: A name prefix for the returned tensors (optional)
-
-##### Returns:
-
-  A `SparseTensor` compactly representing a batch of feature ids and values,
-  useful for passing to functions that expect such a `SparseTensor`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `sp_ids` or `sp_values` are not a `SparseTensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sparse_segment_sqrt_n_grad.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sparse_segment_sqrt_n_grad.md
deleted file mode 100644
index 2a2e0c9e33..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sparse_segment_sqrt_n_grad.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.sparse_segment_sqrt_n_grad(grad, indices, segment_ids, output_dim0, name=None)` {#sparse_segment_sqrt_n_grad}
-
-Computes gradients for SparseSegmentSqrtN.
-
-Returns tensor "output" with same shape as grad, except for dimension 0 whose
-value is output_dim0.
-
-##### Args:
-
-
-*  <b>`grad`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-    gradient propagated to the SparseSegmentSqrtN op.
-*  <b>`indices`</b>: A `Tensor` of type `int32`.
-    indices passed to the corresponding SparseSegmentSqrtN op.
-*  <b>`segment_ids`</b>: A `Tensor` of type `int32`.
-    segment_ids passed to the corresponding SparseSegmentSqrtN op.
-*  <b>`output_dim0`</b>: A `Tensor` of type `int32`.
-    dimension 0 of "data" passed to SparseSegmentSqrtN op.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `grad`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sparse_to_dense.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sparse_to_dense.md
deleted file mode 100644
index d4df5a9183..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.sparse_to_dense.md
+++ /dev/null
@@ -1,45 +0,0 @@
-### `tf.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=0, validate_indices=True, name=None)` {#sparse_to_dense}
-
-Converts a sparse representation into a dense tensor.
-
-Builds an array `dense` with shape `output_shape` such that
-
-```python
-# If sparse_indices is scalar
-dense[i] = (i == sparse_indices ? sparse_values : default_value)
-
-# If sparse_indices is a vector, then for each i
-dense[sparse_indices[i]] = sparse_values[i]
-
-# If sparse_indices is an n by d matrix, then for each i in [0, n)
-dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
-```
-
-All other values in `dense` are set to `default_value`.  If `sparse_values`
-is a scalar, all sparse indices are set to this single value.
-
-Indices should be sorted in lexicographic order, and indices must not
-contain any repeats. If `validate_indices` is True, these properties
-are checked during execution.
-
-##### Args:
-
-
-*  <b>`sparse_indices`</b>: A 0-D, 1-D, or 2-D `Tensor` of type `int32` or `int64`.
-    `sparse_indices[i]` contains the complete index where `sparse_values[i]`
-    will be placed.
-*  <b>`output_shape`</b>: A 1-D `Tensor` of the same type as `sparse_indices`.  Shape
-    of the dense output tensor.
-*  <b>`sparse_values`</b>: A 0-D or 1-D `Tensor`.  Values corresponding to each row of
-    `sparse_indices`, or a scalar value to be used for all sparse indices.
-*  <b>`default_value`</b>: A 0-D `Tensor` of the same type as `sparse_values`.  Value
-    to set for indices not specified in `sparse_indices`.  Defaults to zero.
-*  <b>`validate_indices`</b>: A boolean value.  If True, indices are checked to make
-    sure they are sorted in lexicographic order and that there are no repeats.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  Dense `Tensor` of shape `output_shape`.  Has the same type as
-  `sparse_values`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.squared_difference.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.squared_difference.md
deleted file mode 100644
index d6bb175669..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.squared_difference.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.squared_difference(x, y, name=None)` {#squared_difference}
-
-Returns (x - y)(x - y) element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.squeeze.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.squeeze.md
deleted file mode 100644
index e76c02e115..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.squeeze.md
+++ /dev/null
@@ -1,38 +0,0 @@
-### `tf.squeeze(input, squeeze_dims=None, name=None)` {#squeeze}
-
-Removes dimensions of size 1 from the shape of a tensor.
-
-Given a tensor `input`, this operation returns a tensor of the same type with
-all dimensions of size 1 removed. If you don't want to remove all size 1
-dimensions, you can remove specific size 1 dimensions by specifying
-`squeeze_dims`.
-
-For example:
-
-```prettyprint
-# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
-shape(squeeze(t)) ==> [2, 3]
-```
-
-Or, to remove specific size 1 dimensions:
-
-```prettyprint
-# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
-shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. The `input` to squeeze.
-*  <b>`squeeze_dims`</b>: An optional list of `ints`. Defaults to `[]`.
-    If specified, only squeezes the dimensions listed. The dimension
-    index starts at 0. It is an error to squeeze a dimension that is not 1.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-  Contains the same data as `input`, but has one or more dimensions of
-  size 1 removed.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.string_to_hash_bucket_fast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.string_to_hash_bucket_fast.md
new file mode 100644
index 0000000000..e684058326
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.string_to_hash_bucket_fast.md
@@ -0,0 +1,23 @@
+### `tf.string_to_hash_bucket_fast(input, num_buckets, name=None)` {#string_to_hash_bucket_fast}
+
+Converts each string in the input Tensor to its hash mod by a number of buckets.
+
+The hash function is deterministic on the content of the string within the
+process and will never change. However, it is not suitable for cryptography.
+This function may be used when CPU time is scarce and inputs are trusted or
+unimportant. There is a risk of adversaries constructing inputs that all hash
+to the same bucket. To prevent this problem, use a strong hash function with
+`tf.string_to_hash_bucket_strong`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `string`. The strings to assign a hash bucket.
+*  <b>`num_buckets`</b>: An `int` that is `>= 1`. The number of buckets.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `int64`.
+  A Tensor of the same shape as the input `string_tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.string_to_number.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.string_to_number.md
deleted file mode 100644
index dfbc0c6b6c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.string_to_number.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.string_to_number(string_tensor, out_type=None, name=None)` {#string_to_number}
-
-Converts each string in the input Tensor to the specified numeric type.
-
-(Note that int32 overflow results in an error while float overflow
-results in a rounded value.)
-
-##### Args:
-
-
-*  <b>`string_tensor`</b>: A `Tensor` of type `string`.
-*  <b>`out_type`</b>: An optional `tf.DType` from: `tf.float32, tf.int32`. Defaults to `tf.float32`.
-    The numeric type to interpret each string in string_tensor as.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `out_type`.
-  A Tensor of the same shape as the input `string_tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.tanh.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.tanh.md
deleted file mode 100644
index b41e51c019..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.tanh.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.tanh(x, name=None)` {#tanh}
-
-Computes hyperbolic tangent of `x` element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`,
-    or `qint32`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A Tensor with the same type as `x` if `x.dtype != qint32` otherwise
-    the return type is `quint8`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.test.main.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.test.main.md
new file mode 100644
index 0000000000..c7aa9cf801
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.test.main.md
@@ -0,0 +1,4 @@
+### `tf.test.main()` {#main}
+
+Runs all unit tests.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.to_double.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.to_double.md
new file mode 100644
index 0000000000..0cabea178e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.to_double.md
@@ -0,0 +1,19 @@
+### `tf.to_double(x, name='ToDouble')` {#to_double}
+
+Casts a tensor to type `float64`.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` or `SparseTensor` with same shape as `x` with type `float64`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `x` cannot be cast to the `float64`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.AdadeltaOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.AdadeltaOptimizer.md
new file mode 100644
index 0000000000..9a14c50dc8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.AdadeltaOptimizer.md
@@ -0,0 +1,23 @@
+Optimizer that implements the Adadelta algorithm. 
+
+See [M. D. Zeiler](http://arxiv.org/abs/1212.5701)
+([pdf](http://arxiv.org/pdf/1212.5701v1.pdf))
+
+- - -
+
+#### `tf.train.AdadeltaOptimizer.__init__(learning_rate=0.001, rho=0.95, epsilon=1e-08, use_locking=False, name='Adadelta')` {#AdadeltaOptimizer.__init__}
+
+Construct a new Adadelta optimizer.
+
+##### Args:
+
+
+*  <b>`learning_rate`</b>: A `Tensor` or a floating point value. The learning rate.
+*  <b>`rho`</b>: A `Tensor` or a floating point value. The decay rate.
+*  <b>`epsilon`</b>: A `Tensor` or a floating point value.  A constant epsilon used
+           to better conditioning the grad update.
+*  <b>`use_locking`</b>: If `True` use locks for update operations.
+*  <b>`name`</b>: Optional name prefix for the operations created when applying
+    gradients.  Defaults to "Adadelta".
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.GradientDescentOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.GradientDescentOptimizer.md
deleted file mode 100644
index 99a5f1f0b1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.GradientDescentOptimizer.md
+++ /dev/null
@@ -1,18 +0,0 @@
-Optimizer that implements the gradient descent algorithm.
-
-- - -
-
-#### `tf.train.GradientDescentOptimizer.__init__(learning_rate, use_locking=False, name='GradientDescent')` {#GradientDescentOptimizer.__init__}
-
-Construct a new gradient descent optimizer.
-
-##### Args:
-
-
-*  <b>`learning_rate`</b>: A Tensor or a floating point value.  The learning
-    rate to use.
-*  <b>`use_locking`</b>: If True use locks for update operations.
-*  <b>`name`</b>: Optional name prefix for the operations created when applying
-    gradients. Defaults to "GradientDescent".
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.LooperThread.loop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.LooperThread.loop.md
new file mode 100644
index 0000000000..6665ca7369
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.LooperThread.loop.md
@@ -0,0 +1,22 @@
+#### `tf.train.LooperThread.loop(coord, timer_interval_secs, target, args=None, kwargs=None)` {#LooperThread.loop}
+
+Start a LooperThread that calls a function periodically.
+
+If `timer_interval_secs` is None the thread calls `target(args)`
+repeatedly.  Otherwise `target(args)` is called every `timer_interval_secs`
+seconds.  The thread terminates when a stop of the coordinator is
+requested.
+
+##### Args:
+
+
+*  <b>`coord`</b>: A Coordinator.
+*  <b>`timer_interval_secs`</b>: Number. Time boundaries at which to call `target`.
+*  <b>`target`</b>: A callable object.
+*  <b>`args`</b>: Optional arguments to pass to `target` when calling it.
+*  <b>`kwargs`</b>: Optional keyword arguments to pass to `target` when calling it.
+
+##### Returns:
+
+  The started thread.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.QueueRunner.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.QueueRunner.md
new file mode 100644
index 0000000000..812dc2b5bd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.QueueRunner.md
@@ -0,0 +1,161 @@
+Holds a list of enqueue operations for a queue, each to be run in a thread.
+
+Queues are a convenient TensorFlow mechanism to compute tensors
+asynchronously using multiple threads. For example in the canonical 'Input
+Reader' setup one set of threads generates filenames in a queue; a second set
+of threads read records from the files, processes them, and enqueues tensors
+on a second queue; a third set of threads dequeues these input records to
+construct batches and runs them through training operations.
+
+There are several delicate issues when running multiple threads that way:
+closing the queues in sequence as the input is exhausted, correctly catching
+and reporting exceptions, etc.
+
+The `QueueRunner`, combined with the `Coordinator`, helps handle these issues.
+- - -
+
+#### `tf.train.QueueRunner.__init__(queue=None, enqueue_ops=None, close_op=None, cancel_op=None, queue_runner_def=None)` {#QueueRunner.__init__}
+
+Create a QueueRunner.
+
+On construction the `QueueRunner` adds an op to close the queue.  That op
+will be run if the enqueue ops raise exceptions.
+
+When you later call the `create_threads()` method, the `QueueRunner` will
+create one thread for each op in `enqueue_ops`.  Each thread will run its
+enqueue op in parallel with the other threads.  The enqueue ops do not have
+to all be the same op, but it is expected that they all enqueue tensors in
+`queue`.
+
+##### Args:
+
+
+*  <b>`queue`</b>: A `Queue`.
+*  <b>`enqueue_ops`</b>: List of enqueue ops to run in threads later.
+*  <b>`close_op`</b>: Op to close the queue. Pending enqueue ops are preserved.
+*  <b>`cancel_op`</b>: Op to close the queue and cancel pending enqueue ops.
+*  <b>`queue_runner_def`</b>: Optional `QueueRunnerDef` protocol buffer. If specified,
+    recreates the QueueRunner from its contents. `queue_runner_def` and the
+    other arguments are mutually exclusive.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If both `queue_runner_def` and `queue` are both specified.
+*  <b>`ValueError`</b>: If `queue` or `enqueue_ops` are not provided when not
+    restoring from `queue_runner_def`.
+
+
+- - -
+
+#### `tf.train.QueueRunner.cancel_op` {#QueueRunner.cancel_op}
+
+
+
+
+- - -
+
+#### `tf.train.QueueRunner.close_op` {#QueueRunner.close_op}
+
+
+
+
+- - -
+
+#### `tf.train.QueueRunner.create_threads(sess, coord=None, daemon=False, start=False)` {#QueueRunner.create_threads}
+
+Create threads to run the enqueue ops.
+
+This method requires a session in which the graph was launched.  It creates
+a list of threads, optionally starting them.  There is one thread for each
+op passed in `enqueue_ops`.
+
+The `coord` argument is an optional coordinator, that the threads will use
+to terminate together and report exceptions.  If a coordinator is given,
+this method starts an additional thread to close the queue when the
+coordinator requests a stop.
+
+This method may be called again as long as all threads from a previous call
+have stopped.
+
+##### Args:
+
+
+*  <b>`sess`</b>: A `Session`.
+*  <b>`coord`</b>: Optional `Coordinator` object for reporting errors and checking
+    stop conditions.
+*  <b>`daemon`</b>: Boolean.  If `True` make the threads daemon threads.
+*  <b>`start`</b>: Boolean.  If `True` starts the threads.  If `False` the
+    caller must call the `start()` method of the returned threads.
+
+##### Returns:
+
+  A list of threads.
+
+##### Raises:
+
+
+*  <b>`RuntimeError`</b>: If threads from a previous call to `create_threads()` are
+  still running.
+
+
+- - -
+
+#### `tf.train.QueueRunner.enqueue_ops` {#QueueRunner.enqueue_ops}
+
+
+
+
+- - -
+
+#### `tf.train.QueueRunner.exceptions_raised` {#QueueRunner.exceptions_raised}
+
+Exceptions raised but not handled by the `QueueRunner` threads.
+
+Exceptions raised in queue runner threads are handled in one of two ways
+depending on whether or not a `Coordinator` was passed to
+`create_threads()`:
+
+* With a `Coordinator`, exceptions are reported to the coordinator and
+  forgotten by the `QueueRunner`.
+* Without a `Coordinator`, exceptions are captured by the `QueueRunner` and
+  made available in this `exceptions_raised` property.
+
+##### Returns:
+
+  A list of Python `Exception` objects.  The list is empty if no exception
+  was captured.  (No exceptions are captured when using a Coordinator.)
+
+
+- - -
+
+#### `tf.train.QueueRunner.from_proto(queue_runner_def)` {#QueueRunner.from_proto}
+
+Returns a `QueueRunner` object created from `queue_runner_def`.
+
+
+- - -
+
+#### `tf.train.QueueRunner.name` {#QueueRunner.name}
+
+The string name of the underlying Queue.
+
+
+- - -
+
+#### `tf.train.QueueRunner.queue` {#QueueRunner.queue}
+
+
+
+
+- - -
+
+#### `tf.train.QueueRunner.to_proto()` {#QueueRunner.to_proto}
+
+Converts this `QueueRunner` to a `QueueRunnerDef` protocol buffer.
+
+##### Returns:
+
+  A `QueueRunnerDef` protocol buffer.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.RMSPropOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.RMSPropOptimizer.md
new file mode 100644
index 0000000000..317f1e2adf
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.RMSPropOptimizer.md
@@ -0,0 +1,23 @@
+Optimizer that implements the RMSProp algorithm.
+
+See the [paper]
+(http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).
+
+- - -
+
+#### `tf.train.RMSPropOptimizer.__init__(learning_rate, decay=0.9, momentum=0.0, epsilon=1e-10, use_locking=False, name='RMSProp')` {#RMSPropOptimizer.__init__}
+
+Construct a new RMSProp optimizer.
+
+##### Args:
+
+
+*  <b>`learning_rate`</b>: A Tensor or a floating point value.  The learning rate.
+*  <b>`decay`</b>: Discounting factor for the history/coming gradient
+*  <b>`momentum`</b>: A scalar tensor.
+*  <b>`epsilon`</b>: Small value to avoid zero denominator.
+*  <b>`use_locking`</b>: If True use locks for update operation.
+*  <b>`name`</b>: Optional name prefix for the operations created when applying
+    gradients. Defaults to "RMSProp".
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.batch_join.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.batch_join.md
deleted file mode 100644
index f6985b0a44..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.batch_join.md
+++ /dev/null
@@ -1,79 +0,0 @@
-### `tf.train.batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, shared_name=None, name=None)` {#batch_join}
-
-Runs a list of tensors to fill a queue to create batches of examples.
-
-The `tensors_list` argument is a list of tuples of tensors, or a list of
-dictionaries of tensors.  Each element in the list is treated similarily
-to the `tensors` argument of `tf.train.batch()`.
-
-Enqueues a different list of tensors in different threads.
-Implemented using a queue -- a `QueueRunner` for the queue
-is added to the current `Graph`'s `QUEUE_RUNNER` collection.
-
-`len(tensors_list)` threads will be started,
-with thread `i` enqueuing the tensors from
-`tensors_list[i]`. `tensors_list[i1][j]` must match
-`tensors_list[i2][j]` in type and shape, except in the first
-dimension if `enqueue_many` is true.
-
-If `enqueue_many` is `False`, each `tensors_list[i]` is assumed
-to represent a single example. An input tensor `x` will be output as a
-tensor with shape `[batch_size] + x.shape`.
-
-If `enqueue_many` is `True`, `tensors_list[i]` is assumed to
-represent a batch of examples, where the first dimension is indexed
-by example, and all members of `tensors_list[i]` should have the
-same size in the first dimension.  The slices of any input tensor
-`x` are treated as examples, and the output tensors will have shape
-`[batch_size] + x.shape[1:]`.
-
-The `capacity` argument controls the how long the prefetching is allowed to
-grow the queues.
-
-The returned operation is a dequeue operation and will throw
-`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
-operation is feeding another input queue, its queue runner will catch
-this exception, however, if this operation is used in your main thread
-you are responsible for catching this yourself.
-
-*N.B.:* If `dynamic_pad` is `False`, you must ensure that either
-(i) the `shapes` argument is passed, or (ii) all of the tensors in
-`tensors_list` must have fully-defined shapes. `ValueError` will be
-raised if neither of these conditions holds.
-
-If `dynamic_pad` is `True`, it is sufficient that the *rank* of the
-tensors is known, but individual dimensions may have value `None`.
-In this case, for each enqueue the dimensions with value `None`
-may have a variable length; upon dequeue, the output tensors will be padded
-on the right to the maximum shape of the tensors in the current minibatch.
-For numbers, this padding takes value 0.  For strings, this padding is
-the empty string.  See `PaddingFIFOQueue` for more info.
-
-##### Args:
-
-
-*  <b>`tensors_list`</b>: A list of tuples or dictionaries of tensors to enqueue.
-*  <b>`batch_size`</b>: An integer. The new batch size pulled from the queue.
-*  <b>`capacity`</b>: An integer. The maximum number of elements in the queue.
-*  <b>`enqueue_many`</b>: Whether each tensor in `tensor_list_list` is a single
-    example.
-*  <b>`shapes`</b>: (Optional) The shapes for each example.  Defaults to the
-    inferred shapes for `tensor_list_list[i]`.
-*  <b>`dynamic_pad`</b>: Boolean.  Allow variable dimensions in input shapes.
-    The given dimensions are padded upon dequeue so that tensors within a
-    batch have the same shapes.
-*  <b>`shared_name`</b>: (Optional) If set, this queue will be shared under the given
-    name across multiple sessions.
-*  <b>`name`</b>: (Optional) A name for the operations.
-
-##### Returns:
-
-  A list or dictionary of tensors with the same number and types as
-  `tensors_list[i]`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be
-    inferred from the elements of `tensor_list_list`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.generate_checkpoint_state_proto.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.generate_checkpoint_state_proto.md
new file mode 100644
index 0000000000..7405b289e3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.generate_checkpoint_state_proto.md
@@ -0,0 +1,20 @@
+### `tf.train.generate_checkpoint_state_proto(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None)` {#generate_checkpoint_state_proto}
+
+Generates a checkpoint state proto.
+
+##### Args:
+
+
+*  <b>`save_dir`</b>: Directory where the model was saved.
+*  <b>`model_checkpoint_path`</b>: The checkpoint file.
+*  <b>`all_model_checkpoint_paths`</b>: List of strings.  Paths to all not-yet-deleted
+    checkpoints, sorted from oldest to newest.  If this is a non-empty list,
+    the last element must be equal to model_checkpoint_path.  These paths
+    are also saved in the CheckpointState proto.
+
+##### Returns:
+
+  CheckpointState proto with model_checkpoint_path and
+  all_model_checkpoint_paths updated to either absolute paths or
+  relative paths to the current save_dir.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.limit_epochs.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.limit_epochs.md
new file mode 100644
index 0000000000..ba3e710df4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.train.limit_epochs.md
@@ -0,0 +1,21 @@
+### `tf.train.limit_epochs(tensor, num_epochs=None, name=None)` {#limit_epochs}
+
+Returns tensor `num_epochs` times and then raises an `OutOfRange` error.
+
+##### Args:
+
+
+*  <b>`tensor`</b>: Any `Tensor`.
+*  <b>`num_epochs`</b>: A positive integer (optional).  If specified, limits the number
+    of steps the output tensor may be evaluated.
+*  <b>`name`</b>: A name for the operations (optional).
+
+##### Returns:
+
+  tensor or `OutOfRange`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `num_epochs` is invalid.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.truncated_normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.truncated_normal.md
new file mode 100644
index 0000000000..9ae13882d3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.truncated_normal.md
@@ -0,0 +1,27 @@
+### `tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)` {#truncated_normal}
+
+Outputs random values from a truncated normal distribution.
+
+The generated values follow a normal distribution with specified mean and
+standard deviation, except that values whose magnitude is more than 2 standard
+deviations from the mean are dropped and re-picked.
+
+##### Args:
+
+
+*  <b>`shape`</b>: A 1-D integer Tensor or Python array. The shape of the output tensor.
+*  <b>`mean`</b>: A 0-D Tensor or Python value of type `dtype`. The mean of the
+    truncated normal distribution.
+*  <b>`stddev`</b>: A 0-D Tensor or Python value of type `dtype`. The standard deviation
+    of the truncated normal distribution.
+*  <b>`dtype`</b>: The type of the output.
+*  <b>`seed`</b>: A Python integer. Used to create a random seed for the distribution.
+    See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tensor of the specified shape filled with random truncated normal values.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.unique_with_counts.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.unique_with_counts.md
deleted file mode 100644
index 2d3d32d970..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.unique_with_counts.md
+++ /dev/null
@@ -1,36 +0,0 @@
-### `tf.unique_with_counts(x, name=None)` {#unique_with_counts}
-
-Finds unique elements in a 1-D tensor.
-
-This operation returns a tensor `y` containing all of the unique elements of `x`
-sorted in the same order that they occur in `x`. This operation also returns a
-tensor `idx` the same size as `x` that contains the index of each value of `x`
-in the unique output `y`. Finally, it returns a third tensor `count` that
-contains the count of each element of `y` in `x`. In other words:
-
-`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
-
-For example:
-
-```prettyprint
-# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
-y, idx, count = unique_with_counts(x)
-y ==> [1, 2, 4, 7, 8]
-idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
-count ==> [2, 1, 3, 1, 2]
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. 1-D.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of `Tensor` objects (y, idx, count).
-
-*  <b>`y`</b>: A `Tensor`. Has the same type as `x`. 1-D.
-*  <b>`idx`</b>: A `Tensor` of type `int32`. 1-D.
-*  <b>`count`</b>: A `Tensor` of type `int32`. 1-D.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.variable_axis_size_partitioner.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.variable_axis_size_partitioner.md
new file mode 100644
index 0000000000..5d8822e83c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.variable_axis_size_partitioner.md
@@ -0,0 +1,37 @@
+### `tf.variable_axis_size_partitioner(max_shard_bytes, axis=0, bytes_per_string_element=16, max_shards=None)` {#variable_axis_size_partitioner}
+
+Get a partitioner for VariableScope to keep shards below `max_shard_bytes`.
+
+This partitioner will shard a Variable along one axis, attempting to keep
+the maximum shard size below `max_shard_bytes`.  In practice, this is not
+always possible when sharding along only one axis.  When this happens,
+this axis is sharded as much as possible (i.e., every dimension becomes
+a separate shard).
+
+If the partitioner hits the `max_shards` limit, then each shard may end up
+larger than `max_shard_bytes`. By default `max_shards` equals `None` and no
+limit on the number of shards is enforced.
+
+One reasonable value for `max_shard_bytes` is `(64 << 20) - 1`, or almost
+`64MB`, to keep below the protobuf byte limit.
+
+##### Args:
+
+
+*  <b>`max_shard_bytes`</b>: The maximum size any given shard is allowed to be.
+*  <b>`axis`</b>: The axis to partition along.  Default: outermost axis.
+*  <b>`bytes_per_string_element`</b>: If the `Variable` is of type string, this provides
+    an estimate of how large each scalar in the `Variable` is.
+*  <b>`max_shards`</b>: The maximum number of shards in int created taking precedence
+    over `max_shard_bytes`.
+
+##### Returns:
+
+  A partition function usable as the `partitioner` argument to
+  `variable_scope`, `get_variable`, and `get_partitioned_variable_list`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If any of the byte counts are non-positive.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.variable_scope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.variable_scope.md
deleted file mode 100644
index 86d4684b72..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.variable_scope.md
+++ /dev/null
@@ -1,82 +0,0 @@
-### `tf.variable_scope(name_or_scope, reuse=None, initializer=None, regularizer=None, caching_device=None, partitioner=None)` {#variable_scope}
-
-Returns a context for variable scope.
-
-Variable scope allows to create new variables and to share already created
-ones while providing checks to not create or share by accident. For details,
-see the [Variable Scope How To](../../how_tos/variable_scope/index.md),
-here we present only a few basic examples.
-
-Simple example of how to create a new variable:
-
-```python
-with tf.variable_scope("foo"):
-    with tf.variable_scope("bar"):
-        v = tf.get_variable("v", [1])
-        assert v.name == "foo/bar/v:0"
-```
-
-Basic example of sharing a variable:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-with tf.variable_scope("foo", reuse=True):
-    v1 = tf.get_variable("v", [1])
-assert v1 == v
-```
-
-Sharing a variable by capturing a scope and setting reuse:
-
-```python
-with tf.variable_scope("foo") as scope:
-    v = tf.get_variable("v", [1])
-    scope.reuse_variables()
-    v1 = tf.get_variable("v", [1])
-assert v1 == v
-```
-
-To prevent accidental sharing of variables, we raise an exception when
-getting an existing variable in a non-reusing scope.
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])
-    v1 = tf.get_variable("v", [1])
-    #  Raises ValueError("... v already exists ...").
-```
-
-Similarly, we raise an exception when trying to get a variable that
-does not exist in reuse mode.
-
-```python
-with tf.variable_scope("foo", reuse=True):
-    v = tf.get_variable("v", [1])
-    #  Raises ValueError("... v does not exists ...").
-```
-
-Note that the `reuse` flag is inherited: if we open a reusing scope,
-then all its sub-scopes become reusing as well.
-
-##### Args:
-
-
-*  <b>`name_or_scope`</b>: `string` or `VariableScope`: the scope to open.
-*  <b>`reuse`</b>: `True` or `None`; if `True`, we go into reuse mode for this scope as
-    well as all sub-scopes; if `None`, we just inherit the parent scope reuse.
-*  <b>`initializer`</b>: default initializer for variables within this scope.
-*  <b>`regularizer`</b>: default regularizer for variables within this scope.
-*  <b>`caching_device`</b>: default caching device for variables within this scope.
-*  <b>`partitioner`</b>: default partitioner for variables within this scope.
-
-##### Returns:
-
-  A scope that can be to captured and reused.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: when trying to reuse within a create scope, or create within
-    a reuse scope, or if reuse is not `None` or `True`.
-*  <b>`TypeError`</b>: when the types of some arguments are not appropriate.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.zeros_like.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.zeros_like.md
deleted file mode 100644
index 9017e14287..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.zeros_like.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.zeros_like(tensor, dtype=None, name=None)` {#zeros_like}
-
-Creates a tensor with all elements set to zero.
-
-Given a single tensor (`tensor`), this operation returns a tensor of the
-same type and shape as `tensor` with all elements set to zero. Optionally,
-you can use `dtype` to specify a new type for the returned tensor.
-
-For example:
-
-```python
-# 'tensor' is [[1, 2, 3], [4, 5, 6]]
-tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
-```
-
-##### Args:
-
-
-*  <b>`tensor`</b>: A `Tensor`.
-*  <b>`dtype`</b>: A type for the returned `Tensor`. Must be `float32`, `float64`,
-  `int8`, `int16`, `int32`, `int64`, `uint8`, or `complex64`.
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` with all elements set to zero.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.Assert.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.Assert.md
deleted file mode 100644
index 6471b9aea4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.Assert.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.Assert(condition, data, summarize=None, name=None)` {#Assert}
-
-Asserts that the given condition is true.
-
-If `condition` evaluates to false, print the list of tensors in `data`.
-`summarize` determines how many entries of the tensors to print.
-
-NOTE: To ensure that Assert executes, one usually attaches a dependency:
-
-```python
- # Ensure maximum element of x is smaller or equal to 1
-assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x])
-x = tf.with_dependencies([assert_op], x)
-```
-
-##### Args:
-
-
-*  <b>`condition`</b>: The condition to evaluate.
-*  <b>`data`</b>: The tensors to print out when condition is false.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-
-*  <b>`assert_op`</b>: An `Operation` that, when executed, raises a
-  `tf.errors.InvalidArgumentError` if `condition` is not true.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.QueueBase.from_list.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.QueueBase.from_list.md
deleted file mode 100644
index d9a2e7c71f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.QueueBase.from_list.md
+++ /dev/null
@@ -1,21 +0,0 @@
-#### `tf.QueueBase.from_list(index, queues)` {#QueueBase.from_list}
-
-Create a queue using the queue reference from `queues[index]`.
-
-##### Args:
-
-
-*  <b>`index`</b>: An integer scalar tensor that determines the input that gets
-    selected.
-*  <b>`queues`</b>: A list of `QueueBase` objects.
-
-##### Returns:
-
-  A `QueueBase` object.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: When `queues` is not a list of `QueueBase` objects,
-    or when the data types of `queues` are not all the same.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.QueueBase.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.QueueBase.md
new file mode 100644
index 0000000000..35373f6edc
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.QueueBase.md
@@ -0,0 +1,268 @@
+Base class for queue implementations.
+
+A queue is a TensorFlow data structure that stores tensors across
+multiple steps, and exposes operations that enqueue and dequeue
+tensors.
+
+Each queue element is a tuple of one or more tensors, where each
+tuple component has a static dtype, and may have a static shape. The
+queue implementations support versions of enqueue and dequeue that
+handle single elements, versions that support enqueuing and
+dequeuing a batch of elements at once.
+
+See [`tf.FIFOQueue`](#FIFOQueue) and
+[`tf.RandomShuffleQueue`](#RandomShuffleQueue) for concrete
+implementations of this class, and instructions on how to create
+them.
+
+- - -
+
+#### `tf.QueueBase.enqueue(vals, name=None)` {#QueueBase.enqueue}
+
+Enqueues one element to this queue.
+
+If the queue is full when this operation executes, it will block
+until the element has been enqueued.
+
+##### Args:
+
+
+*  <b>`vals`</b>: A tensor, a list or tuple of tensors, or a dictionary containing
+    the values to enqueue.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The operation that enqueues a new tuple of tensors to the queue.
+
+
+- - -
+
+#### `tf.QueueBase.enqueue_many(vals, name=None)` {#QueueBase.enqueue_many}
+
+Enqueues zero or more elements to this queue.
+
+This operation slices each component tensor along the 0th dimension to
+make multiple queue elements. All of the tensors in `vals` must have the
+same size in the 0th dimension.
+
+If the queue is full when this operation executes, it will block
+until all of the elements have been enqueued.
+
+##### Args:
+
+
+*  <b>`vals`</b>: A tensor, a list or tuple of tensors, or a dictionary
+    from which the queue elements are taken.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The operation that enqueues a batch of tuples of tensors to the queue.
+
+
+
+- - -
+
+#### `tf.QueueBase.dequeue(name=None)` {#QueueBase.dequeue}
+
+Dequeues one element from this queue.
+
+If the queue is empty when this operation executes, it will block
+until there is an element to dequeue.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The tuple of tensors that was dequeued.
+
+
+- - -
+
+#### `tf.QueueBase.dequeue_many(n, name=None)` {#QueueBase.dequeue_many}
+
+Dequeues and concatenates `n` elements from this queue.
+
+This operation concatenates queue-element component tensors along
+the 0th dimension to make a single component tensor.  All of the
+components in the dequeued tuple will have size `n` in the 0th dimension.
+
+If the queue is closed and there are less than `n` elements left, then an
+`OutOfRange` exception is raised.
+
+##### Args:
+
+
+*  <b>`n`</b>: A scalar `Tensor` containing the number of elements to dequeue.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The tuple of concatenated tensors that was dequeued.
+
+
+
+- - -
+
+#### `tf.QueueBase.size(name=None)` {#QueueBase.size}
+
+Compute the number of elements in this queue.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A scalar tensor containing the number of elements in this queue.
+
+
+
+- - -
+
+#### `tf.QueueBase.close(cancel_pending_enqueues=False, name=None)` {#QueueBase.close}
+
+Closes this queue.
+
+This operation signals that no more elements will be enqueued in
+the given queue. Subsequent `enqueue` and `enqueue_many`
+operations will fail. Subsequent `dequeue` and `dequeue_many`
+operations will continue to succeed if sufficient elements remain
+in the queue. Subsequent `dequeue` and `dequeue_many` operations
+that would block will fail immediately.
+
+If `cancel_pending_enqueues` is `True`, all pending requests will also
+be cancelled.
+
+##### Args:
+
+
+*  <b>`cancel_pending_enqueues`</b>: (Optional.) A boolean, defaulting to
+    `False` (described above).
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The operation that closes the queue.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.QueueBase.__init__(dtypes, shapes, names, queue_ref)` {#QueueBase.__init__}
+
+Constructs a queue object from a queue reference.
+
+The two optional lists, `shapes` and `names`, must be of the same length
+as `dtypes` if provided.  The values at a given index `i` indicate the
+shape and name to use for the corresponding queue component in `dtypes`.
+
+##### Args:
+
+
+*  <b>`dtypes`</b>: A list of types.  The length of dtypes must equal the number
+    of tensors in each element.
+*  <b>`shapes`</b>: Constraints on the shapes of tensors in an element:
+    A list of shape tuples or None. This list is the same length
+    as dtypes.  If the shape of any tensors in the element are constrained,
+    all must be; shapes can be None if the shapes should not be constrained.
+*  <b>`names`</b>: Optional list of names.  If provided, the `enqueue()` and
+    `dequeue()` methods will use dictionaries with these names as keys.
+    Must be None or a list or tuple of the same length as `dtypes`.
+*  <b>`queue_ref`</b>: The queue reference, i.e. the output of the queue op.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If one of the arguments is invalid.
+
+
+- - -
+
+#### `tf.QueueBase.dequeue_up_to(n, name=None)` {#QueueBase.dequeue_up_to}
+
+Dequeues and concatenates `n` elements from this queue.
+
+**Note** This operation is not supported by all queues.  If a queue does not
+support DequeueUpTo, then an Unimplemented exception is raised.
+
+This operation concatenates queue-element component tensors along the
+0th dimension to make a single component tensor.  All of the components
+in the dequeued tuple will have size `n` in the 0th dimension.
+
+If the queue is closed and there are more than `0` but less than `n`
+elements remaining, then instead of raising an `OutOfRange` exception like
+`dequeue_many`, the remaining elements are returned immediately.
+If the queue is closed and there are `0` elements left in the queue, then
+an `OutOfRange` exception is raised just like in `dequeue_many`.
+Otherwise the behavior is identical to `dequeue_many`:
+
+##### Args:
+
+
+*  <b>`n`</b>: A scalar `Tensor` containing the number of elements to dequeue.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The tuple of concatenated tensors that was dequeued.
+
+
+- - -
+
+#### `tf.QueueBase.dtypes` {#QueueBase.dtypes}
+
+The list of dtypes for each component of a queue element.
+
+
+- - -
+
+#### `tf.QueueBase.from_list(index, queues)` {#QueueBase.from_list}
+
+Create a queue using the queue reference from `queues[index]`.
+
+##### Args:
+
+
+*  <b>`index`</b>: An integer scalar tensor that determines the input that gets
+    selected.
+*  <b>`queues`</b>: A list of `QueueBase` objects.
+
+##### Returns:
+
+  A `QueueBase` object.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: When `queues` is not a list of `QueueBase` objects,
+    or when the data types of `queues` are not all the same.
+
+
+- - -
+
+#### `tf.QueueBase.name` {#QueueBase.name}
+
+The name of the underlying queue.
+
+
+- - -
+
+#### `tf.QueueBase.names` {#QueueBase.names}
+
+The list of names for each component of a queue element.
+
+
+- - -
+
+#### `tf.QueueBase.queue_ref` {#QueueBase.queue_ref}
+
+The underlying queue reference.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.RandomShuffleQueue.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.RandomShuffleQueue.md
new file mode 100644
index 0000000000..cd617e7578
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.RandomShuffleQueue.md
@@ -0,0 +1,54 @@
+A queue implementation that dequeues elements in a random order.
+
+See [`tf.QueueBase`](#QueueBase) for a description of the methods on
+this class.
+
+- - -
+
+#### `tf.RandomShuffleQueue.__init__(capacity, min_after_dequeue, dtypes, shapes=None, names=None, seed=None, shared_name=None, name='random_shuffle_queue')` {#RandomShuffleQueue.__init__}
+
+Create a queue that dequeues elements in a random order.
+
+A `RandomShuffleQueue` has bounded capacity; supports multiple
+concurrent producers and consumers; and provides exactly-once
+delivery.
+
+A `RandomShuffleQueue` holds a list of up to `capacity`
+elements. Each element is a fixed-length tuple of tensors whose
+dtypes are described by `dtypes`, and whose shapes are optionally
+described by the `shapes` argument.
+
+If the `shapes` argument is specified, each component of a queue
+element must have the respective fixed shape. If it is
+unspecified, different queue elements may have different shapes,
+but the use of `dequeue_many` is disallowed.
+
+The `min_after_dequeue` argument allows the caller to specify a
+minimum number of elements that will remain in the queue after a
+`dequeue` or `dequeue_many` operation completes, to ensure a
+minimum level of mixing of elements. This invariant is maintained
+by blocking those operations until sufficient elements have been
+enqueued. The `min_after_dequeue` argument is ignored after the
+queue has been closed.
+
+##### Args:
+
+
+*  <b>`capacity`</b>: An integer. The upper bound on the number of elements
+    that may be stored in this queue.
+*  <b>`min_after_dequeue`</b>: An integer (described above).
+*  <b>`dtypes`</b>: A list of `DType` objects. The length of `dtypes` must equal
+    the number of tensors in each queue element.
+*  <b>`shapes`</b>: (Optional.) A list of fully-defined `TensorShape` objects
+    with the same length as `dtypes`, or `None`.
+*  <b>`names`</b>: (Optional.) A list of string naming the components in the queue
+    with the same length as `dtypes`, or `None`.  If specified the dequeue
+    methods return a dictionary with the names as keys.
+*  <b>`seed`</b>: A Python integer. Used to create a random seed. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`shared_name`</b>: (Optional.) If non-empty, this queue will be shared under
+    the given name across multiple sessions.
+*  <b>`name`</b>: Optional name for the queue operation.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.TextLineReader.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.TextLineReader.md
deleted file mode 100644
index ebb023a2fa..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.TextLineReader.md
+++ /dev/null
@@ -1,148 +0,0 @@
-A Reader that outputs the lines of a file delimited by newlines.
-
-Newlines are stripped from the output.
-See ReaderBase for supported methods.
-- - -
-
-#### `tf.TextLineReader.__init__(skip_header_lines=None, name=None)` {#TextLineReader.__init__}
-
-Create a TextLineReader.
-
-##### Args:
-
-
-*  <b>`skip_header_lines`</b>: An optional int. Defaults to 0.  Number of lines
-    to skip from the beginning of every file.
-*  <b>`name`</b>: A name for the operation (optional).
-
-
-- - -
-
-#### `tf.TextLineReader.num_records_produced(name=None)` {#TextLineReader.num_records_produced}
-
-Returns the number of records this reader has produced.
-
-This is the same as the number of Read executions that have
-succeeded.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.TextLineReader.num_work_units_completed(name=None)` {#TextLineReader.num_work_units_completed}
-
-Returns the number of work units this reader has finished processing.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.TextLineReader.read(queue, name=None)` {#TextLineReader.read}
-
-Returns the next record (key, value pair) produced by a reader.
-
-Will dequeue a work unit from queue if necessary (e.g. when the
-Reader needs to start reading from a new file since it has
-finished with the previous file).
-
-##### Args:
-
-
-*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
-    to a Queue, with string work items.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of Tensors (key, value).
-
-*  <b>`key`</b>: A string scalar Tensor.
-*  <b>`value`</b>: A string scalar Tensor.
-
-
-- - -
-
-#### `tf.TextLineReader.reader_ref` {#TextLineReader.reader_ref}
-
-Op that implements the reader.
-
-
-- - -
-
-#### `tf.TextLineReader.reset(name=None)` {#TextLineReader.reset}
-
-Restore a reader to its initial clean state.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.TextLineReader.restore_state(state, name=None)` {#TextLineReader.restore_state}
-
-Restore a reader to a previously saved state.
-
-Not all Readers support being restored, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`state`</b>: A string Tensor.
-    Result of a SerializeState of a Reader with matching type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.TextLineReader.serialize_state(name=None)` {#TextLineReader.serialize_state}
-
-Produce a string tensor that encodes the state of a reader.
-
-Not all Readers support being serialized, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A string Tensor.
-
-
-- - -
-
-#### `tf.TextLineReader.supports_serialize` {#TextLineReader.supports_serialize}
-
-Whether the Reader implementation can serialize its state.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.VariableScope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.VariableScope.md
new file mode 100644
index 0000000000..04fdca9bdf
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.VariableScope.md
@@ -0,0 +1,105 @@
+Variable scope object to carry defaults to provide to get_variable.
+
+Many of the arguments we need for get_variable in a variable store are most
+easily handled with a context. This object is used for the defaults.
+
+Attributes:
+  name: name of the current scope, used as prefix in get_variable.
+  initializer: default initializer passed to get_variable.
+  regularizer: default regularizer passed to get_variable.
+  reuse: Boolean or None, setting the reuse in get_variable.
+  caching_device: string, callable, or None: the caching device passed to
+    get_variable.
+  partitioner: callable or `None`: the partitioner passed to `get_variable`.
+  name_scope: The name passed to `tf.name_scope`.
+- - -
+
+#### `tf.VariableScope.__init__(reuse, name='', initializer=None, regularizer=None, caching_device=None, partitioner=None, name_scope='')` {#VariableScope.__init__}
+
+Creates a new VariableScope with the given properties.
+
+
+- - -
+
+#### `tf.VariableScope.caching_device` {#VariableScope.caching_device}
+
+
+
+
+- - -
+
+#### `tf.VariableScope.get_variable(var_store, name, shape=None, dtype=tf.float32, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True)` {#VariableScope.get_variable}
+
+Gets an existing variable with this name or create a new one.
+
+
+- - -
+
+#### `tf.VariableScope.initializer` {#VariableScope.initializer}
+
+
+
+
+- - -
+
+#### `tf.VariableScope.name` {#VariableScope.name}
+
+
+
+
+- - -
+
+#### `tf.VariableScope.partitioner` {#VariableScope.partitioner}
+
+
+
+
+- - -
+
+#### `tf.VariableScope.regularizer` {#VariableScope.regularizer}
+
+
+
+
+- - -
+
+#### `tf.VariableScope.reuse` {#VariableScope.reuse}
+
+
+
+
+- - -
+
+#### `tf.VariableScope.reuse_variables()` {#VariableScope.reuse_variables}
+
+Reuse variables in this scope.
+
+
+- - -
+
+#### `tf.VariableScope.set_caching_device(caching_device)` {#VariableScope.set_caching_device}
+
+Set caching_device for this scope.
+
+
+- - -
+
+#### `tf.VariableScope.set_initializer(initializer)` {#VariableScope.set_initializer}
+
+Set initializer for this scope.
+
+
+- - -
+
+#### `tf.VariableScope.set_partitioner(partitioner)` {#VariableScope.set_partitioner}
+
+Set partitioner for this scope.
+
+
+- - -
+
+#### `tf.VariableScope.set_regularizer(regularizer)` {#VariableScope.set_regularizer}
+
+Set regularizer for this scope.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.assert_negative.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.assert_negative.md
new file mode 100644
index 0000000000..81daebec0d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.assert_negative.md
@@ -0,0 +1,33 @@
+### `tf.assert_negative(x, data=None, summarize=None, name=None)` {#assert_negative}
+
+Assert the condition `x < 0` holds element-wise.
+
+Example of adding a dependency to an operation:
+
+```python
+with tf.control_dependencies([tf.assert_negative(x)]):
+  output = tf.reduce_sum(x)
+```
+
+Example of adding dependency to the tensor being checked:
+
+```python
+x = tf.with_dependencies([tf.assert_negative(x)], x)
+```
+
+Negative means, for every element `x[i]` of `x`, we have `x[i] < 0`.
+If `x` is empty this is trivially satisfied.
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
+    error message and first few entries of `x`.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_negative".
+
+##### Returns:
+
+  Op raising `InvalidArgumentError` unless `x` is all negative.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.assert_rank.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.assert_rank.md
deleted file mode 100644
index e8da009641..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.assert_rank.md
+++ /dev/null
@@ -1,36 +0,0 @@
-### `tf.assert_rank(x, rank, data=None, summarize=None, name=None)` {#assert_rank}
-
-Assert `x` has rank equal to `rank`.
-
-Example of adding a dependency to an operation:
-
-```python
-with tf.control_dependencies([tf.assert_rank(x, 2)]):
-  output = tf.reduce_sum(x)
-```
-
-Example of adding dependency to the tensor being checked:
-
-```python
-x = tf.with_dependencies([tf.assert_rank(x, 2)], x)
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`rank`</b>: Scalar integer `Tensor`.
-*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
-    error message and first few entries of `x`.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_rank".
-
-##### Returns:
-
-  Op raising `InvalidArgumentError` unless `x` has specified rank.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If static checks determine `x` has wrong rank.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_determinant.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_determinant.md
deleted file mode 100644
index 83f9503a4d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_determinant.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.batch_matrix_determinant(input, name=None)` {#batch_matrix_determinant}
-
-Calculates the determinants for a batch of square matrices.
-
-The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-form square matrices. The output is a 1-D tensor containing the determinants
-for all input submatrices `[..., :, :]`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-    Shape is `[..., M, M]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`. Shape is `[...]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_diag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_diag.md
new file mode 100644
index 0000000000..6e5458ba6c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_diag.md
@@ -0,0 +1,42 @@
+### `tf.batch_matrix_diag(diagonal, name=None)` {#batch_matrix_diag}
+
+Returns a batched diagonal tensor with a given batched diagonal values.
+
+Given a `diagonal`, this operation returns a tensor with the `diagonal` and
+everything else padded with zeros. The diagonal is computed as follows:
+
+Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a
+tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where:
+
+`output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.
+
+For example:
+
+```prettyprint
+# 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]
+
+and diagonal.shape = (2, 4)
+
+tf.batch_matrix_diag(diagonal) ==> [[[1, 0, 0, 0]
+                                     [0, 2, 0, 0]
+                                     [0, 0, 3, 0]
+                                     [0, 0, 0, 4]],
+                                    [[5, 0, 0, 0]
+                                     [0, 6, 0, 0]
+                                     [0, 0, 7, 0]
+                                     [0, 0, 0, 8]]]
+
+which has shape (2, 4, 4)
+```
+
+##### Args:
+
+
+*  <b>`diagonal`</b>: A `Tensor`. Rank `k`, where `k >= 1`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `diagonal`.
+  Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_solve.md
deleted file mode 100644
index f75ea79bc5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_solve.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.batch_matrix_solve(matrix, rhs, adjoint=None, name=None)` {#batch_matrix_solve}
-
-Solves systems of linear equations. Checks for invertibility.
-
-Matrix is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-form square matrices. Rhs is a tensor of shape
-`[..., M, K]`. The output is a tensor shape `[..., M, K]`.  If `adjoint` is `False` then each output
-matrix satisfies `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
-If `adjoint` is `True` then each output
-matrix satisfies `adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]`.
-
-##### Args:
-
-
-*  <b>`matrix`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
-    Shape is `[..., M, M]`.
-*  <b>`rhs`</b>: A `Tensor`. Must have the same type as `matrix`.
-    Shape is `[..., M, K]`.
-*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
-    Boolean indicating whether to solve with `matrix` or its (block-wise)
-    adjoint.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `matrix`. Shape is `[..., M, K]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_solve_ls.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_solve_ls.md
new file mode 100644
index 0000000000..2b33669fa2
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_matrix_solve_ls.md
@@ -0,0 +1,56 @@
+### `tf.batch_matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None)` {#batch_matrix_solve_ls}
+
+Solves multiple linear least-squares problems.
+
+`matrix` is a tensor of shape `[..., M, N]` whose inner-most 2 dimensions
+form `M`-by-`N` matrices. Rhs is a tensor of shape `[..., M, K]` whose
+inner-most 2 dimensions form `M`-by-`K` matrices.   The computed output is a
+`Tensor` of shape `[..., N, K]` whose inner-most 2 dimensions form `M`-by-`K`
+matrices that solve the equations
+`matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]` in the least squares
+sense.
+
+Below we will use the following notation for each pair of
+matrix and right-hand sides in the batch:
+
+`matrix`=\\(A \in \Re^{m \times n}\\),
+`rhs`=\\(B  \in \Re^{m \times k}\\),
+`output`=\\(X  \in \Re^{n \times k}\\),
+`l2_regularizer`=\\(\lambda\\).
+
+If `fast` is `True`, then the solution is computed by solving the normal
+equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then
+\\(X = (A^T A + \lambda I)^{-1} A^T B\\), which solves the least-squares
+problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k}} ||A Z - B||_F^2 +
+\lambda ||Z||_F^2\\). If \\(m \lt n\\) then `output` is computed as
+\\(X = A^T (A A^T + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is
+the minimum-norm solution to the under-determined linear system, i.e.
+\\(X = \mathrm{argmin}_{Z \in \Re^{n \times k}} ||Z||_F^2 \\), subject to
+\\(A Z = B\\). Notice that the fast path is only numerically stable when
+\\(A\\) is numerically full rank and has a condition number
+\\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach}}}\\) or\\(\lambda\\)
+is sufficiently large.
+
+If `fast` is `False` an algorithm based on the numerically robust complete
+orthogonal decomposition is used. This computes the minimum-norm
+least-squares solution, even when \\(A\\) is rank deficient. This path is
+typically 6-7 times slower than the fast path. If `fast` is `False` then
+`l2_regularizer` is ignored.
+
+##### Args:
+
+
+*  <b>`matrix`</b>: `Tensor` of shape `[..., M, N]`.
+*  <b>`rhs`</b>: `Tensor` of shape `[..., M, K]`.
+*  <b>`l2_regularizer`</b>: 0-D `double` `Tensor`. Ignored if `fast=False`.
+*  <b>`fast`</b>: bool. Defaults to `True`.
+*  <b>`name`</b>: string, optional name of the operation.
+
+##### Returns:
+
+
+*  <b>`output`</b>: `Tensor` of shape `[..., N, K]` whose inner-most 2 dimensions form
+    `M`-by-`K` matrices that solve the equations
+    `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]` in the least
+    squares sense.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_self_adjoint_eig.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_self_adjoint_eig.md
new file mode 100644
index 0000000000..19d6c5319f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.batch_self_adjoint_eig.md
@@ -0,0 +1,22 @@
+### `tf.batch_self_adjoint_eig(input, name=None)` {#batch_self_adjoint_eig}
+
+Calculates the Eigen Decomposition of a batch of square self-adjoint matrices.
+
+The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+form square matrices, with the same constraints as the single matrix
+SelfAdjointEig.
+
+The result is a '[..., M+1, M] matrix with [..., 0,:] containing the
+eigenvalues, and subsequent [...,1:, :] containing the eigenvectors.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
+    Shape is `[..., M, M]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`. Shape is `[..., M+1, M]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.cholesky_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.cholesky_solve.md
deleted file mode 100644
index 7445d3f929..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.cholesky_solve.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.cholesky_solve(chol, rhs, name=None)` {#cholesky_solve}
-
-Solve linear equations `A X = RHS`, given Cholesky factorization of `A`.
-
-```python
-# Solve one system of linear equations (K = 1).
-A = [[3, 1], [1, 3]]
-RHS = [[2], [22]]  # shape 2 x 1
-chol = tf.cholesky(A)
-X = tf.cholesky_solve(chol, RHS)
-# tf.matmul(A, X) ~ RHS
-X[:, 0]  # Solution to the linear system A x = RHS[:, 0]
-
-# Solve five systems of linear equations (K = 5).
-A = [[3, 1], [1, 3]]
-RHS = [[1, 2, 3, 4, 5], [11, 22, 33, 44, 55]]  # shape 2 x 5
-...
-X[:, 2]  # Solution to the linear system A x = RHS[:, 2]
-```
-
-##### Args:
-
-
-*  <b>`chol`</b>: A `Tensor`.  Must be `float32` or `float64`, shape is `[M, M]`.
-    Cholesky factorization of `A`, e.g. `chol = tf.cholesky(A)`.  For that
-    reason, only the lower triangular part (including the diagonal) of `chol`
-    is used.  The strictly upper part is assumed to be zero and not accessed.
-*  <b>`rhs`</b>: A `Tensor`, same type as `chol`, shape is `[M, K]`, designating `K`
-    systems of linear equations.
-*  <b>`name`</b>: A name to give this `Op`.  Defaults to `cholesky_solve`.
-
-##### Returns:
-
-  Solution to `A X = RHS`, shape `[M, K]`.  The solutions to the `K` systems.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.complex_abs.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.complex_abs.md
deleted file mode 100644
index 1cb76668d6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.complex_abs.md
+++ /dev/null
@@ -1,26 +0,0 @@
-### `tf.complex_abs(x, name=None)` {#complex_abs}
-
-Computes the complex absolute value of a tensor.
-
-Given a tensor `x` of complex numbers, this operation returns a tensor of type
-`float` or `double` that is the absolute value of each element in `x`. All
-elements in `x` must be complex numbers of the form \\(a + bj\\). The
-absolute value is computed as \\( \sqrt{a^2 + b^2}\\).
-
-For example:
-
-```
-# tensor 'x' is [[-2.25 + 4.75j], [-3.25 + 5.75j]]
-tf.complex_abs(x) ==> [5.25594902, 6.60492229]
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` of type `complex64` or `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `float32` or `float64`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.constant.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.constant.md
deleted file mode 100644
index ff34b6eeb1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.constant.md
+++ /dev/null
@@ -1,50 +0,0 @@
-### `tf.constant(value, dtype=None, shape=None, name='Const')` {#constant}
-
-Creates a constant tensor.
-
- The resulting tensor is populated with values of type `dtype`, as
- specified by arguments `value` and (optionally) `shape` (see examples
- below).
-
- The argument `value` can be a constant value, or a list of values of type
- `dtype`. If `value` is a list, then the length of the list must be less
- than or equal to the number of elements implied by the `shape` argument (if
- specified). In the case where the list length is less than the number of
- elements specified by `shape`, the last element in the list will be used
- to fill the remaining entries.
-
- The argument `shape` is optional. If present, it specifies the dimensions of
- the resulting tensor. If not present, the shape of `value` is used.
-
- If the argument `dtype` is not specified, then the type is inferred from
- the type of `value`.
-
- For example:
-
- ```python
- # Constant 1-D Tensor populated with value list.
- tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]
-
- # Constant 2-D tensor populated with scalar value -1.
- tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.]
-                                              [-1. -1. -1.]]
- ```
-
-##### Args:
-
-
-*  <b>`value`</b>: A constant value (or list) of output type `dtype`.
-
-
-*  <b>`dtype`</b>: The type of the elements of the resulting tensor.
-
-
-*  <b>`shape`</b>: Optional dimensions of resulting tensor.
-
-
-*  <b>`name`</b>: Optional name for the tensor.
-
-##### Returns:
-
-  A Constant Tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.constant_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.constant_initializer.md
new file mode 100644
index 0000000000..4ac524d708
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.constant_initializer.md
@@ -0,0 +1,20 @@
+### `tf.constant_initializer(value=0.0, dtype=tf.float32)` {#constant_initializer}
+
+Returns an initializer that generates tensors with a single value.
+
+##### Args:
+
+
+*  <b>`value`</b>: A Python scalar. All elements of the initialized variable
+    will be set to this value.
+*  <b>`dtype`</b>: The data type. Only floating point types are supported.
+
+##### Returns:
+
+  An initializer that generates tensors with a single value.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.Exponential.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.Exponential.md
deleted file mode 100644
index 62181034b9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.Exponential.md
+++ /dev/null
@@ -1,260 +0,0 @@
-The Exponential distribution with rate parameter lam.
-
-The PDF of this distribution is:
-
-```pdf(x) = (lam * e^(-lam * x)), x > 0```
-
-Note that the Exponential distribution is a special case of the Gamma
-distribution, with Exponential(lam) = Gamma(1, lam).
-- - -
-
-#### `tf.contrib.distributions.Exponential.__init__(lam, name='Exponential')` {#Exponential.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.alpha` {#Exponential.alpha}
-
-Shape parameter.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.batch_shape(name='batch_shape')` {#Exponential.batch_shape}
-
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-The product of the dimensions of the `batch_shape` is the number of
-independent distributions of this kind the instance represents.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `batch_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.beta` {#Exponential.beta}
-
-Inverse scale parameter.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.cdf(x, name='cdf')` {#Exponential.cdf}
-
-CDF of observations `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.dtype` {#Exponential.dtype}
-
-dtype of samples from this distribution.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.entropy(name='entropy')` {#Exponential.entropy}
-
-The entropy of Gamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.event_shape(name='event_shape')` {#Exponential.event_shape}
-
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `event_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.get_batch_shape()` {#Exponential.get_batch_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `batch_shape`. May be only partially defined.
-
-##### Returns:
-
-  `TensorShape` object.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.get_event_shape()` {#Exponential.get_event_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `event_shape`. May be only partially defined.
-
-##### Returns:
-
-  `TensorShape` object.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.is_reparameterized` {#Exponential.is_reparameterized}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.lam` {#Exponential.lam}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.log_cdf(x, name='log_cdf')` {#Exponential.log_cdf}
-
-Log CDF of observations `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.log_pdf(x, name='log_pdf')` {#Exponential.log_pdf}
-
-Log pdf of observations in `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.mean` {#Exponential.mean}
-
-Mean of each batch member.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.name` {#Exponential.name}
-
-Name to prepend to all ops.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.pdf(x, name='pdf')` {#Exponential.pdf}
-
-Pdf of observations in `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`pdf`</b>: tensor of dtype `dtype`, the PDFs of `x`
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.sample(n, seed=None, name=None)` {#Exponential.sample}
-
-Sample `n` observations from the Exponential Distributions.
-
-##### Args:
-
-
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by the hyperparameters.
-
-
-- - -
-
-#### `tf.contrib.distributions.Exponential.variance` {#Exponential.variance}
-
-Variance of each batch member.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormal.md
deleted file mode 100644
index 258cb03ea8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormal.md
+++ /dev/null
@@ -1,218 +0,0 @@
-The Multivariate Normal distribution on `R^k`.
-
-The distribution has mean and covariance parameters mu (1-D), sigma (2-D),
-or alternatively mean `mu` and factored covariance (cholesky decomposed
-`sigma`) called `sigma_chol`.
-
-#### Mathematical details
-
-The PDF of this distribution is:
-
-```
-f(x) = (2*pi)^(-k/2) |det(sigma)|^(-1/2) exp(-1/2*(x-mu)^*.sigma^{-1}.(x-mu))
-```
-
-where `.` denotes the inner product on `R^k` and `^*` denotes transpose.
-
-Alternatively, if `sigma` is positive definite, it can be represented in terms
-of its lower triangular cholesky factorization
-
-```sigma = sigma_chol . sigma_chol^*```
-
-and the pdf above allows simpler computation:
-
-```
-|det(sigma)| = reduce_prod(diag(sigma_chol))^2
-x_whitened = sigma^{-1/2} . (x - mu) = tri_solve(sigma_chol, x - mu)
-(x-mu)^* .sigma^{-1} . (x-mu) = x_whitened^* . x_whitened
-```
-
-where `tri_solve()` solves a triangular system of equations.
-
-#### Examples
-
-A single multi-variate Gaussian distribution is defined by a vector of means
-of length `k`, and a covariance matrix of shape `k x k`.
-
-Extra leading dimensions, if provided, allow for batches.
-
-```python
-# Initialize a single 3-variate Gaussian with diagonal covariance.
-mu = [1, 2, 3]
-sigma = [[1, 0, 0], [0, 3, 0], [0, 0, 2]]
-dist = tf.contrib.distributions.MultivariateNormal(mu=mu, sigma=sigma)
-
-# Evaluate this on an observation in R^3, returning a scalar.
-dist.pdf([-1, 0, 1])
-
-# Initialize a batch of two 3-variate Gaussians.
-mu = [[1, 2, 3], [11, 22, 33]]
-sigma = ...  # shape 2 x 3 x 3
-dist = tf.contrib.distributions.MultivariateNormal(mu=mu, sigma=sigma)
-
-# Evaluate this on a two observations, each in R^3, returning a length two
-# tensor.
-x = [[-1, 0, 1], [-11, 0, 11]]  # Shape 2 x 3.
-dist.pdf(x)
-```
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.__init__(mu, sigma=None, sigma_chol=None, name=None)` {#MultivariateNormal.__init__}
-
-Multivariate Normal distributions on `R^k`.
-
-User must provide means `mu`, which are tensors of rank `N+1` (`N >= 0`)
-with the last dimension having length `k`.
-
-User must provide exactly one of `sigma` (the covariance matrices) or
-`sigma_chol` (the cholesky decompositions of the covariance matrices).
-`sigma` or `sigma_chol` must be of rank `N+2`.  The last two dimensions
-must both have length `k`.  The first `N` dimensions correspond to batch
-indices.
-
-If `sigma_chol` is not provided, the batch cholesky factorization of `sigma`
-is calculated for you.
-
-The shapes of `mu` and `sigma` must match for the first `N` dimensions.
-
-Regardless of which parameter is provided, the covariance matrices must all
-be **positive definite** (an error is raised if one of them is not).
-
-##### Args:
-
-
-*  <b>`mu`</b>: (N+1)-D.  `float` or `double` tensor, the means of the distributions.
-*  <b>`sigma`</b>: (N+2)-D.  (optional) `float` or `double` tensor, the covariances
-    of the distribution(s).  The first `N+1` dimensions must match
-    those of `mu`.  Must be batch-positive-definite.
-*  <b>`sigma_chol`</b>: (N+2)-D.  (optional) `float` or `double` tensor, a
-    lower-triangular factorization of `sigma`
-    (`sigma = sigma_chol . sigma_chol^*`).  The first `N+1` dimensions
-    must match those of `mu`.  The tensor itself need not be batch
-    lower triangular: we ignore the upper triangular part.  However,
-    the batch diagonals must be positive (i.e., sigma_chol must be
-    batch-positive-definite).
-*  <b>`name`</b>: The name to give Ops created by the initializer.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if neither sigma nor sigma_chol is provided.
-*  <b>`TypeError`</b>: if mu and sigma (resp. sigma_chol) are different dtypes.
-
-
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.dtype` {#MultivariateNormal.dtype}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.entropy(name=None)` {#MultivariateNormal.entropy}
-
-The entropies of these Multivariate Normals.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropies.
-
-
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.is_reparameterized` {#MultivariateNormal.is_reparameterized}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.log_pdf(x, name=None)` {#MultivariateNormal.log_pdf}
-
-Log pdf of observations `x` given these Multivariate Normals.
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.mean` {#MultivariateNormal.mean}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.mu` {#MultivariateNormal.mu}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.pdf(x, name=None)` {#MultivariateNormal.pdf}
-
-The PDF of observations `x` under these Multivariate Normals.
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.sample(n, seed=None, name=None)` {#MultivariateNormal.sample}
-
-Sample `n` observations from the Multivariate Normal Distributions.
-
-##### Args:
-
-
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
-
-
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.sigma` {#MultivariateNormal.sigma}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.MultivariateNormal.sigma_det` {#MultivariateNormal.sigma_det}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.layers.summarize_collection.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.layers.summarize_collection.md
deleted file mode 100644
index b1b5f56056..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.layers.summarize_collection.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.contrib.layers.summarize_collection(collection, name_filter=None, summarizer=summarize_tensor)` {#summarize_collection}
-
-Summarize a graph collection of tensors, possibly filtered by name.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.layers.summarize_tensors.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.layers.summarize_tensors.md
new file mode 100644
index 0000000000..608999b437
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.layers.summarize_tensors.md
@@ -0,0 +1,4 @@
+### `tf.contrib.layers.summarize_tensors(tensors, summarizer=summarize_tensor)` {#summarize_tensors}
+
+Summarize a set of tensors.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.layers.variance_scaling_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.layers.variance_scaling_initializer.md
deleted file mode 100644
index c82f924432..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.layers.variance_scaling_initializer.md
+++ /dev/null
@@ -1,47 +0,0 @@
-### `tf.contrib.layers.variance_scaling_initializer(factor=2.0, mode='FAN_IN', uniform=False, seed=None, dtype=tf.float32)` {#variance_scaling_initializer}
-
-Returns an initializer that generates tensors without scaling variance.
-
-When initializing a deep network, it is in principle advantageous to keep
-the scale of the input variance constant, so it does not explode or diminish
-by reaching the final layer. This initializer use the following formula:
-  if mode='FAN_IN': # Count only number of input connections.
-    n = fan_in
-  elif mode='FAN_OUT': # Count only number of output connections.
-    n = fan_out
-  elif mode='FAN_AVG': # Average number of inputs and output connections.
-    n = (fan_in + fan_out)/2.0
-
-    truncated_normal(shape, 0.0, stddev=sqrt(factor / n))
-
-To get http://arxiv.org/pdf/1502.01852v1.pdf use (Default):
-  - factor=2.0 mode='FAN_IN' uniform=False
-To get http://arxiv.org/abs/1408.5093 use:
-  - factor=1.0 mode='FAN_IN' uniform=True
-To get http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf use:
-  - factor=1.0 mode='FAN_AVG' uniform=True.
-To get xavier_initializer use either:
-  - factor=1.0 mode='FAN_AVG' uniform=True.
-  - factor=1.0 mode='FAN_AVG' uniform=False.
-
-##### Args:
-
-
-*  <b>`factor`</b>: Float.  A multiplicative factor.
-*  <b>`mode`</b>: String.  'FAN_IN', 'FAN_OUT', 'FAN_AVG'.
-*  <b>`uniform`</b>: Whether to use uniform or normal distributed random initialization.
-*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`dtype`</b>: The data type. Only floating point types are supported.
-
-##### Returns:
-
-  An initializer that generates tensors with unit variance.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
-*  <b>`TypeError`</b>: if `mode` is not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG'].
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.Estimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.Estimator.md
deleted file mode 100644
index 00f12fa0a1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.Estimator.md
+++ /dev/null
@@ -1,215 +0,0 @@
-Estimator class is the basic TensorFlow model trainer/evaluator.
-
-Parameters:
-  model_fn: Model function, takes features and targets tensors or dicts of
-            tensors and returns predictions and loss tensors.
-            E.g. `(features, targets) -> (predictions, loss)`.
-  model_dir: Directory to save model parameters, graph and etc.
-  classification: boolean, true if classification problem.
-  learning_rate: learning rate for the model.
-  optimizer: optimizer for the model, can be:
-             string: name of optimizer, like 'SGD', 'Adam', 'Adagrad', 'Ftl',
-               'Momentum', 'RMSProp', 'Momentum').
-               Full list in contrib/layers/optimizers.py
-             class: sub-class of Optimizer
-               (like tf.train.GradientDescentOptimizer).
-  clip_gradients: clip_norm value for call to `clip_by_global_norm`. None
-                  denotes no gradient clipping.
-  config: Configuration object.
-- - -
-
-#### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, classification=True, learning_rate=0.1, optimizer='Adagrad', clip_gradients=None, config=None)` {#Estimator.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.Estimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=32, steps=None, metrics=None, name=None)` {#Estimator.evaluate}
-
-Evaluates given model with provided evaluation data.
-
-##### Args:
-
-
-*  <b>`x`</b>: features.
-*  <b>`y`</b>: targets.
-*  <b>`input_fn`</b>: Input function. If set, x and y must be None.
-*  <b>`feed_fn`</b>: Function creating a feed dict every time it is called. Called
-    once per iteration.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32. Ignored
-    if input_fn is set.
-*  <b>`steps`</b>: Number of steps to evalute for.
-*  <b>`metrics`</b>: Dict of metric ops to run. If None, the default metric functions
-    are used; if {}, no metrics are used.
-*  <b>`name`</b>: Name of the evaluation if user needs to run multiple evaluation on
-    different data sets, such as evaluate on training data vs test data.
-
-##### Returns:
-
-  Returns self.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If x or y are not None while input_fn or feed_fn is not None.
-
-
-- - -
-
-#### `tf.contrib.learn.Estimator.fit(x, y, steps, batch_size=32, monitors=None)` {#Estimator.fit}
-
-Trains a model given training data X and y.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-     iterator that returns arrays of features. The training input
-     samples for fitting the model.
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-     iterator that returns array of targets. The training target values
-     (class labels in classification, real numbers in regression).
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.Estimator.get_params(deep=True)` {#Estimator.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.Estimator.model_dir` {#Estimator.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.Estimator.partial_fit(x, y, steps=1, batch_size=32, monitors=None)` {#Estimator.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-    iterator that returns arrays of features. The training input
-    samples for fitting the model.
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-    iterator that returns array of targets. The training target values
-    (class label in classification, real numbers in regression).
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.Estimator.predict(x=None, input_fn=None, axis=None, batch_size=None)` {#Estimator.predict}
-
-Returns predictions for given features.
-
-##### Args:
-
-
-*  <b>`x`</b>: features.
-*  <b>`input_fn`</b>: Input function. If set, x must be None.
-*  <b>`axis`</b>: Axis on which to argmax (for classification).
-        Last axis is used by default.
-*  <b>`batch_size`</b>: Override default batch size.
-
-##### Returns:
-
-  Numpy array of predicted classes or regression values.
-
-
-- - -
-
-#### `tf.contrib.learn.Estimator.predict_proba(x=None, input_fn=None, batch_size=None)` {#Estimator.predict_proba}
-
-Returns prediction probabilities for given features (classification).
-
-##### Args:
-
-
-*  <b>`x`</b>: features.
-*  <b>`input_fn`</b>: Input function. If set, x and y must be None.
-*  <b>`batch_size`</b>: Override default batch size.
-
-##### Returns:
-
-  Numpy array of predicted probabilities.
-
-
-- - -
-
-#### `tf.contrib.learn.Estimator.set_params(**params)` {#Estimator.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.Estimator.train(input_fn, steps, monitors=None)` {#Estimator.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.TensorFlowClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.TensorFlowClassifier.md
new file mode 100644
index 0000000000..63588166ec
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.TensorFlowClassifier.md
@@ -0,0 +1,279 @@
+TensorFlow Linear Classifier model.
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.__init__(n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowClassifier.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.bias_` {#TensorFlowClassifier.bias_}
+
+Returns weights of the linear classifier.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowClassifier.evaluate}
+
+See base class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowClassifier.fit}
+
+Builds a neural network model given provided `model_fn` and training
+data X and y.
+
+Note: called first time constructs the graph and initializers
+variables. Consecutives times it will continue training the same model.
+This logic follows partial_fit() interface in scikit-learn.
+
+To restart learning, create new estimator.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class labels in classification, real numbers in regression).
+
+*  <b>`steps`</b>: int, number of steps to train.
+         If None or 0, train for `self.steps`.
+*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
+    invoke early stopping.
+*  <b>`logdir`</b>: the directory to save the log file that can be used for
+  optional visualization.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.get_params(deep=True)` {#TensorFlowClassifier.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.get_tensor(name)` {#TensorFlowClassifier.get_tensor}
+
+Returns tensor by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.get_tensor_value(name)` {#TensorFlowClassifier.get_tensor_value}
+
+Returns value of the tensor give by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Numpy array - value of the tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.get_variable_names()` {#TensorFlowClassifier.get_variable_names}
+
+Returns list of all variable names in this model.
+
+##### Returns:
+
+  List of names.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.model_dir` {#TensorFlowClassifier.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.partial_fit(x, y)` {#TensorFlowClassifier.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class label in classification, real numbers in regression).
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.predict(x, axis=1, batch_size=None)` {#TensorFlowClassifier.predict}
+
+Predict class or regression for X.
+
+For a classification model, the predicted class for each sample in X is
+returned. For a regression model, the predicted value based on X is
+returned.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`axis`</b>: Which axis to argmax for classification.
+    By default axis 1 (next after batch) is used.
+    Use 2 for sequence predictions.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member
+    variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
+  value.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.predict_proba(x, batch_size=None)` {#TensorFlowClassifier.predict_proba}
+
+Predict class probability of the input samples X.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
+  probabilities for each class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.restore(cls, path, config=None)` {#TensorFlowClassifier.restore}
+
+Restores model from give path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Path to the checkpoints and other model information.
+*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
+      reconfigured.
+
+##### Returns:
+
+  Estimator, object of the subclass of TensorFlowEstimator.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.save(path)` {#TensorFlowClassifier.save}
+
+Saves checkpoints and graph to given path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Folder to save model to.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.set_params(**params)` {#TensorFlowClassifier.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.train(input_fn, steps, monitors=None)` {#TensorFlowClassifier.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.weights_` {#TensorFlowClassifier.weights_}
+
+Returns weights of the linear classifier.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.TensorFlowEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.TensorFlowEstimator.md
new file mode 100644
index 0000000000..c3270290b9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.TensorFlowEstimator.md
@@ -0,0 +1,295 @@
+Base class for all TensorFlow estimators.
+
+Parameters:
+  model_fn: Model function, that takes input X, y tensors and outputs
+    prediction and loss tensors.
+  n_classes: Number of classes in the target.
+  batch_size: Mini batch size.
+  steps: Number of steps to run over data.
+  optimizer: Optimizer name (or class), for example "SGD", "Adam",
+    "Adagrad".
+  learning_rate: If this is constant float value, no decay function is used.
+    Instead, a customized decay function can be passed that accepts
+    global_step as parameter and returns a Tensor.
+    e.g. exponential decay function:
+    def exp_decay(global_step):
+        return tf.train.exponential_decay(
+            learning_rate=0.1, global_step,
+            decay_steps=2, decay_rate=0.001)
+  clip_gradients: Clip norm of the gradients to this value to stop
+    gradient explosion.
+  class_weight: None or list of n_classes floats. Weight associated with
+    classes for loss computation. If not given, all classes are supposed to
+    have weight one.
+  continue_training: when continue_training is True, once initialized
+    model will be continuely trained on every call of fit.
+  config: RunConfig object that controls the configurations of the
+    session, e.g. num_cores, gpu_memory_fraction, etc.
+  verbose: Controls the verbosity, possible values:
+    0: the algorithm and debug information is muted.
+    1: trainer prints the progress.
+    2: log device placement is printed.
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.__init__(model_fn, n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, class_weight=None, continue_training=False, config=None, verbose=1)` {#TensorFlowEstimator.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowEstimator.evaluate}
+
+See base class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowEstimator.fit}
+
+Builds a neural network model given provided `model_fn` and training
+data X and y.
+
+Note: called first time constructs the graph and initializers
+variables. Consecutives times it will continue training the same model.
+This logic follows partial_fit() interface in scikit-learn.
+
+To restart learning, create new estimator.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class labels in classification, real numbers in regression).
+
+*  <b>`steps`</b>: int, number of steps to train.
+         If None or 0, train for `self.steps`.
+*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
+    invoke early stopping.
+*  <b>`logdir`</b>: the directory to save the log file that can be used for
+  optional visualization.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.get_params(deep=True)` {#TensorFlowEstimator.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.get_tensor(name)` {#TensorFlowEstimator.get_tensor}
+
+Returns tensor by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.get_tensor_value(name)` {#TensorFlowEstimator.get_tensor_value}
+
+Returns value of the tensor give by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Numpy array - value of the tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.get_variable_names()` {#TensorFlowEstimator.get_variable_names}
+
+Returns list of all variable names in this model.
+
+##### Returns:
+
+  List of names.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.model_dir` {#TensorFlowEstimator.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.partial_fit(x, y)` {#TensorFlowEstimator.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class label in classification, real numbers in regression).
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.predict(x, axis=1, batch_size=None)` {#TensorFlowEstimator.predict}
+
+Predict class or regression for X.
+
+For a classification model, the predicted class for each sample in X is
+returned. For a regression model, the predicted value based on X is
+returned.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`axis`</b>: Which axis to argmax for classification.
+    By default axis 1 (next after batch) is used.
+    Use 2 for sequence predictions.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member
+    variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
+  value.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.predict_proba(x, batch_size=None)` {#TensorFlowEstimator.predict_proba}
+
+Predict class probability of the input samples X.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
+  probabilities for each class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.restore(cls, path, config=None)` {#TensorFlowEstimator.restore}
+
+Restores model from give path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Path to the checkpoints and other model information.
+*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
+      reconfigured.
+
+##### Returns:
+
+  Estimator, object of the subclass of TensorFlowEstimator.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.save(path)` {#TensorFlowEstimator.save}
+
+Saves checkpoints and graph to given path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Folder to save model to.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.set_params(**params)` {#TensorFlowEstimator.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.train(input_fn, steps, monitors=None)` {#TensorFlowEstimator.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.TensorFlowLinearRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.TensorFlowLinearRegressor.md
deleted file mode 100644
index 6c793e1b90..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.TensorFlowLinearRegressor.md
+++ /dev/null
@@ -1,279 +0,0 @@
-TensorFlow Linear Regression model.
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.__init__(n_classes=0, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowLinearRegressor.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.bias_` {#TensorFlowLinearRegressor.bias_}
-
-Returns bias of the linear regression.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowLinearRegressor.evaluate}
-
-See base class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowLinearRegressor.fit}
-
-Builds a neural network model given provided `model_fn` and training
-data X and y.
-
-Note: called first time constructs the graph and initializers
-variables. Consecutives times it will continue training the same model.
-This logic follows partial_fit() interface in scikit-learn.
-
-To restart learning, create new estimator.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class labels in classification, real numbers in regression).
-
-*  <b>`steps`</b>: int, number of steps to train.
-         If None or 0, train for `self.steps`.
-*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
-    invoke early stopping.
-*  <b>`logdir`</b>: the directory to save the log file that can be used for
-  optional visualization.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.get_params(deep=True)` {#TensorFlowLinearRegressor.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.get_tensor(name)` {#TensorFlowLinearRegressor.get_tensor}
-
-Returns tensor by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.get_tensor_value(name)` {#TensorFlowLinearRegressor.get_tensor_value}
-
-Returns value of the tensor give by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.get_variable_names()` {#TensorFlowLinearRegressor.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.model_dir` {#TensorFlowLinearRegressor.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.partial_fit(x, y)` {#TensorFlowLinearRegressor.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class label in classification, real numbers in regression).
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.predict(x, axis=1, batch_size=None)` {#TensorFlowLinearRegressor.predict}
-
-Predict class or regression for X.
-
-For a classification model, the predicted class for each sample in X is
-returned. For a regression model, the predicted value based on X is
-returned.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`axis`</b>: Which axis to argmax for classification.
-    By default axis 1 (next after batch) is used.
-    Use 2 for sequence predictions.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member
-    variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
-  value.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.predict_proba(x, batch_size=None)` {#TensorFlowLinearRegressor.predict_proba}
-
-Predict class probability of the input samples X.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
-  probabilities for each class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.restore(cls, path, config=None)` {#TensorFlowLinearRegressor.restore}
-
-Restores model from give path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Path to the checkpoints and other model information.
-*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
-      reconfigured.
-
-##### Returns:
-
-  Estimator, object of the subclass of TensorFlowEstimator.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.save(path)` {#TensorFlowLinearRegressor.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.set_params(**params)` {#TensorFlowLinearRegressor.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.train(input_fn, steps, monitors=None)` {#TensorFlowLinearRegressor.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.weights_` {#TensorFlowLinearRegressor.weights_}
-
-Returns weights of the linear regression.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.extract_pandas_data.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.extract_pandas_data.md
deleted file mode 100644
index 82703a8097..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.extract_pandas_data.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.contrib.learn.extract_pandas_data(data)` {#extract_pandas_data}
-
-Extract data from pandas.DataFrame for predictors
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.extract_pandas_matrix.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.extract_pandas_matrix.md
new file mode 100644
index 0000000000..c2a275bc66
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.extract_pandas_matrix.md
@@ -0,0 +1,4 @@
+### `tf.contrib.learn.extract_pandas_matrix(data)` {#extract_pandas_matrix}
+
+Extracts numpy matrix from pandas DataFrame.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.read_batch_features.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.read_batch_features.md
new file mode 100644
index 0000000000..75b40f7e75
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.read_batch_features.md
@@ -0,0 +1,43 @@
+### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name=None)` {#read_batch_features}
+
+Adds operations to read, queue, batch and parse `Example` protos.
+
+Given file pattern (or list of files), will setup a queue for file names,
+read `Example` proto using provided `reader`, use batch queue to create
+batches of examples of size `batch_size` and parse example given `features`
+specification.
+
+All queue runners are added to the queue runners collection, and may be
+started via `start_queue_runners`.
+
+All ops are added to the default graph.
+
+##### Args:
+
+
+*  <b>`file_pattern`</b>: List of files or pattern of file paths containing
+      `Example` records. See `tf.gfile.Glob` for pattern rules.
+*  <b>`batch_size`</b>: An int or scalar `Tensor` specifying the batch size to use.
+*  <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
+    `VarLenFeature` values.
+*  <b>`reader`</b>: A function or class that returns an object with
+    `read` method, (filename tensor) -> (example tensor).
+*  <b>`randomize_input`</b>: Whether the input should be randomized.
+*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
+    dataset. If None, cycles through the dataset forever. NOTE - If specified,
+    creates a variable that must be initialized, so call
+    tf.initialize_all_variables() as shown in the tests.
+*  <b>`queue_capacity`</b>: Capacity for input queue.
+*  <b>`reader_num_threads`</b>: The number of threads to read examples.
+*  <b>`parser_num_threads`</b>: The number of threads to parse examples.
+*  <b>`name`</b>: Name of resulting op.
+
+##### Returns:
+
+  A dict of `Tensor` or `SparseTensor` objects for each in `features`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: for invalid inputs.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_accuracy.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_accuracy.md
deleted file mode 100644
index 684d1849d3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_accuracy.md
+++ /dev/null
@@ -1,51 +0,0 @@
-### `tf.contrib.metrics.streaming_accuracy(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_accuracy}
-
-Calculates how often `predictions` matches `labels`.
-
-The `streaming_accuracy` function creates two local variables, `total` and
-`count` that are used to compute the frequency with which `predictions`
-matches `labels`. This frequency is ultimately returned as `accuracy`: an
-idempotent operation that simply divides `total` by `count`.
-To facilitate the estimation of the accuracy over a stream of data, the
-function utilizes two operations. First, an `is_correct` operation that
-computes a tensor whose shape matches `predictions` and whose elements are
-set to 1.0 when the corresponding values of `predictions` and `labels match
-and 0.0 otherwise. Second, an `update_op` operation whose behavior is
-dependent on the value of `weights`. If `weights` is None, then `update_op`
-increments `total` with the number of elements of `predictions` that match
-`labels` and increments `count` with the number of elements in `values`. If
-`weights` is not `None`, then `update_op` increments `total` with the reduced
-sum of the product of `weights` and `is_correct` and increments `count` with
-the reduced sum of `weights`. In addition to performing the updates,
-`update_op` also returns the `accuracy` value.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: The predicted values, a `Tensor` of any shape.
-*  <b>`labels`</b>: The ground truth values, a `Tensor` whose shape matches
-    `predictions`.
-*  <b>`weights`</b>: An optional set of weights whose shape matches `predictions`
-    which, when not `None`, produces a weighted mean accuracy.
-*  <b>`metrics_collections`</b>: An optional list of collections that `accuracy` should
-    be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
-    be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`accuracy`</b>: A tensor representing the accuracy, the value of `total` divided
-    by `count`.
-*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
-    appropriately and whose value matches `accuracy`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the dimensions of `predictions` and `labels` don't match or
-    if `weight` is not `None` and its shape doesn't match `predictions` or
-    if either `metrics_collections` or `updates_collections` are not
-    a list or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_mean_relative_error.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_mean_relative_error.md
new file mode 100644
index 0000000000..3740bbbaad
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_mean_relative_error.md
@@ -0,0 +1,49 @@
+### `tf.contrib.metrics.streaming_mean_relative_error(predictions, labels, normalizer, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean_relative_error}
+
+Computes the mean relative error by normalizing with the given values.
+
+The `streaming_mean_relative_error` function creates two local variables,
+`total` and `count` that are used to compute the mean relative absolute error.
+This average is ultimately returned as `mean_relative_error`: an idempotent
+operation that simply divides `total` by `count`. To facilitate the estimation
+of the mean relative error over a stream of data, the function utilizes two
+operations. First, a `relative_errors` operation divides the absolute value
+of the differences between `predictions` and `labels` by the `normalizer`.
+Second, an `update_op` operation whose behavior is dependent on the value of
+`weights`. If `weights` is None, then `update_op` increments `total` with the
+reduced sum of `relative_errors` and increments `count` with the number of
+elements in `relative_errors`. If `weights` is not `None`, then `update_op`
+increments `total` with the reduced sum of the product of `weights` and
+`relative_errors` and increments `count` with the reduced sum of `weights`. In
+addition to performing the updates, `update_op` also returns the
+`mean_relative_error` value.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: A `Tensor` of arbitrary shape.
+*  <b>`labels`</b>: A `Tensor` of the same shape as `predictions`.
+*  <b>`normalizer`</b>: A `Tensor` of the same shape as `predictions`.
+*  <b>`weights`</b>: An optional set of weights of the same shape as `predictions`. If
+    `weights` is not None, the function computes a weighted mean.
+*  <b>`metrics_collections`</b>: An optional list of collections that
+    `mean_relative_error` should be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
+    be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`mean_relative_error`</b>: A tensor representing the current mean, the value of
+    `total` divided by `count`.
+*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
+    appropriately and whose value matches `mean_relative_error`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `weights` is not `None` and its shape doesn't match
+    `predictions` or if either `metrics_collections` or `updates_collections`
+    are not a list or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_mean_squared_error.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_mean_squared_error.md
new file mode 100644
index 0000000000..6d682d0427
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_mean_squared_error.md
@@ -0,0 +1,48 @@
+### `tf.contrib.metrics.streaming_mean_squared_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean_squared_error}
+
+Computes the mean squared error between the labels and predictions.
+
+The `streaming_mean_squared_error` function creates two local variables,
+`total` and `count` that are used to compute the mean squared error.
+This average is ultimately returned as `mean_squared_error`: an idempotent
+operation that simply divides `total` by `count`. To facilitate the estimation
+of the mean squared error over a stream of data, the function utilizes two
+operations. First, a `squared_error` operation computes the element-wise
+square of the difference between `predictions` and `labels`. Second, an
+`update_op` operation whose behavior is dependent on the value of `weights`.
+If `weights` is None, then `update_op` increments `total` with the
+reduced sum of `squared_error` and increments `count` with the number of
+elements in `squared_error`. If `weights` is not `None`, then `update_op`
+increments `total` with the reduced sum of the product of `weights` and
+`squared_error` and increments `count` with the reduced sum of `weights`. In
+addition to performing the updates, `update_op` also returns the
+`mean_squared_error` value.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: A `Tensor` of arbitrary shape.
+*  <b>`labels`</b>: A `Tensor` of the same shape as `predictions`.
+*  <b>`weights`</b>: An optional set of weights of the same shape as `predictions`. If
+    `weights` is not None, the function computes a weighted mean.
+*  <b>`metrics_collections`</b>: An optional list of collections that
+    `mean_squared_error` should be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
+    be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`mean_squared_error`</b>: A tensor representing the current mean, the value of
+    `total` divided by `count`.
+*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
+    appropriately and whose value matches `mean_squared_error`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `weights` is not `None` and its shape doesn't match
+    `predictions` or if either `metrics_collections` or `updates_collections`
+    are not a list or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_recall_at_k.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_recall_at_k.md
new file mode 100644
index 0000000000..dd03b95b69
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.metrics.streaming_recall_at_k.md
@@ -0,0 +1,52 @@
+### `tf.contrib.metrics.streaming_recall_at_k(predictions, labels, k, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_recall_at_k}
+
+Computes the recall@k of the predictions with respect to dense labels.
+
+The `streaming_recall_at_k` function creates two local variables, `total` and
+`count`, that are used to compute the recall@k frequency. This frequency is
+ultimately returned as `recall_at_<k>`: an idempotent operation that simply
+divides `total` by `count`. To facilitate the estimation of recall@k over a
+stream of data, the function utilizes two operations. First, an `in_top_k`
+operation computes a tensor with shape [batch_size] whose elements indicate
+whether or not the corresponding label is in the top `k` predictions of the
+`predictions` `Tensor`. Second, an `update_op` operation whose behavior is
+dependent on the value of `ignore_mask`. If `ignore_mask` is None, then
+`update_op` increments `total` with the number of elements of `in_top_k` that
+are set to `True` and increments `count` with the batch size. If `ignore_mask`
+is not `None`, then `update_op` increments `total` with the number of elements
+in `in_top_k` that are `True` whose corresponding element in `ignore_mask` is
+`False`. In addition to performing the updates, `update_op` also returns the
+recall value.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: A floating point tensor of dimension [batch_size, num_classes]
+*  <b>`labels`</b>: A tensor of dimension [batch_size] whose type is in `int32`,
+    `int64`.
+*  <b>`k`</b>: The number of top elements to look at for computing precision.
+*  <b>`ignore_mask`</b>: An optional, binary tensor whose size matches `labels`. If an
+    element of `ignore_mask` is True, the corresponding prediction and label
+    pair is used to compute the metrics. Otherwise, the pair is ignored.
+*  <b>`metrics_collections`</b>: An optional list of collections that `recall_at_k`
+    should be added to.
+*  <b>`updates_collections`</b>: An optional list of collections `update_op` should be
+    added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`recall_at_k`</b>: A tensor representing the recall@k, the fraction of labels
+    which fall into the top `k` predictions.
+*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
+    appropriately and whose value matches `recall_at_k`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the dimensions of `predictions` and `labels` don't match or
+    if `ignore_mask` is not `None` and its shape doesn't match `predictions`
+    or if either `metrics_collections` or `updates_collections` are not a list
+    or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.util.constant_value.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.util.constant_value.md
deleted file mode 100644
index 58ba7b0abb..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.util.constant_value.md
+++ /dev/null
@@ -1,31 +0,0 @@
-### `tf.contrib.util.constant_value(tensor)` {#constant_value}
-
-Returns the constant value of the given tensor, if efficiently calculable.
-
-This function attempts to partially evaluate the given tensor, and
-returns its value as a numpy ndarray if this succeeds.
-
-TODO(mrry): Consider whether this function should use a registration
-mechanism like gradients and ShapeFunctions, so that it is easily
-extensible.
-
-NOTE: If `constant_value(tensor)` returns a non-`None` result, it will no
-longer be possible to feed a different value for `tensor`. This allows the
-result of this function to influence the graph that is constructed, and
-permits static shape optimizations.
-
-##### Args:
-
-
-*  <b>`tensor`</b>: The Tensor to be evaluated.
-
-##### Returns:
-
-  A numpy ndarray containing the constant value of the given `tensor`,
-  or None if it cannot be calculated.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if tensor is not an ops.Tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.cross.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.cross.md
new file mode 100644
index 0000000000..eecf2e869b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.cross.md
@@ -0,0 +1,22 @@
+### `tf.cross(a, b, name=None)` {#cross}
+
+Compute the pairwise cross product.
+
+`a` and `b` must be the same shape; they can either be simple 3-element vectors,
+or any shape where the innermost dimension is 3. In the latter case, each pair
+of corresponding 3-element vectors is cross-multiplied independently.
+
+##### Args:
+
+
+*  <b>`a`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+    A tensor containing 3-element vectors.
+*  <b>`b`</b>: A `Tensor`. Must have the same type as `a`.
+    Another tensor, of same type and shape as `a`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `a`.
+  Pairwise cross product of the vectors in `a` and `b`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.decode_json_example.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.decode_json_example.md
new file mode 100644
index 0000000000..bf5184c40a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.decode_json_example.md
@@ -0,0 +1,25 @@
+### `tf.decode_json_example(json_examples, name=None)` {#decode_json_example}
+
+Convert JSON-encoded Example records to binary protocol buffer strings.
+
+This op translates a tensor containing Example records, encoded using
+the [standard JSON
+mapping](https://developers.google.com/protocol-buffers/docs/proto3#json),
+into a tensor containing the same records encoded as binary protocol
+buffers. The resulting tensor can then be fed to any of the other
+Example-parsing ops.
+
+##### Args:
+
+
+*  <b>`json_examples`</b>: A `Tensor` of type `string`.
+    Each string is a JSON object serialized according to the JSON
+    mapping of the Example proto.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `string`.
+  Each string is a binary Example protocol buffer corresponding
+  to the respective element of `json_examples`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.delete_session_tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.delete_session_tensor.md
new file mode 100644
index 0000000000..2f52941c5f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.delete_session_tensor.md
@@ -0,0 +1,19 @@
+### `tf.delete_session_tensor(name=None)` {#delete_session_tensor}
+
+Delete the tensor by feeding a tensor handle.
+
+This is EXPERIMENTAL and subject to change.
+
+Delete the tensor of a given tensor handle. The tensor is produced
+in a previous run() and stored in the state of the session.
+
+##### Args:
+
+
+*  <b>`name`</b>: Optional name prefix for the return tensor.
+
+##### Returns:
+
+  A pair of graph elements. The first is a placeholder for feeding a
+  tensor handle and the second is a deletion operation.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.div.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.div.md
deleted file mode 100644
index 92eba7927a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.div.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.div(x, y, name=None)` {#div}
-
-Returns x / y element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.errors.CancelledError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.errors.CancelledError.md
new file mode 100644
index 0000000000..cf20c0e2e3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.errors.CancelledError.md
@@ -0,0 +1,17 @@
+Raised when an operation or step is cancelled.
+
+For example, a long-running operation (e.g.
+[`queue.enqueue()`](../../api_docs/python/io_ops.md#QueueBase.enqueue) may be
+cancelled by running another operation (e.g.
+[`queue.close(cancel_pending_enqueues=True)`](../../api_docs/python/io_ops.md#QueueBase.close),
+or by [closing the session](../../api_docs/python/client.md#Session.close).
+A step that is running such a long-running operation will fail by raising
+`CancelledError`.
+
+- - -
+
+#### `tf.errors.CancelledError.__init__(node_def, op, message)` {#CancelledError.__init__}
+
+Creates a `CancelledError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.errors.DataLossError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.errors.DataLossError.md
deleted file mode 100644
index 3193e77ae3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.errors.DataLossError.md
+++ /dev/null
@@ -1,13 +0,0 @@
-Raised when unrecoverable data loss or corruption is encountered.
-
-For example, this may be raised by running a
-[`tf.WholeFileReader.read()`](../../api_docs/python/io_ops.md#WholeFileReader)
-operation, if the file is truncated while it is being read.
-
-- - -
-
-#### `tf.errors.DataLossError.__init__(node_def, op, message)` {#DataLossError.__init__}
-
-Creates a `DataLossError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.errors.FailedPreconditionError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.errors.FailedPreconditionError.md
deleted file mode 100644
index 1cbd338bf9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.errors.FailedPreconditionError.md
+++ /dev/null
@@ -1,13 +0,0 @@
-Operation was rejected because the system is not in a state to execute it.
-
-This exception is most commonly raised when running an operation
-that reads a [`tf.Variable`](../../api_docs/python/state_ops.md#Variable)
-before it has been initialized.
-
-- - -
-
-#### `tf.errors.FailedPreconditionError.__init__(node_def, op, message)` {#FailedPreconditionError.__init__}
-
-Creates a `FailedPreconditionError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.fft.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.fft.md
new file mode 100644
index 0000000000..5a2c3c635d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.fft.md
@@ -0,0 +1,14 @@
+### `tf.fft(input, name=None)` {#fft}
+
+Compute the 1-dimensional discrete Fourier Transform.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 vector.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`. The 1D Fourier Transform of `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.get_collection.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.get_collection.md
new file mode 100644
index 0000000000..fc0044b490
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.get_collection.md
@@ -0,0 +1,25 @@
+### `tf.get_collection(key, scope=None)` {#get_collection}
+
+Wrapper for `Graph.get_collection()` using the default graph.
+
+See [`Graph.get_collection()`](../../api_docs/python/framework.md#Graph.get_collection)
+for more details.
+
+##### Args:
+
+
+*  <b>`key`</b>: The key for the collection. For example, the `GraphKeys` class
+    contains many standard names for collections.
+*  <b>`scope`</b>: (Optional.) If supplied, the resulting list is filtered to include
+    only items whose `name` attribute matches using `re.match`. Items
+    without a `name` attribute are never returned if a scope is supplied and
+    the choice or `re.match` means that a `scope` without special tokens
+    filters by prefix.
+
+##### Returns:
+
+  The list of values in the collection with the given `name`, or
+  an empty list if no value has been added to that collection. The
+  list contains the values in the order under which they were
+  collected.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.group.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.group.md
deleted file mode 100644
index 7958cf9e58..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.group.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.group(*inputs, **kwargs)` {#group}
-
-Create an op that groups multiple operations.
-
-When this op finishes, all ops in `input` have finished. This op has no
-output.
-
-See also `tuple` and `with_dependencies`.
-
-##### Args:
-
-
-*  <b>`*inputs`</b>: Zero or more tensors to group.
-*  <b>`**kwargs`</b>: Optional parameters to pass when constructing the NodeDef.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  An Operation that executes all its inputs.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If an unknown keyword argument is provided.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.histogram_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.histogram_summary.md
deleted file mode 100644
index 1ede11e820..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.histogram_summary.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.histogram_summary(tag, values, collections=None, name=None)` {#histogram_summary}
-
-Outputs a `Summary` protocol buffer with a histogram.
-
-The generated
-[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-has one summary value containing a histogram for `values`.
-
-This op reports an `InvalidArgument` error if any value is not finite.
-
-##### Args:
-
-
-*  <b>`tag`</b>: A `string` `Tensor`. 0-D.  Tag to use for the summary value.
-*  <b>`values`</b>: A real numeric `Tensor`. Any shape. Values to use to
-    build the histogram.
-*  <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
-    added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A scalar `Tensor` of type `string`. The serialized `Summary` protocol
-  buffer.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.igamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.igamma.md
deleted file mode 100644
index 1cf6860651..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.igamma.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.igamma(a, x, name=None)` {#igamma}
-
-Compute the lower regularized incomplete Gamma function `Q(a, x)`.
-
-The lower regularized incomplete Gamma function is defined as:
-
-```
-P(a, x) = gamma(a, x) / Gamma(x) = 1 - Q(a, x)
-```
-where
-```
-gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt
-```
-is the lower incomplete Gamma function.
-
-Note, above `Q(a, x)` (`Igammac`) is the upper regularized complete
-Gamma function.
-
-##### Args:
-
-
-*  <b>`a`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-*  <b>`x`</b>: A `Tensor`. Must have the same type as `a`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `a`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.convert_image_dtype.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.convert_image_dtype.md
deleted file mode 100644
index 63db6f36a9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.convert_image_dtype.md
+++ /dev/null
@@ -1,32 +0,0 @@
-### `tf.image.convert_image_dtype(image, dtype, saturate=False, name=None)` {#convert_image_dtype}
-
-Convert `image` to `dtype`, scaling its values if needed.
-
-Images that are represented using floating point values are expected to have
-values in the range [0,1). Image data stored in integer data types are
-expected to have values in the range `[0,MAX]`, where `MAX` is the largest
-positive representable number for the data type.
-
-This op converts between data types, scaling the values appropriately before
-casting.
-
-Note that converting from floating point inputs to integer types may lead to
-over/underflow problems. Set saturate to `True` to avoid such problem in
-problematic conversions. If enabled, saturation will clip the output into the
-allowed range before performing a potentially dangerous cast (and only before
-performing such a cast, i.e., when casting from a floating point to an integer
-type, and when casting from a signed to an unsigned type; `saturate` has no
-effect on casts between floats, or on casts that increase the type's range).
-
-##### Args:
-
-
-*  <b>`image`</b>: An image.
-*  <b>`dtype`</b>: A `DType` to convert `image` to.
-*  <b>`saturate`</b>: If `True`, clip the input before casting (if necessary).
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  `image`, converted to `dtype`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.decode_jpeg.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.decode_jpeg.md
new file mode 100644
index 0000000000..f4c6f1340a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.decode_jpeg.md
@@ -0,0 +1,41 @@
+### `tf.image.decode_jpeg(contents, channels=None, ratio=None, fancy_upscaling=None, try_recover_truncated=None, acceptable_fraction=None, name=None)` {#decode_jpeg}
+
+Decode a JPEG-encoded image to a uint8 tensor.
+
+The attr `channels` indicates the desired number of color channels for the
+decoded image.
+
+Accepted values are:
+
+*   0: Use the number of channels in the JPEG-encoded image.
+*   1: output a grayscale image.
+*   3: output an RGB image.
+
+If needed, the JPEG-encoded image is transformed to match the requested number
+of color channels.
+
+The attr `ratio` allows downscaling the image by an integer factor during
+decoding.  Allowed values are: 1, 2, 4, and 8.  This is much faster than
+downscaling the image later.
+
+##### Args:
+
+
+*  <b>`contents`</b>: A `Tensor` of type `string`. 0-D.  The JPEG-encoded image.
+*  <b>`channels`</b>: An optional `int`. Defaults to `0`.
+    Number of color channels for the decoded image.
+*  <b>`ratio`</b>: An optional `int`. Defaults to `1`. Downscaling ratio.
+*  <b>`fancy_upscaling`</b>: An optional `bool`. Defaults to `True`.
+    If true use a slower but nicer upscaling of the
+    chroma planes (yuv420/422 only).
+*  <b>`try_recover_truncated`</b>: An optional `bool`. Defaults to `False`.
+    If true try to recover an image from truncated input.
+*  <b>`acceptable_fraction`</b>: An optional `float`. Defaults to `1`.
+    The minimum required fraction of lines before a truncated
+    input is accepted.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `uint8`. 3-D with shape `[height, width, channels]`..
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.flip_left_right.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.flip_left_right.md
deleted file mode 100644
index ac8c99806e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.flip_left_right.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.image.flip_left_right(image)` {#flip_left_right}
-
-Flip an image horizontally (left to right).
-
-Outputs the contents of `image` flipped along the second dimension, which is
-`width`.
-
-See also `reverse()`.
-
-##### Args:
-
-
-*  <b>`image`</b>: A 3-D tensor of shape `[height, width, channels].`
-
-##### Returns:
-
-  A 3-D tensor of the same type and shape as `image`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if the shape of `image` not supported.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.flip_up_down.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.flip_up_down.md
new file mode 100644
index 0000000000..ed92277f8a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.flip_up_down.md
@@ -0,0 +1,23 @@
+### `tf.image.flip_up_down(image)` {#flip_up_down}
+
+Flip an image horizontally (upside down).
+
+Outputs the contents of `image` flipped along the first dimension, which is
+`height`.
+
+See also `reverse()`.
+
+##### Args:
+
+
+*  <b>`image`</b>: A 3-D tensor of shape `[height, width, channels].`
+
+##### Returns:
+
+  A 3-D tensor of the same type and shape as `image`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the shape of `image` not supported.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.hsv_to_rgb.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.hsv_to_rgb.md
deleted file mode 100644
index 3193dd9c60..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.hsv_to_rgb.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.image.hsv_to_rgb(images, name=None)` {#hsv_to_rgb}
-
-Convert one or more images from HSV to RGB.
-
-Outputs a tensor of the same shape as the `images` tensor, containing the RGB
-value of the pixels. The output is only well defined if the value in `images`
-are in `[0,1]`.
-
-See `rgb_to_hsv` for a description of the HSV encoding.
-
-##### Args:
-
-
-*  <b>`images`</b>: A `Tensor` of type `float32`.
-    1-D or higher rank. HSV data to convert. Last dimension must be size 3.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `float32`. `images` converted to RGB.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.random_brightness.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.random_brightness.md
new file mode 100644
index 0000000000..6c773b6985
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.random_brightness.md
@@ -0,0 +1,25 @@
+### `tf.image.random_brightness(image, max_delta, seed=None)` {#random_brightness}
+
+Adjust the brightness of images by a random factor.
+
+Equivalent to `adjust_brightness()` using a `delta` randomly picked in the
+interval `[-max_delta, max_delta)`.
+
+##### Args:
+
+
+*  <b>`image`</b>: An image.
+*  <b>`max_delta`</b>: float, must be non-negative.
+*  <b>`seed`</b>: A Python integer. Used to create a random seed. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+
+##### Returns:
+
+  The brightness-adjusted image.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `max_delta` is negative.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.random_saturation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.random_saturation.md
deleted file mode 100644
index 397bfc4d0b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.random_saturation.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.image.random_saturation(image, lower, upper, seed=None)` {#random_saturation}
-
-Adjust the saturation of an RGB image by a random factor.
-
-Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly
-picked in the interval `[lower, upper]`.
-
-##### Args:
-
-
-*  <b>`image`</b>: RGB image or images. Size of the last dimension must be 3.
-*  <b>`lower`</b>: float.  Lower bound for the random saturation factor.
-*  <b>`upper`</b>: float.  Upper bound for the random saturation factor.
-*  <b>`seed`</b>: An operation-specific seed. It will be used in conjunction
-    with the graph-level seed to determine the real seeds that will be
-    used in this operation. Please see the documentation of
-    set_random_seed for its interaction with the graph-level random seed.
-
-##### Returns:
-
-  Adjusted image(s), same shape and DType as `image`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `upper <= lower` or if `lower < 0`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.resize_area.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.resize_area.md
deleted file mode 100644
index dbc6fd1bcd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.resize_area.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.image.resize_area(images, size, align_corners=None, name=None)` {#resize_area}
-
-Resize `images` to `size` using area interpolation.
-
-Input images can be of different types but output images are always float.
-
-##### Args:
-
-
-*  <b>`images`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
-    4-D with shape `[batch, height, width, channels]`.
-*  <b>`size`</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
-    new size for the images.
-*  <b>`align_corners`</b>: An optional `bool`. Defaults to `False`.
-    If true, rescale input by (new_height - 1) / (height - 1), which
-    exactly aligns the 4 corners of images and resized images. If false, rescale
-    by new_height / height. Treat similarly the width dimension.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `float32`. 4-D with shape
-  `[batch, new_height, new_width, channels]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.resize_bicubic.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.resize_bicubic.md
new file mode 100644
index 0000000000..1805c7423d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.image.resize_bicubic.md
@@ -0,0 +1,24 @@
+### `tf.image.resize_bicubic(images, size, align_corners=None, name=None)` {#resize_bicubic}
+
+Resize `images` to `size` using bicubic interpolation.
+
+Input images can be of different types but output images are always float.
+
+##### Args:
+
+
+*  <b>`images`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
+    4-D with shape `[batch, height, width, channels]`.
+*  <b>`size`</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
+    new size for the images.
+*  <b>`align_corners`</b>: An optional `bool`. Defaults to `False`.
+    If true, rescale input by (new_height - 1) / (height - 1), which
+    exactly aligns the 4 corners of images and resized images. If false, rescale
+    by new_height / height. Treat similarly the width dimension.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `float32`. 4-D with shape
+  `[batch, new_height, new_width, channels]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.import_graph_def.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.import_graph_def.md
deleted file mode 100644
index 0ff3d621d4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.import_graph_def.md
+++ /dev/null
@@ -1,49 +0,0 @@
-### `tf.import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None, producer_op_list=None)` {#import_graph_def}
-
-Imports the TensorFlow graph in `graph_def` into the Python `Graph`.
-
-This function provides a way to import a serialized TensorFlow
-[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
-protocol buffer, and extract individual objects in the `GraphDef` as
-[`Tensor`](#Tensor) and [`Operation`](#Operation) objects. See
-[`Graph.as_graph_def()`](#Graph.as_graph_def) for a way to create a
-`GraphDef` proto.
-
-##### Args:
-
-
-*  <b>`graph_def`</b>: A `GraphDef` proto containing operations to be imported into
-    the default graph.
-*  <b>`input_map`</b>: A dictionary mapping input names (as strings) in `graph_def`
-    to `Tensor` objects. The values of the named input tensors in the
-    imported graph will be re-mapped to the respective `Tensor` values.
-*  <b>`return_elements`</b>: A list of strings containing operation names in
-    `graph_def` that will be returned as `Operation` objects; and/or
-    tensor names in `graph_def` that will be returned as `Tensor` objects.
-*  <b>`name`</b>: (Optional.) A prefix that will be prepended to the names in
-    `graph_def`. Defaults to `"import"`.
-*  <b>`op_dict`</b>: (Optional.) A dictionary mapping op type names to `OpDef` protos.
-    Must contain an `OpDef` proto for each op type named in `graph_def`.
-    If omitted, uses the `OpDef` protos registered in the global registry.
-*  <b>`producer_op_list`</b>: (Optional.) An `OpList` proto with the (possibly stripped)
-    list of `OpDef`s used by the producer of the graph. If provided, attrs
-    for ops in `graph_def` that are not in `op_dict` that have their default
-    value according to `producer_op_list` will be removed. This will allow
-    some more `GraphDef`s produced by later binaries to be accepted by
-    earlier binaries.
-
-##### Returns:
-
-  A list of `Operation` and/or `Tensor` objects from the imported graph,
-  corresponding to the names in `return_elements`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `graph_def` is not a `GraphDef` proto,
-    `input_map` is not a dictionary mapping strings to `Tensor` objects,
-    or `return_elements` is not a list of strings.
-*  <b>`ValueError`</b>: If `input_map`, or `return_elements` contains names that
-    do not appear in `graph_def`, or `graph_def` is not well-formed (e.g.
-    it refers to an unknown tensor).
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.initialize_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.initialize_variables.md
deleted file mode 100644
index 8941ab4853..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.initialize_variables.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.initialize_variables(var_list, name='init')` {#initialize_variables}
-
-Returns an Op that initializes a list of variables.
-
-After you launch the graph in a session, you can run the returned Op to
-initialize all the variables in `var_list`. This Op runs all the
-initializers of the variables in `var_list` in parallel.
-
-Calling `initialize_variables()` is equivalent to passing the list of
-initializers to `Group()`.
-
-If `var_list` is empty, however, the function still returns an Op that can
-be run. That Op just has no effect.
-
-##### Args:
-
-
-*  <b>`var_list`</b>: List of `Variable` objects to initialize.
-*  <b>`name`</b>: Optional name for the returned operation.
-
-##### Returns:
-
-  An Op that run the initializers of all the specified variables.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.is_inf.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.is_inf.md
deleted file mode 100644
index 8955d5c9cc..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.is_inf.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.is_inf(x, name=None)` {#is_inf}
-
-Returns which elements of x are Inf.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.linspace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.linspace.md
new file mode 100644
index 0000000000..570845f502
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.linspace.md
@@ -0,0 +1,28 @@
+### `tf.linspace(start, stop, num, name=None)` {#linspace}
+
+Generates values in an interval.
+
+A sequence of `num` evenly-spaced values are generated beginning at `start`.
+If `num > 1`, the values in the sequence increase by `stop - start / num - 1`,
+so that the last one is exactly `stop`.
+
+For example:
+
+```
+tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0  11.0  12.0]
+```
+
+##### Args:
+
+
+*  <b>`start`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+    First entry in the range.
+*  <b>`stop`</b>: A `Tensor`. Must have the same type as `start`.
+    Last entry in the range.
+*  <b>`num`</b>: A `Tensor` of type `int32`. Number of values to generate.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `start`. 1-D. The generated values.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.listdiff.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.listdiff.md
new file mode 100644
index 0000000000..1f04bd8d9e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.listdiff.md
@@ -0,0 +1,40 @@
+### `tf.listdiff(x, y, name=None)` {#listdiff}
+
+Computes the difference between two lists of numbers or strings.
+
+Given a list `x` and a list `y`, this operation returns a list `out` that
+represents all values that are in `x` but not in `y`. The returned list `out`
+is sorted in the same order that the numbers appear in `x` (duplicates are
+preserved). This operation also returns a list `idx` that represents the
+position of each `out` element in `x`. In other words:
+
+`out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1]`
+
+For example, given this input:
+
+```prettyprint
+x = [1, 2, 3, 4, 5, 6]
+y = [1, 3, 5]
+```
+
+This operation would return:
+
+```prettyprint
+out ==> [2, 4, 6]
+idx ==> [1, 3, 5]
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. 1-D. Values to keep.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`. 1-D. Values to remove.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of `Tensor` objects (out, idx).
+
+*  <b>`out`</b>: A `Tensor`. Has the same type as `x`. 1-D. Values present in `x` but not in `y`.
+*  <b>`idx`</b>: A `Tensor` of type `int32`. 1-D. Positions of `x` values preserved in `out`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.local_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.local_variables.md
deleted file mode 100644
index b3612c7cbf..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.local_variables.md
+++ /dev/null
@@ -1,8 +0,0 @@
-### `tf.local_variables()` {#local_variables}
-
-Returns all variables created with collection=[LOCAL_VARIABLES].
-
-##### Returns:
-
-  A list of local Variable objects.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.log.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.log.md
new file mode 100644
index 0000000000..4ce9ddac8c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.log.md
@@ -0,0 +1,16 @@
+### `tf.log(x, name=None)` {#log}
+
+Computes natural logarithm of x element-wise.
+
+I.e., \\(y = \log_e x\\).
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.matrix_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.matrix_solve.md
new file mode 100644
index 0000000000..b33decd2e9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.matrix_solve.md
@@ -0,0 +1,21 @@
+### `tf.matrix_solve(matrix, rhs, adjoint=None, name=None)` {#matrix_solve}
+
+Solves a system of linear equations. Checks for invertibility.
+
+##### Args:
+
+
+*  <b>`matrix`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
+    Shape is `[M, M]`.
+*  <b>`rhs`</b>: A `Tensor`. Must have the same type as `matrix`. Shape is `[M, K]`.
+*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
+    Boolean indicating whether to solve with `matrix` or its adjoint.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `matrix`.
+  Shape is `[M, K]`. If `adjoint` is `False` then `output` that solves
+  `matrix` * `output` = `rhs`. If `adjoint` is `True` then `output` that solves
+  `adjoint(matrix)` * `output` = `rhs`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.maximum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.maximum.md
new file mode 100644
index 0000000000..309946f435
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.maximum.md
@@ -0,0 +1,15 @@
+### `tf.maximum(x, y, name=None)` {#maximum}
+
+Returns the max of x and y (i.e. x > y ? x : y) element-wise, broadcasts.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.merge_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.merge_summary.md
deleted file mode 100644
index b61a501c2d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.merge_summary.md
+++ /dev/null
@@ -1,26 +0,0 @@
-### `tf.merge_summary(inputs, collections=None, name=None)` {#merge_summary}
-
-Merges summaries.
-
-This op creates a
-[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-protocol buffer that contains the union of all the values in the input
-summaries.
-
-When the Op is run, it reports an `InvalidArgument` error if multiple values
-in the summaries to merge use the same tag.
-
-##### Args:
-
-
-*  <b>`inputs`</b>: A list of `string` `Tensor` objects containing serialized `Summary`
-    protocol buffers.
-*  <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
-    added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A scalar `Tensor` of type `string`. The serialized `Summary` protocol
-  buffer resulting from the merging.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.mul.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.mul.md
new file mode 100644
index 0000000000..3d6fa56864
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.mul.md
@@ -0,0 +1,15 @@
+### `tf.mul(x, y, name=None)` {#mul}
+
+Returns x * y element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.atrous_conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.atrous_conv2d.md
new file mode 100644
index 0000000000..cf4c473689
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.atrous_conv2d.md
@@ -0,0 +1,107 @@
+### `tf.nn.atrous_conv2d(value, filters, rate, padding, name=None)` {#atrous_conv2d}
+
+Atrous convolution (a.k.a. convolution with holes or dilated convolution).
+
+Computes a 2-D atrous convolution, also known as convolution with holes or
+dilated convolution, given 4-D `value` and `filters` tensors. If the `rate`
+parameter is equal to one, it performs regular 2-D convolution. If the `rate`
+parameter is greater than one, it performs convolution with holes, sampling
+the input values every `rate` pixels in the `height` and `width` dimensions.
+This is equivalent to convolving the input with a set of upsampled filters,
+produced by inserting `rate - 1` zeros between two consecutive values of the
+filters along the `height` and `width` dimensions, hence the name atrous
+convolution or convolution with holes (the French word trous means holes in
+English).
+
+More specifically:
+
+    output[b, i, j, k] = sum_{di, dj, q} filters[di, dj, q, k] *
+          value[b, i + rate * di, j + rate * dj, q]
+
+Atrous convolution allows us to explicitly control how densely to compute
+feature responses in fully convolutional networks. Used in conjunction with
+bilinear interpolation, it offers an alternative to `conv2d_transpose` in
+dense prediction tasks such as semantic image segmentation, optical flow
+computation, or depth estimation. It also allows us to effectively enlarge
+the field of view of filters without increasing the number of parameters or
+the amount of computation.
+
+For a description of atrous convolution and how it can be used for dense
+feature extraction, please see: [Semantic Image Segmentation with Deep
+Convolutional Nets and Fully Connected CRFs](http://arxiv.org/abs/1412.7062).
+The same operation is investigated further in [Multi-Scale Context Aggregation
+by Dilated Convolutions](http://arxiv.org/abs/1511.07122). Previous works
+that effectively use atrous convolution in different ways are, among others,
+[OverFeat: Integrated Recognition, Localization and Detection using
+Convolutional Networks](http://arxiv.org/abs/1312.6229) and [Fast Image
+Scanning with Deep Max-Pooling Convolutional Neural Networks]
+(http://arxiv.org/abs/1302.1700). Atrous convolution is also closely related
+to the so-called noble identities in multi-rate signal processing.
+
+There are many different ways to implement atrous convolution (see the refs
+above). The implementation here reduces
+
+    atrous_conv2d(value, filters, rate, padding=padding)
+
+to the following three operations:
+
+    paddings = ...
+    net = space_to_batch(value, paddings, block_size=rate)
+    net = conv2d(net, filters, strides=[1, 1, 1, 1], padding="VALID")
+    crops = ...
+    net = batch_to_space(net, crops, block_size=rate)
+
+Advanced usage. Note the following optimization: A sequence of `atrous_conv2d`
+operations with identical `rate` parameters, 'SAME' `padding`, and filters
+with odd heights/ widths:
+
+    net = atrous_conv2d(net, filters1, rate, padding="SAME")
+    net = atrous_conv2d(net, filters2, rate, padding="SAME")
+    ...
+    net = atrous_conv2d(net, filtersK, rate, padding="SAME")
+
+can be equivalently performed cheaper in terms of computation and memory as:
+
+    pad = ...  # padding so that the input dims are multiples of rate
+    net = space_to_batch(net, paddings=pad, block_size=rate)
+    net = conv2d(net, filters1, strides=[1, 1, 1, 1], padding="SAME")
+    net = conv2d(net, filters2, strides=[1, 1, 1, 1], padding="SAME")
+    ...
+    net = conv2d(net, filtersK, strides=[1, 1, 1, 1], padding="SAME")
+    net = batch_to_space(net, crops=pad, block_size=rate)
+
+because a pair of consecutive `space_to_batch` and `batch_to_space` ops with
+the same `block_size` cancel out when their respective `paddings` and `crops`
+inputs are identical.
+
+##### Args:
+
+
+*  <b>`value`</b>: A 4-D `Tensor` of type `float`. It needs to be in the default "NHWC"
+    format. Its shape is `[batch, in_height, in_width, in_channels]`.
+*  <b>`filters`</b>: A 4-D `Tensor` with the same type as `value` and shape
+    `[filter_height, filter_width, in_channels, out_channels]`. `filters`'
+    `in_channels` dimension must match that of `value`. Atrous convolution is
+    equivalent to standard convolution with upsampled filters with effective
+    height `filter_height + (filter_height - 1) * (rate - 1)` and effective
+    width `filter_width + (filter_width - 1) * (rate - 1)`, produced by
+    inserting `rate - 1` zeros along consecutive elements across the
+    `filters`' spatial dimensions.
+*  <b>`rate`</b>: A positive int32. The stride with which we sample input values across
+    the `height` and `width` dimensions. Equivalently, the rate by which we
+    upsample the filter values by inserting zeros across the `height` and
+    `width` dimensions. In the literature, the same parameter is sometimes
+    called `input stride` or `dilation`.
+*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
+*  <b>`name`</b>: Optional name for the returned tensor.
+
+##### Returns:
+
+  A `Tensor` with the same type as `value`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If input/output depth does not match `filters`' shape, or if
+    padding is other than `'VALID'` or `'SAME'`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.conv2d_transpose.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.conv2d_transpose.md
new file mode 100644
index 0000000000..ee459ae0e6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.conv2d_transpose.md
@@ -0,0 +1,34 @@
+### `tf.nn.conv2d_transpose(value, filter, output_shape, strides, padding='SAME', name=None)` {#conv2d_transpose}
+
+The transpose of `conv2d`.
+
+This operation is sometimes called "deconvolution" after [Deconvolutional
+Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is
+actually the transpose (gradient) of `conv2d` rather than an actual
+deconvolution.
+
+##### Args:
+
+
+*  <b>`value`</b>: A 4-D `Tensor` of type `float` and shape
+    `[batch, height, width, in_channels]`.
+*  <b>`filter`</b>: A 4-D `Tensor` with the same type as `value` and shape
+    `[height, width, output_channels, in_channels]`.  `filter`'s
+    `in_channels` dimension must match that of `value`.
+*  <b>`output_shape`</b>: A 1-D `Tensor` representing the output shape of the
+    deconvolution op.
+*  <b>`strides`</b>: A list of ints. The stride of the sliding window for each
+    dimension of the input tensor.
+*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
+*  <b>`name`</b>: Optional name for the returned tensor.
+
+##### Returns:
+
+  A `Tensor` with the same type as `value`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If input/output depth does not match `filter`'s shape, or if
+    padding is other than `'VALID'` or `'SAME'`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.dropout.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.dropout.md
new file mode 100644
index 0000000000..4f2b7c0214
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.dropout.md
@@ -0,0 +1,38 @@
+### `tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)` {#dropout}
+
+Computes dropout.
+
+With probability `keep_prob`, outputs the input element scaled up by
+`1 / keep_prob`, otherwise outputs `0`.  The scaling is so that the expected
+sum is unchanged.
+
+By default, each element is kept or dropped independently.  If `noise_shape`
+is specified, it must be
+[broadcastable](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+to the shape of `x`, and only dimensions with `noise_shape[i] == shape(x)[i]`
+will make independent decisions.  For example, if `shape(x) = [k, l, m, n]`
+and `noise_shape = [k, 1, 1, n]`, each batch and channel component will be
+kept independently and each row and column will be kept or not kept together.
+
+##### Args:
+
+
+*  <b>`x`</b>: A tensor.
+*  <b>`keep_prob`</b>: A scalar `Tensor` with the same type as x. The probability
+    that each element is kept.
+*  <b>`noise_shape`</b>: A 1-D `Tensor` of type `int32`, representing the
+    shape for randomly generated keep/drop flags.
+*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  A Tensor of the same shape of `x`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `keep_prob` is not in `(0, 1]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.l2_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.l2_loss.md
deleted file mode 100644
index fd648ca642..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.l2_loss.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.nn.l2_loss(t, name=None)` {#l2_loss}
-
-L2 Loss.
-
-Computes half the L2 norm of a tensor without the `sqrt`:
-
-    output = sum(t ** 2) / 2
-
-##### Args:
-
-
-*  <b>`t`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-    Typically 2-D, but may have any dimensions.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `t`. 0-D.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.max_pool_with_argmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.max_pool_with_argmax.md
deleted file mode 100644
index 0bf84c16d0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.max_pool_with_argmax.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.nn.max_pool_with_argmax(input, ksize, strides, padding, Targmax=None, name=None)` {#max_pool_with_argmax}
-
-Performs max pooling on the input and outputs both max values and indices.
-
-The indices in `argmax` are flattened, so that a maximum value at position
-`[b, y, x, c]` becomes flattened index
-`((b * height + y) * width + x) * channels + c`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `float32`.
-    4-D with shape `[batch, height, width, channels]`.  Input to pool over.
-*  <b>`ksize`</b>: A list of `ints` that has length `>= 4`.
-    The size of the window for each dimension of the input tensor.
-*  <b>`strides`</b>: A list of `ints` that has length `>= 4`.
-    The stride of the sliding window for each dimension of the
-    input tensor.
-*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
-    The type of padding algorithm to use.
-*  <b>`Targmax`</b>: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int64`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of `Tensor` objects (output, argmax).
-
-*  <b>`output`</b>: A `Tensor` of type `float32`. The max pooled output tensor.
-*  <b>`argmax`</b>: A `Tensor` of type `Targmax`. 4-D.  The flattened indices of the max values chosen for each output.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.sigmoid_cross_entropy_with_logits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.sigmoid_cross_entropy_with_logits.md
new file mode 100644
index 0000000000..c449554fb8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.sigmoid_cross_entropy_with_logits.md
@@ -0,0 +1,48 @@
+### `tf.nn.sigmoid_cross_entropy_with_logits(logits, targets, name=None)` {#sigmoid_cross_entropy_with_logits}
+
+Computes sigmoid cross entropy given `logits`.
+
+Measures the probability error in discrete classification tasks in which each
+class is independent and not mutually exclusive.  For instance, one could
+perform multilabel classification where a picture can contain both an elephant
+and a dog at the same time.
+
+For brevity, let `x = logits`, `z = targets`.  The logistic loss is
+
+      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
+    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
+    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
+    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
+    = (1 - z) * x + log(1 + exp(-x))
+    = x - x * z + log(1 + exp(-x))
+
+For x < 0, to avoid overflow in exp(-x), we reformulate the above
+
+      x - x * z + log(1 + exp(-x))
+    = log(exp(x)) - x * z + log(1 + exp(-x))
+    = - x * z + log(1 + exp(x))
+
+Hence, to ensure stability and avoid overflow, the implementation uses this
+equivalent formulation
+
+    max(x, 0) - x * z + log(1 + exp(-abs(x)))
+
+`logits` and `targets` must have the same type and shape.
+
+##### Args:
+
+
+*  <b>`logits`</b>: A `Tensor` of type `float32` or `float64`.
+*  <b>`targets`</b>: A `Tensor` of the same type and shape as `logits`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of the same shape as `logits` with the componentwise
+  logistic losses.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `logits` and `targets` do not have the same shape.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.softmax_cross_entropy_with_logits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.softmax_cross_entropy_with_logits.md
new file mode 100644
index 0000000000..d6054c49ac
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.softmax_cross_entropy_with_logits.md
@@ -0,0 +1,36 @@
+### `tf.nn.softmax_cross_entropy_with_logits(logits, labels, name=None)` {#softmax_cross_entropy_with_logits}
+
+Computes softmax cross entropy between `logits` and `labels`.
+
+Measures the probability error in discrete classification tasks in which the
+classes are mutually exclusive (each entry is in exactly one class).  For
+example, each CIFAR-10 image is labeled with one and only one label: an image
+can be a dog or a truck, but not both.
+
+**NOTE:**  While the classes are mutually exclusive, their probabilities
+need not be.  All that is required is that each row of `labels` is
+a valid probability distribution.  If they are not, the computation of the
+gradient will be incorrect.
+
+If using exclusive `labels` (wherein one and only
+one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`.
+
+**WARNING:** This op expects unscaled logits, since it performs a `softmax`
+on `logits` internally for efficiency.  Do not call this op with the
+output of `softmax`, as it will produce incorrect results.
+
+`logits` and `labels` must have the same shape `[batch_size, num_classes]`
+and the same dtype (either `float32` or `float64`).
+
+##### Args:
+
+
+*  <b>`logits`</b>: Unscaled log probabilities.
+*  <b>`labels`</b>: Each row `labels[i]` must be a valid probability distribution.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the
+  softmax cross entropy loss.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.sparse_softmax_cross_entropy_with_logits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.sparse_softmax_cross_entropy_with_logits.md
new file mode 100644
index 0000000000..6d53d84c5b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.sparse_softmax_cross_entropy_with_logits.md
@@ -0,0 +1,38 @@
+### `tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels, name=None)` {#sparse_softmax_cross_entropy_with_logits}
+
+Computes sparse softmax cross entropy between `logits` and `labels`.
+
+Measures the probability error in discrete classification tasks in which the
+classes are mutually exclusive (each entry is in exactly one class).  For
+example, each CIFAR-10 image is labeled with one and only one label: an image
+can be a dog or a truck, but not both.
+
+**NOTE:**  For this operation, the probability of a given label is considered
+exclusive.  That is, soft classes are not allowed, and the `labels` vector
+must provide a single specific index for the true class for each row of
+`logits` (each minibatch entry).  For soft softmax classification with
+a probability distribution for each entry, see
+`softmax_cross_entropy_with_logits`.
+
+**WARNING:** This op expects unscaled logits, since it performs a softmax
+on `logits` internally for efficiency.  Do not call this op with the
+output of `softmax`, as it will produce incorrect results.
+
+`logits` must have the shape `[batch_size, num_classes]`
+and dtype `float32` or `float64`.
+
+`labels` must have the shape `[batch_size]` and dtype `int32` or `int64`.
+
+##### Args:
+
+
+*  <b>`logits`</b>: Unscaled log probabilities.
+*  <b>`labels`</b>: Each entry `labels[i]` must be an index in `[0, num_classes)`. Other
+    values will result in a loss of 0, but incorrect gradient computations.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the
+  softmax cross entropy loss.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.weighted_cross_entropy_with_logits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.weighted_cross_entropy_with_logits.md
new file mode 100644
index 0000000000..697de67936
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.nn.weighted_cross_entropy_with_logits.md
@@ -0,0 +1,52 @@
+### `tf.nn.weighted_cross_entropy_with_logits(logits, targets, pos_weight, name=None)` {#weighted_cross_entropy_with_logits}
+
+Computes a weighted cross entropy.
+
+This is like `sigmoid_cross_entropy_with_logits()` except that `pos_weight`,
+allows one to trade off recall and precision by up- or down-weighting the
+cost of a positive error relative to a negative error.
+
+The usual cross-entropy cost is defined as:
+
+  targets * -log(sigmoid(logits)) + (1 - targets) * -log(1 - sigmoid(logits))
+
+The argument `pos_weight` is used as a multiplier for the positive targets:
+
+  targets * -log(sigmoid(logits)) * pos_weight +
+      (1 - targets) * -log(1 - sigmoid(logits))
+
+For brevity, let `x = logits`, `z = targets`, `q = pos_weight`.
+The loss is:
+
+      qz * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
+    = qz * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
+    = qz * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
+    = qz * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
+    = (1 - z) * x + (qz +  1 - z) * log(1 + exp(-x))
+    = (1 - z) * x + (1 + (q - 1) * z) * log(1 + exp(-x))
+
+Setting `l = (1 + (q - 1) * z)`, to ensure stability and avoid overflow,
+the implementation uses
+
+    (1 - z) * x + l * (log(1 + exp(-abs(x))) + max(-x, 0))
+
+`logits` and `targets` must have the same type and shape.
+
+##### Args:
+
+
+*  <b>`logits`</b>: A `Tensor` of type `float32` or `float64`.
+*  <b>`targets`</b>: A `Tensor` of the same type and shape as `logits`.
+*  <b>`pos_weight`</b>: A coefficient to use on the positive examples.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of the same shape as `logits` with the componentwise
+  weightedlogistic losses.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `logits` and `targets` do not have the same shape.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.no_regularizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.no_regularizer.md
deleted file mode 100644
index cb55675641..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.no_regularizer.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.no_regularizer(_)` {#no_regularizer}
-
-Use this function to prevent regularization of variables.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.pack.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.pack.md
new file mode 100644
index 0000000000..75a5fbe15c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.pack.md
@@ -0,0 +1,23 @@
+### `tf.pack(values, name='pack')` {#pack}
+
+Packs a list of rank-`R` tensors into one rank-`(R+1)` tensor.
+
+Packs tensors in `values` into a tensor with rank one higher than each tensor
+in `values` and shape `[len(values)] + values[0].shape`. The output satisfies
+`output[i, ...] = values[i][...]`.
+
+This is the opposite of unpack.  The numpy equivalent is
+
+    tf.pack([x, y, z]) = np.asarray([x, y, z])
+
+##### Args:
+
+
+*  <b>`values`</b>: A list of `Tensor` objects with the same shape and type.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+
+*  <b>`output`</b>: A packed `Tensor` with the same type as `values`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.placeholder.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.placeholder.md
deleted file mode 100644
index 28cdc11cce..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.placeholder.md
+++ /dev/null
@@ -1,34 +0,0 @@
-### `tf.placeholder(dtype, shape=None, name=None)` {#placeholder}
-
-Inserts a placeholder for a tensor that will be always fed.
-
-**Important**: This tensor will produce an error if evaluated. Its value must
-be fed using the `feed_dict` optional argument to `Session.run()`,
-`Tensor.eval()`, or `Operation.run()`.
-
-For example:
-
-```python
-x = tf.placeholder(tf.float32, shape=(1024, 1024))
-y = tf.matmul(x, x)
-
-with tf.Session() as sess:
-  print(sess.run(y))  # ERROR: will fail because x was not fed.
-
-  rand_array = np.random.rand(1024, 1024)
-  print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.
-```
-
-##### Args:
-
-
-*  <b>`dtype`</b>: The type of elements in the tensor to be fed.
-*  <b>`shape`</b>: The shape of the tensor to be fed (optional). If the shape is not
-    specified, you can feed a tensor of any shape.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` that may be used as a handle for feeding a value, but not
-  evaluated directly.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.pow.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.pow.md
new file mode 100644
index 0000000000..8588b72fb8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.pow.md
@@ -0,0 +1,24 @@
+### `tf.pow(x, y, name=None)` {#pow}
+
+Computes the power of one value to another.
+
+Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for
+corresponding elements in `x` and `y`. For example:
+
+```
+# tensor 'x' is [[2, 2], [3, 3]]
+# tensor 'y' is [[8, 16], [2, 3]]
+tf.pow(x, y) ==> [[256, 65536], [9, 27]]
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` of type `float`, `double`, `int32`, `complex64`, or `int64`.
+*  <b>`y`</b>: A `Tensor` of type `float`, `double`, `int32`, `complex64`, or `int64`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.py_func.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.py_func.md
new file mode 100644
index 0000000000..c115d21781
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.py_func.md
@@ -0,0 +1,31 @@
+### `tf.py_func(func, inp, Tout, name=None)` {#py_func}
+
+Wraps a python function and uses it as a tensorflow op.
+
+Given a python function `func`, which takes numpy arrays as its
+inputs and returns numpy arrays as its outputs. E.g.,
+
+```python
+def my_func(x):
+  # x will be a numpy array with the contents of the placeholder below
+  return np.sinh(x)
+inp = tf.placeholder(tf.float32, [...])
+y = py_func(my_func, [inp], [tf.float32])
+```
+
+The above snippet constructs a tf graph which invokes a numpy
+sinh(x) as an op in the graph.
+
+##### Args:
+
+
+*  <b>`func`</b>: A python function.
+*  <b>`inp`</b>: A list of `Tensor`.
+*  <b>`Tout`</b>: A list of tensorflow data types indicating what `func`
+        returns.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A list of `Tensor` which `func` computes.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.random_uniform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.random_uniform.md
deleted file mode 100644
index 517bdd98c4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.random_uniform.md
+++ /dev/null
@@ -1,41 +0,0 @@
-### `tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)` {#random_uniform}
-
-Outputs random values from a uniform distribution.
-
-The generated values follow a uniform distribution in the range
-`[minval, maxval)`. The lower bound `minval` is included in the range, while
-the upper bound `maxval` is excluded.
-
-For floats, the default range is `[0, 1)`.  For ints, at least `maxval` must
-be specified explicitly.
-
-In the integer case, the random integers are slightly biased unless
-`maxval - minval` is an exact power of two.  The bias is small for values of
-`maxval - minval` significantly smaller than the range of the output (either
-`2**32` or `2**64`).
-
-##### Args:
-
-
-*  <b>`shape`</b>: A 1-D integer Tensor or Python array. The shape of the output tensor.
-*  <b>`minval`</b>: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
-    range of random values to generate.  Defaults to 0.
-*  <b>`maxval`</b>: A 0-D Tensor or Python value of type `dtype`. The upper bound on
-    the range of random values to generate.  Defaults to 1 if `dtype` is
-    floating point.
-*  <b>`dtype`</b>: The type of the output: `float32`, `float64`, `int32`, or `int64`.
-*  <b>`seed`</b>: A Python integer. Used to create a random seed for the distribution.
-    See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tensor of the specified shape filled with random uniform values.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `dtype` is integral and `maxval` is not specified.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.rank.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.rank.md
deleted file mode 100644
index 8d8fdb4af4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.rank.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.rank(input, name=None)` {#rank}
-
-Returns the rank of a tensor.
-
-This operation returns an integer representing the rank of `input`.
-
-For example:
-
-```prettyprint
-# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
-# shape of tensor 't' is [2, 2, 3]
-rank(t) ==> 3
-```
-
-**Note**: The rank of a tensor is not the same as the rank of a matrix. The rank
-of a tensor is the number of indices required to uniquely select each element
-of the tensor. Rank is also known as "order", "degree", or "ndims."
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `int32`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.real.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.real.md
deleted file mode 100644
index 3be066f588..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.real.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.real(input, name=None)` {#real}
-
-Returns the real part of a complex number.
-
-Given a tensor `input` of complex numbers, this operation returns a tensor of
-type `float` or `double` that is the real part of each element in `input`.
-All elements in `input` must be complex numbers of the form \(a + bj\),
-where *a* is the real part returned by this operation and *b* is the
-imaginary part.
-
-For example:
-
-```
-# tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
-tf.real(input) ==> [-2.25, 3.25]
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `complex64`,
-       `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `float` or `double`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reduce_prod.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reduce_prod.md
new file mode 100644
index 0000000000..a87daa33fb
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reduce_prod.md
@@ -0,0 +1,25 @@
+### `tf.reduce_prod(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_prod}
+
+Computes the product of elements across dimensions of a tensor.
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.report_uninitialized_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.report_uninitialized_variables.md
deleted file mode 100644
index 35536e65d9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.report_uninitialized_variables.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.report_uninitialized_variables(var_list=None, name='report_uninitialized_variables')` {#report_uninitialized_variables}
-
-Adds ops to list the names of uninitialized variables.
-
-When run, it returns a 1-D tensor containing the names of uninitialized
-variables if there are any, or an empty array if there are none.
-
-##### Args:
-
-
-*  <b>`var_list`</b>: List of `Variable` objects to check. Defaults to the
-    value of `all_variables() + local_variables()`
-*  <b>`name`</b>: Optional name of the `Operation`.
-
-##### Returns:
-
-  A 1-D tensor containing names of the unintialized variables, or an empty 1-D
-  tensor if there are no variables or no uninitialized variables.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reverse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reverse.md
new file mode 100644
index 0000000000..e316d5faae
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reverse.md
@@ -0,0 +1,61 @@
+### `tf.reverse(tensor, dims, name=None)` {#reverse}
+
+Reverses specific dimensions of a tensor.
+
+Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
+of `tensor`, this operation reverses each dimension i of `tensor` where
+`dims[i]` is `True`.
+
+`tensor` can have up to 8 dimensions. The number of dimensions
+of `tensor` must equal the number of elements in `dims`. In other words:
+
+`rank(tensor) = size(dims)`
+
+For example:
+
+```prettyprint
+# tensor 't' is [[[[ 0,  1,  2,  3],
+#                  [ 4,  5,  6,  7],
+#                  [ 8,  9, 10, 11]],
+#                 [[12, 13, 14, 15],
+#                  [16, 17, 18, 19],
+#                  [20, 21, 22, 23]]]]
+# tensor 't' shape is [1, 2, 3, 4]
+
+# 'dims' is [False, False, False, True]
+reverse(t, dims) ==> [[[[ 3,  2,  1,  0],
+                        [ 7,  6,  5,  4],
+                        [ 11, 10, 9, 8]],
+                       [[15, 14, 13, 12],
+                        [19, 18, 17, 16],
+                        [23, 22, 21, 20]]]]
+
+# 'dims' is [False, True, False, False]
+reverse(t, dims) ==> [[[[12, 13, 14, 15],
+                        [16, 17, 18, 19],
+                        [20, 21, 22, 23]
+                       [[ 0,  1,  2,  3],
+                        [ 4,  5,  6,  7],
+                        [ 8,  9, 10, 11]]]]
+
+# 'dims' is [False, False, True, False]
+reverse(t, dims) ==> [[[[8, 9, 10, 11],
+                        [4, 5, 6, 7],
+                        [0, 1, 2, 3]]
+                       [[20, 21, 22, 23],
+                        [16, 17, 18, 19],
+                        [12, 13, 14, 15]]]]
+```
+
+##### Args:
+
+
+*  <b>`tensor`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int32`, `bool`, `float32`, `float64`.
+    Up to 8-D.
+*  <b>`dims`</b>: A `Tensor` of type `bool`. 1-D. The dimensions to reverse.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `tensor`. The same shape as `tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.scan.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.scan.md
new file mode 100644
index 0000000000..6ea0ac677b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.scan.md
@@ -0,0 +1,44 @@
+### `tf.scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#scan}
+
+scan on the list of tensors unpacked from `elems` on dimension 0.
+
+This scan operator repeatedly applies the callable `fn` to a sequence
+of elements from first to last. The elements are made of the tensors
+unpacked from `elems` on dimension 0. The callable fn takes two tensors as
+arguments. The first argument is the accumulated value computed from the
+preceding invocation of fn. If `initializer` is None, `elems` must contain
+at least one element, and its first element is used as the initializer.
+
+Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
+of the result tensor is `[len(values)] + fn(initializer, values[0]).shape`.
+
+##### Args:
+
+
+*  <b>`fn`</b>: The callable to be performed.
+*  <b>`elems`</b>: A tensor to be unpacked on dimension 0.
+*  <b>`initializer`</b>: (optional) The initial value for the accumulator.
+*  <b>`parallel_iterations`</b>: (optional) The number of iterations allowed to run
+                       in parallel.
+*  <b>`back_prop`</b>: (optional) True enables back propagation.
+*  <b>`swap_memory`</b>: (optional) True enables GPU-CPU memory swapping.
+*  <b>`name`</b>: (optional) Name prefix for the returned tensors.
+
+##### Returns:
+
+  A tensor that packs the results of applying `fn` to the list of tensors
+  unpacked from `elems`, from first to last.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `fn` is not callable.
+
+##### Example:
+
+  ```python
+  elems = [1, 2, 3, 4, 5, 6]
+  sum = scan(lambda a, x: a + x, elems)
+  # sum == [1, 3, 6, 10, 15, 21]
+  ```
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.segment_prod.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.segment_prod.md
new file mode 100644
index 0000000000..c9ed2759cf
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.segment_prod.md
@@ -0,0 +1,31 @@
+### `tf.segment_prod(data, segment_ids, name=None)` {#segment_prod}
+
+Computes the product along segments of a tensor.
+
+Read [the section on
+Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
+of segments.
+
+Computes a tensor such that
+\\(output_i = \prod_j data_j\\) where the product is over `j` such
+that `segment_ids[j] == i`.
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/SegmentProd.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    A 1-D tensor whose rank is equal to the rank of `data`'s
+    first dimension.  Values should be sorted and can be repeated.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `data`.
+  Has same shape as data, except for dimension 0 which
+  has size `k`, the number of segments.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.self_adjoint_eig.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.self_adjoint_eig.md
deleted file mode 100644
index efbc0cd3be..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.self_adjoint_eig.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.self_adjoint_eig(input, name=None)` {#self_adjoint_eig}
-
-Calculates the Eigen Decomposition of a square Self-Adjoint matrix.
-
-Only the lower-triangular part of the input will be used in this case. The
-upper-triangular part will not be read.
-
-The result is a M+1 x M matrix whose first row is the eigenvalues, and
-subsequent rows are eigenvectors.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
-    Shape is `[M, M]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`. Shape is `[M+1, M]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.shape.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.shape.md
deleted file mode 100644
index 4262f41a3d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.shape.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.shape(input, name=None)` {#shape}
-
-Returns the shape of a tensor.
-
-This operation returns a 1-D integer tensor representing the shape of `input`.
-
-For example:
-
-```prettyprint
-# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
-shape(t) ==> [2, 2, 3]
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `int32`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.shape_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.shape_n.md
new file mode 100644
index 0000000000..a229253406
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.shape_n.md
@@ -0,0 +1,16 @@
+### `tf.shape_n(input, name=None)` {#shape_n}
+
+Returns shape of tensors.
+
+This operation returns N 1-D integer tensors representing shape of `input[i]s`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A list of at least 1 `Tensor` objects of the same type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A list with the same number of `Tensor` objects as `input` of `Tensor` objects of type `int32`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.squared_difference.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.squared_difference.md
new file mode 100644
index 0000000000..d6bb175669
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.squared_difference.md
@@ -0,0 +1,15 @@
+### `tf.squared_difference(x, y, name=None)` {#squared_difference}
+
+Returns (x - y)(x - y) element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.string_to_hash_bucket.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.string_to_hash_bucket.md
deleted file mode 100644
index 941d50e139..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.string_to_hash_bucket.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.string_to_hash_bucket(string_tensor, num_buckets, name=None)` {#string_to_hash_bucket}
-
-Converts each string in the input Tensor to its hash mod by a number of buckets.
-
-The hash function is deterministic on the content of the string within the
-process.
-
-Note that the hash function may change from time to time.
-
-##### Args:
-
-
-*  <b>`string_tensor`</b>: A `Tensor` of type `string`.
-*  <b>`num_buckets`</b>: An `int` that is `>= 1`. The number of buckets.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `int64`.
-  A Tensor of the same shape as the input `string_tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.sub.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.sub.md
new file mode 100644
index 0000000000..2d1da0f0b9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.sub.md
@@ -0,0 +1,15 @@
+### `tf.sub(x, y, name=None)` {#sub}
+
+Returns x - y element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.test.get_temp_dir.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.test.get_temp_dir.md
deleted file mode 100644
index e36d6163a7..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.test.get_temp_dir.md
+++ /dev/null
@@ -1,10 +0,0 @@
-### `tf.test.get_temp_dir()` {#get_temp_dir}
-
-Returns a temporary directory for use during tests.
-
-There is no need to delete the directory after the test.
-
-##### Returns:
-
-  The temporary directory.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.test.is_built_with_cuda.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.test.is_built_with_cuda.md
new file mode 100644
index 0000000000..51e3d97d8c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.test.is_built_with_cuda.md
@@ -0,0 +1,4 @@
+### `tf.test.is_built_with_cuda()` {#is_built_with_cuda}
+
+Returns whether TensorFlow was built with CUDA (GPU) support.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.tile.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.tile.md
deleted file mode 100644
index 650f1f7eb8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.tile.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.tile(input, multiples, name=None)` {#tile}
-
-Constructs a tensor by tiling a given tensor.
-
-This operation creates a new tensor by replicating `input` `multiples` times.
-The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
-and the values of `input` are replicated `multiples[i]` times along the 'i'th
-dimension. For example, tiling `[a b c d]` by `[2]` produces
-`[a b c d a b c d]`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. 1-D or higher.
-*  <b>`multiples`</b>: A `Tensor` of type `int32`.
-    1-D. Length must be the same as the number of dimensions in `input`
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.to_int64.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.to_int64.md
new file mode 100644
index 0000000000..0762822b3d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.to_int64.md
@@ -0,0 +1,19 @@
+### `tf.to_int64(x, name='ToInt64')` {#to_int64}
+
+Casts a tensor to type `int64`.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` or `SparseTensor` with same shape as `x` with type `int64`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `x` cannot be cast to the `int64`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.AdadeltaOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.AdadeltaOptimizer.md
deleted file mode 100644
index 9a14c50dc8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.AdadeltaOptimizer.md
+++ /dev/null
@@ -1,23 +0,0 @@
-Optimizer that implements the Adadelta algorithm. 
-
-See [M. D. Zeiler](http://arxiv.org/abs/1212.5701)
-([pdf](http://arxiv.org/pdf/1212.5701v1.pdf))
-
-- - -
-
-#### `tf.train.AdadeltaOptimizer.__init__(learning_rate=0.001, rho=0.95, epsilon=1e-08, use_locking=False, name='Adadelta')` {#AdadeltaOptimizer.__init__}
-
-Construct a new Adadelta optimizer.
-
-##### Args:
-
-
-*  <b>`learning_rate`</b>: A `Tensor` or a floating point value. The learning rate.
-*  <b>`rho`</b>: A `Tensor` or a floating point value. The decay rate.
-*  <b>`epsilon`</b>: A `Tensor` or a floating point value.  A constant epsilon used
-           to better conditioning the grad update.
-*  <b>`use_locking`</b>: If `True` use locks for update operations.
-*  <b>`name`</b>: Optional name prefix for the operations created when applying
-    gradients.  Defaults to "Adadelta".
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.AdagradOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.AdagradOptimizer.md
new file mode 100644
index 0000000000..35e416386e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.AdagradOptimizer.md
@@ -0,0 +1,26 @@
+Optimizer that implements the Adagrad algorithm.
+
+See this [paper](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).
+
+- - -
+
+#### `tf.train.AdagradOptimizer.__init__(learning_rate, initial_accumulator_value=0.1, use_locking=False, name='Adagrad')` {#AdagradOptimizer.__init__}
+
+Construct a new Adagrad optimizer.
+
+##### Args:
+
+
+*  <b>`learning_rate`</b>: A `Tensor` or a floating point value.  The learning rate.
+*  <b>`initial_accumulator_value`</b>: A floating point value.
+    Starting value for the accumulators, must be positive.
+*  <b>`use_locking`</b>: If `True` use locks for update operations.
+*  <b>`name`</b>: Optional name prefix for the operations created when applying
+    gradients.  Defaults to "Adagrad".
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the `initial_accumulator_value` is invalid.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.GradientDescentOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.GradientDescentOptimizer.md
new file mode 100644
index 0000000000..99a5f1f0b1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.GradientDescentOptimizer.md
@@ -0,0 +1,18 @@
+Optimizer that implements the gradient descent algorithm.
+
+- - -
+
+#### `tf.train.GradientDescentOptimizer.__init__(learning_rate, use_locking=False, name='GradientDescent')` {#GradientDescentOptimizer.__init__}
+
+Construct a new gradient descent optimizer.
+
+##### Args:
+
+
+*  <b>`learning_rate`</b>: A Tensor or a floating point value.  The learning
+    rate to use.
+*  <b>`use_locking`</b>: If True use locks for update operations.
+*  <b>`name`</b>: Optional name prefix for the operations created when applying
+    gradients. Defaults to "GradientDescent".
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.LooperThread.loop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.LooperThread.loop.md
deleted file mode 100644
index 6665ca7369..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.LooperThread.loop.md
+++ /dev/null
@@ -1,22 +0,0 @@
-#### `tf.train.LooperThread.loop(coord, timer_interval_secs, target, args=None, kwargs=None)` {#LooperThread.loop}
-
-Start a LooperThread that calls a function periodically.
-
-If `timer_interval_secs` is None the thread calls `target(args)`
-repeatedly.  Otherwise `target(args)` is called every `timer_interval_secs`
-seconds.  The thread terminates when a stop of the coordinator is
-requested.
-
-##### Args:
-
-
-*  <b>`coord`</b>: A Coordinator.
-*  <b>`timer_interval_secs`</b>: Number. Time boundaries at which to call `target`.
-*  <b>`target`</b>: A callable object.
-*  <b>`args`</b>: Optional arguments to pass to `target` when calling it.
-*  <b>`kwargs`</b>: Optional keyword arguments to pass to `target` when calling it.
-
-##### Returns:
-
-  The started thread.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.global_step.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.global_step.md
new file mode 100644
index 0000000000..a53175be6a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.global_step.md
@@ -0,0 +1,27 @@
+### `tf.train.global_step(sess, global_step_tensor)` {#global_step}
+
+Small helper to get the global step.
+
+```python
+# Creates a variable to hold the global_step.
+global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
+# Creates a session.
+sess = tf.Session()
+# Initializes the variable.
+sess.run(global_step_tensor.initializer)
+print('global_step: %s' % tf.train.global_step(sess, global_step_tensor))
+
+global_step: 10
+```
+
+##### Args:
+
+
+*  <b>`sess`</b>: A TensorFlow `Session` object.
+*  <b>`global_step_tensor`</b>: `Tensor` or the `name` of the operation that contains
+    the global step.
+
+##### Returns:
+
+  The global step value.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.match_filenames_once.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.match_filenames_once.md
deleted file mode 100644
index 6c84221cc5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.match_filenames_once.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.train.match_filenames_once(pattern, name=None)` {#match_filenames_once}
-
-Save the list of files matching pattern, so it is only computed once.
-
-##### Args:
-
-
-*  <b>`pattern`</b>: A file pattern (glob).
-*  <b>`name`</b>: A name for the operations (optional).
-
-##### Returns:
-
-  A variable that is initialized to the list of files matching pattern.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.replica_device_setter.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.replica_device_setter.md
deleted file mode 100644
index a5ea200562..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.replica_device_setter.md
+++ /dev/null
@@ -1,50 +0,0 @@
-### `tf.train.replica_device_setter(ps_tasks=0, ps_device='/job:ps', worker_device='/job:worker', merge_devices=True, cluster=None, ps_ops=None)` {#replica_device_setter}
-
-Return a `device function` to use when building a Graph for replicas.
-
-Device Functions are used in `with tf.device(device_function):` statement to
-automatically assign devices to `Operation` objects as they are constructed,
-Device constraints are added from the inner-most context first, working
-outwards. The merging behavior adds constraints to fields that are yet unset
-by a more inner context. Currently the fields are (job, task, cpu/gpu).
-
-If `cluster` is `None`, and `ps_tasks` is 0, the returned function is a no-op.
-
-For example,
-
-```python
-# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
-# jobs on hosts worker0, worker1 and worker2.
-cluster_spec = {
-    "ps": ["ps0:2222", "ps1:2222"],
-    "worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
-with tf.device(tf.replica_device_setter(cluster=cluster_spec)):
-  # Build your graph
-  v1 = tf.Variable(...)  # assigned to /job:ps/task:0
-  v2 = tf.Variable(...)  # assigned to /job:ps/task:1
-  v3 = tf.Variable(...)  # assigned to /job:ps/task:0
-# Run compute
-```
-
-##### Args:
-
-
-*  <b>`ps_tasks`</b>: Number of tasks in the `ps` job.
-*  <b>`ps_device`</b>: String.  Device of the `ps` job.  If empty no `ps` job is used.
-    Defaults to `ps`.
-*  <b>`worker_device`</b>: String.  Device of the `worker` job.  If empty no `worker`
-    job is used.
-*  <b>`merge_devices`</b>: `Boolean`. If `True`, merges or only sets a device if the
-    device constraint is completely unset. merges device specification rather
-    than overriding them.
-*  <b>`cluster`</b>: `ClusterDef` proto or `ClusterSpec`.
-*  <b>`ps_ops`</b>: List of `Operation` objects that need to be placed on `ps` devices.
-
-##### Returns:
-
-  A function to pass to `tf.device()`.
-
-##### Raises:
-
-  TypeError if `cluster` is not a dictionary or `ClusterDef` protocol buffer.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.slice_input_producer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.slice_input_producer.md
deleted file mode 100644
index da888d0fc2..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.slice_input_producer.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.train.slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None)` {#slice_input_producer}
-
-Produces a slice of each `Tensor` in `tensor_list`.
-
-Implemented using a Queue -- a `QueueRunner` for the Queue
-is added to the current `Graph`'s `QUEUE_RUNNER` collection.
-
-##### Args:
-
-
-*  <b>`tensor_list`</b>: A list of `Tensor` objects. Every `Tensor` in
-    `tensor_list` must have the same size in the first dimension.
-*  <b>`num_epochs`</b>: An integer (optional). If specified, `slice_input_producer`
-    produces each slice `num_epochs` times before generating
-    an `OutOfRange` error. If not specified, `slice_input_producer` can cycle
-    through the slices an unlimited number of times.
-*  <b>`shuffle`</b>: Boolean. If true, the integers are randomly shuffled within each
-    epoch.
-*  <b>`seed`</b>: An integer (optional). Seed used if shuffle == True.
-*  <b>`capacity`</b>: An integer. Sets the queue capacity.
-*  <b>`shared_name`</b>: (optional). If set, this queue will be shared under the given
-    name across multiple sessions.
-*  <b>`name`</b>: A name for the operations (optional).
-
-##### Returns:
-
-  A list of tensors, one for each element of `tensor_list`.  If the tensor
-  in `tensor_list` has shape `[N, a, b, .., z]`, then the corresponding output
-  tensor will have shape `[a, b, ..., z]`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `slice_input_producer` produces nothing from `tensor_list`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.start_queue_runners.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.start_queue_runners.md
new file mode 100644
index 0000000000..21ac6efee8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.start_queue_runners.md
@@ -0,0 +1,24 @@
+### `tf.train.start_queue_runners(sess=None, coord=None, daemon=True, start=True, collection='queue_runners')` {#start_queue_runners}
+
+Starts all queue runners collected in the graph.
+
+This is a companion method to `add_queue_runner()`.  It just starts
+threads for all queue runners collected in the graph.  It returns
+the list of all threads.
+
+##### Args:
+
+
+*  <b>`sess`</b>: `Session` used to run the queue ops.  Defaults to the
+    default session.
+*  <b>`coord`</b>: Optional `Coordinator` for coordinating the started threads.
+*  <b>`daemon`</b>: Whether the threads should be marked as `daemons`, meaning
+    they don't block program exit.
+*  <b>`start`</b>: Set to `False` to only create the threads, not start them.
+*  <b>`collection`</b>: A `GraphKey` specifying the graph collection to
+    get the queue runners from.  Defaults to `GraphKeys.QUEUE_RUNNERS`.
+
+##### Returns:
+
+  A list of threads.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.string_input_producer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.string_input_producer.md
deleted file mode 100644
index 5ca2a4cb86..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.train.string_input_producer.md
+++ /dev/null
@@ -1,32 +0,0 @@
-### `tf.train.string_input_producer(string_tensor, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None)` {#string_input_producer}
-
-Output strings (e.g. filenames) to a queue for an input pipeline.
-
-##### Args:
-
-
-*  <b>`string_tensor`</b>: A 1-D string tensor with the strings to produce.
-*  <b>`num_epochs`</b>: An integer (optional). If specified, `string_input_producer`
-    produces each string from `string_tensor` `num_epochs` times before
-    generating an `OutOfRange` error. If not specified,
-    `string_input_producer` can cycle through the strings in `string_tensor`
-    an unlimited number of times.
-*  <b>`shuffle`</b>: Boolean. If true, the strings are randomly shuffled within each
-    epoch.
-*  <b>`seed`</b>: An integer (optional). Seed used if shuffle == True.
-*  <b>`capacity`</b>: An integer. Sets the queue capacity.
-*  <b>`shared_name`</b>: (optional). If set, this queue will be shared under the given
-    name across multiple sessions.
-*  <b>`name`</b>: A name for the operations (optional).
-
-##### Returns:
-
-  A queue with the output strings.  A `QueueRunner` for the Queue
-  is added to the current `Graph`'s `QUEUE_RUNNER` collection.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the string_tensor is a null Python list.  At runtime,
-  will fail with an assertion if string_tensor becomes a null tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.truncated_normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.truncated_normal.md
deleted file mode 100644
index 9ae13882d3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.truncated_normal.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)` {#truncated_normal}
-
-Outputs random values from a truncated normal distribution.
-
-The generated values follow a normal distribution with specified mean and
-standard deviation, except that values whose magnitude is more than 2 standard
-deviations from the mean are dropped and re-picked.
-
-##### Args:
-
-
-*  <b>`shape`</b>: A 1-D integer Tensor or Python array. The shape of the output tensor.
-*  <b>`mean`</b>: A 0-D Tensor or Python value of type `dtype`. The mean of the
-    truncated normal distribution.
-*  <b>`stddev`</b>: A 0-D Tensor or Python value of type `dtype`. The standard deviation
-    of the truncated normal distribution.
-*  <b>`dtype`</b>: The type of the output.
-*  <b>`seed`</b>: A Python integer. Used to create a random seed for the distribution.
-    See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tensor of the specified shape filled with random truncated normal values.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.unique_with_counts.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.unique_with_counts.md
new file mode 100644
index 0000000000..2d3d32d970
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.unique_with_counts.md
@@ -0,0 +1,36 @@
+### `tf.unique_with_counts(x, name=None)` {#unique_with_counts}
+
+Finds unique elements in a 1-D tensor.
+
+This operation returns a tensor `y` containing all of the unique elements of `x`
+sorted in the same order that they occur in `x`. This operation also returns a
+tensor `idx` the same size as `x` that contains the index of each value of `x`
+in the unique output `y`. Finally, it returns a third tensor `count` that
+contains the count of each element of `y` in `x`. In other words:
+
+`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
+
+For example:
+
+```prettyprint
+# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
+y, idx, count = unique_with_counts(x)
+y ==> [1, 2, 4, 7, 8]
+idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
+count ==> [2, 1, 3, 1, 2]
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. 1-D.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of `Tensor` objects (y, idx, count).
+
+*  <b>`y`</b>: A `Tensor`. Has the same type as `x`. 1-D.
+*  <b>`idx`</b>: A `Tensor` of type `int32`. 1-D.
+*  <b>`count`</b>: A `Tensor` of type `int32`. 1-D.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.verify_tensor_all_finite.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.verify_tensor_all_finite.md
new file mode 100644
index 0000000000..37fa105df5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.verify_tensor_all_finite.md
@@ -0,0 +1,15 @@
+### `tf.verify_tensor_all_finite(t, msg, name=None)` {#verify_tensor_all_finite}
+
+Assert that the tensor does not contain any NaN's or Inf's.
+
+##### Args:
+
+
+*  <b>`t`</b>: Tensor to check.
+*  <b>`msg`</b>: Message to log on failure.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  Same tensor as `t`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.DeviceSpec.from_string.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.DeviceSpec.from_string.md
deleted file mode 100644
index 5cbba0ada6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.DeviceSpec.from_string.md
+++ /dev/null
@@ -1,18 +0,0 @@
-#### `tf.DeviceSpec.from_string(spec)` {#DeviceSpec.from_string}
-
-Construct a `DeviceSpec` from a string.
-
-##### Args:
-
-
-*  <b>`spec`</b>: a string of the form
-   /job:<name>/replica:<id>/task:<id>/device:CPU:<id>
-  or
-   /job:<name>/replica:<id>/task:<id>/device:GPU:<id>
-  as cpu and gpu are mutually exclusive.
-  All entries are optional.
-
-##### Returns:
-
-  A DeviceSpec.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.DeviceSpec.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.DeviceSpec.md
deleted file mode 100644
index 18c651a45d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.DeviceSpec.md
+++ /dev/null
@@ -1,146 +0,0 @@
-Represents a (possibly partial) specification for a TensorFlow device.
-
-`DeviceSpec`s are used throughout TensorFlow to describe where state is stored
-and computations occur. Using `DeviceSpec` allows you to parse device spec
-strings to verify their validity, merge them or compose them programmatically.
-
-Example:
-```python
-# Place the operations on device "GPU:0" in the "ps" job.
-device_spec = DeviceSpec(job="ps", device_type="GPU", device_index=0)
-with tf.device(device_spec):
-  # Both my_var and squared_var will be placed on /job:ps/device:GPU:0.
-  my_var = tf.Variable(..., name="my_variable")
-  squared_var = tf.square(my_var)
-```
-
-If a `DeviceSpec` is partially specified, it will be merged with other
-`DeviceSpec`s according to the scope in which it is defined. `DeviceSpec`
-components defined in inner scopes take precedence over those defined in
-outer scopes.
-
-```python
-with tf.device(DeviceSpec(job="train", )):
-  with tf.device(DeviceSpec(job="ps", device_type="GPU", device_index=0):
-    # Nodes created here will be assigned to /job:ps/device:GPU:0.
-  with tf.device(DeviceSpec(device_type="GPU", device_index=1):
-    # Nodes created here will be assigned to /job:train/device:GPU:1.
-```
-
-A `DeviceSpec` consists of 5 components -- each of
-which is optionally specified:
-
-* Job: The job name.
-* Replica: The replica index.
-* Task: The task index.
-* Device type: The device type string (e.g. "CPU" or "GPU").
-* Device index: The device index.
-- - -
-
-#### `tf.DeviceSpec.__init__(job=None, replica=None, task=None, device_type=None, device_index=None)` {#DeviceSpec.__init__}
-
-Create a new `DeviceSpec` object.
-
-##### Args:
-
-
-*  <b>`job`</b>: string.  Optional job name.
-*  <b>`replica`</b>: int.  Optional replica index.
-*  <b>`task`</b>: int.  Optional task index.
-*  <b>`device_type`</b>: Optional device type string (e.g. "CPU" or "GPU")
-*  <b>`device_index`</b>: int.  Optional device index.  If left
-    unspecified, device represents 'any' device_index.
-
-
-- - -
-
-#### `tf.DeviceSpec.from_string(spec)` {#DeviceSpec.from_string}
-
-Construct a `DeviceSpec` from a string.
-
-##### Args:
-
-
-*  <b>`spec`</b>: a string of the form
-   /job:<name>/replica:<id>/task:<id>/device:CPU:<id>
-  or
-   /job:<name>/replica:<id>/task:<id>/device:GPU:<id>
-  as cpu and gpu are mutually exclusive.
-  All entries are optional.
-
-##### Returns:
-
-  A DeviceSpec.
-
-
-- - -
-
-#### `tf.DeviceSpec.job` {#DeviceSpec.job}
-
-
-
-
-- - -
-
-#### `tf.DeviceSpec.merge_from(dev)` {#DeviceSpec.merge_from}
-
-Merge the properties of "dev" into this `DeviceSpec`.
-
-##### Args:
-
-
-*  <b>`dev`</b>: a `DeviceSpec`.
-
-
-- - -
-
-#### `tf.DeviceSpec.parse_from_string(spec)` {#DeviceSpec.parse_from_string}
-
-Parse a `DeviceSpec` name into its components.
-
-##### Args:
-
-
-*  <b>`spec`</b>: a string of the form
-   /job:<name>/replica:<id>/task:<id>/device:CPU:<id>
-  or
-   /job:<name>/replica:<id>/task:<id>/device:GPU:<id>
-  as cpu and gpu are mutually exclusive.
-  All entries are optional.
-
-##### Returns:
-
-  The `DeviceSpec`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if the spec was not valid.
-
-
-- - -
-
-#### `tf.DeviceSpec.replica` {#DeviceSpec.replica}
-
-
-
-
-- - -
-
-#### `tf.DeviceSpec.task` {#DeviceSpec.task}
-
-
-
-
-- - -
-
-#### `tf.DeviceSpec.to_string()` {#DeviceSpec.to_string}
-
-Return a string representation of this `DeviceSpec`.
-
-##### Returns:
-
-  a string of the form
-  /job:<name>/replica:<id>/task:<id>/device:<device_type>:<id>.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.IdentityReader.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.IdentityReader.md
deleted file mode 100644
index 46ba1e9d17..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.IdentityReader.md
+++ /dev/null
@@ -1,148 +0,0 @@
-A Reader that outputs the queued work as both the key and value.
-
-To use, enqueue strings in a Queue.  Read will take the front
-work string and output (work, work).
-
-See ReaderBase for supported methods.
-- - -
-
-#### `tf.IdentityReader.__init__(name=None)` {#IdentityReader.__init__}
-
-Create a IdentityReader.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-
-- - -
-
-#### `tf.IdentityReader.num_records_produced(name=None)` {#IdentityReader.num_records_produced}
-
-Returns the number of records this reader has produced.
-
-This is the same as the number of Read executions that have
-succeeded.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.IdentityReader.num_work_units_completed(name=None)` {#IdentityReader.num_work_units_completed}
-
-Returns the number of work units this reader has finished processing.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.IdentityReader.read(queue, name=None)` {#IdentityReader.read}
-
-Returns the next record (key, value pair) produced by a reader.
-
-Will dequeue a work unit from queue if necessary (e.g. when the
-Reader needs to start reading from a new file since it has
-finished with the previous file).
-
-##### Args:
-
-
-*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
-    to a Queue, with string work items.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of Tensors (key, value).
-
-*  <b>`key`</b>: A string scalar Tensor.
-*  <b>`value`</b>: A string scalar Tensor.
-
-
-- - -
-
-#### `tf.IdentityReader.reader_ref` {#IdentityReader.reader_ref}
-
-Op that implements the reader.
-
-
-- - -
-
-#### `tf.IdentityReader.reset(name=None)` {#IdentityReader.reset}
-
-Restore a reader to its initial clean state.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.IdentityReader.restore_state(state, name=None)` {#IdentityReader.restore_state}
-
-Restore a reader to a previously saved state.
-
-Not all Readers support being restored, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`state`</b>: A string Tensor.
-    Result of a SerializeState of a Reader with matching type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.IdentityReader.serialize_state(name=None)` {#IdentityReader.serialize_state}
-
-Produce a string tensor that encodes the state of a reader.
-
-Not all Readers support being serialized, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A string Tensor.
-
-
-- - -
-
-#### `tf.IdentityReader.supports_serialize` {#IdentityReader.supports_serialize}
-
-Whether the Reader implementation can serialize its state.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.InteractiveSession.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.InteractiveSession.md
new file mode 100644
index 0000000000..cdb5101815
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.InteractiveSession.md
@@ -0,0 +1,68 @@
+A TensorFlow `Session` for use in interactive contexts, such as a shell.
+
+The only difference with a regular `Session` is that an `InteractiveSession`
+installs itself as the default session on construction.
+The methods [`Tensor.eval()`](../../api_docs/python/framework.md#Tensor.eval)
+and [`Operation.run()`](../../api_docs/python/framework.md#Operation.run)
+will use that session to run ops.
+
+This is convenient in interactive shells and [IPython
+notebooks](http://ipython.org), as it avoids having to pass an explicit
+`Session` object to run ops.
+
+For example:
+
+```python
+sess = tf.InteractiveSession()
+a = tf.constant(5.0)
+b = tf.constant(6.0)
+c = a * b
+# We can just use 'c.eval()' without passing 'sess'
+print(c.eval())
+sess.close()
+```
+
+Note that a regular session installs itself as the default session when it
+is created in a `with` statement.  The common usage in non-interactive
+programs is to follow that pattern:
+
+```python
+a = tf.constant(5.0)
+b = tf.constant(6.0)
+c = a * b
+with tf.Session():
+  # We can also use 'c.eval()' here.
+  print(c.eval())
+```
+
+- - -
+
+#### `tf.InteractiveSession.__init__(target='', graph=None, config=None)` {#InteractiveSession.__init__}
+
+Creates a new interactive TensorFlow session.
+
+If no `graph` argument is specified when constructing the session,
+the default graph will be launched in the session. If you are
+using more than one graph (created with `tf.Graph()` in the same
+process, you will have to use different sessions for each graph,
+but each graph can be used in multiple sessions. In this case, it
+is often clearer to pass the graph to be launched explicitly to
+the session constructor.
+
+##### Args:
+
+
+*  <b>`target`</b>: (Optional.) The execution engine to connect to.
+    Defaults to using an in-process engine. At present, no value
+    other than the empty string is supported.
+*  <b>`graph`</b>: (Optional.) The `Graph` to be launched (described above).
+*  <b>`config`</b>: (Optional) `ConfigProto` proto used to configure the session.
+
+
+- - -
+
+#### `tf.InteractiveSession.close()` {#InteractiveSession.close}
+
+Closes an `InteractiveSession`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.QueueBase.from_list.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.QueueBase.from_list.md
new file mode 100644
index 0000000000..d9a2e7c71f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.QueueBase.from_list.md
@@ -0,0 +1,21 @@
+#### `tf.QueueBase.from_list(index, queues)` {#QueueBase.from_list}
+
+Create a queue using the queue reference from `queues[index]`.
+
+##### Args:
+
+
+*  <b>`index`</b>: An integer scalar tensor that determines the input that gets
+    selected.
+*  <b>`queues`</b>: A list of `QueueBase` objects.
+
+##### Returns:
+
+  A `QueueBase` object.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: When `queues` is not a list of `QueueBase` objects,
+    or when the data types of `queues` are not all the same.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.RandomShuffleQueue.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.RandomShuffleQueue.md
deleted file mode 100644
index cd617e7578..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.RandomShuffleQueue.md
+++ /dev/null
@@ -1,54 +0,0 @@
-A queue implementation that dequeues elements in a random order.
-
-See [`tf.QueueBase`](#QueueBase) for a description of the methods on
-this class.
-
-- - -
-
-#### `tf.RandomShuffleQueue.__init__(capacity, min_after_dequeue, dtypes, shapes=None, names=None, seed=None, shared_name=None, name='random_shuffle_queue')` {#RandomShuffleQueue.__init__}
-
-Create a queue that dequeues elements in a random order.
-
-A `RandomShuffleQueue` has bounded capacity; supports multiple
-concurrent producers and consumers; and provides exactly-once
-delivery.
-
-A `RandomShuffleQueue` holds a list of up to `capacity`
-elements. Each element is a fixed-length tuple of tensors whose
-dtypes are described by `dtypes`, and whose shapes are optionally
-described by the `shapes` argument.
-
-If the `shapes` argument is specified, each component of a queue
-element must have the respective fixed shape. If it is
-unspecified, different queue elements may have different shapes,
-but the use of `dequeue_many` is disallowed.
-
-The `min_after_dequeue` argument allows the caller to specify a
-minimum number of elements that will remain in the queue after a
-`dequeue` or `dequeue_many` operation completes, to ensure a
-minimum level of mixing of elements. This invariant is maintained
-by blocking those operations until sufficient elements have been
-enqueued. The `min_after_dequeue` argument is ignored after the
-queue has been closed.
-
-##### Args:
-
-
-*  <b>`capacity`</b>: An integer. The upper bound on the number of elements
-    that may be stored in this queue.
-*  <b>`min_after_dequeue`</b>: An integer (described above).
-*  <b>`dtypes`</b>: A list of `DType` objects. The length of `dtypes` must equal
-    the number of tensors in each queue element.
-*  <b>`shapes`</b>: (Optional.) A list of fully-defined `TensorShape` objects
-    with the same length as `dtypes`, or `None`.
-*  <b>`names`</b>: (Optional.) A list of string naming the components in the queue
-    with the same length as `dtypes`, or `None`.  If specified the dequeue
-    methods return a dictionary with the names as keys.
-*  <b>`seed`</b>: A Python integer. Used to create a random seed. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`shared_name`</b>: (Optional.) If non-empty, this queue will be shared under
-    the given name across multiple sessions.
-*  <b>`name`</b>: Optional name for the queue operation.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.RegisterShape.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.RegisterShape.md
deleted file mode 100644
index e3bb956f88..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.RegisterShape.md
+++ /dev/null
@@ -1,27 +0,0 @@
-A decorator for registering the shape function for an op type.
-
-This decorator is only used when defining a new op type. A shape
-function is a function from an `Operation` object to a list of
-`TensorShape` objects, with one `TensorShape` for each output of the
-operation.
-
-For example, assuming that operations of type `"Sub"` take two
-inputs `x` and `y`, and return a single output `x - y`, all with the
-same shape, the following shape function would be registered:
-
-```python
-@tf.RegisterShape("Sub")
-def _sub_shape(op):
-  return [op.inputs[0].get_shape().merge_with(op.inputs[1].get_shape())]
-```
-
-The decorator argument `op_type` is the string type of an
-operation. This corresponds to the `OpDef.name` field for the proto
-that defines the operation.
-- - -
-
-#### `tf.RegisterShape.__init__(op_type)` {#RegisterShape.__init__}
-
-Saves the `op_type` as the `Operation` type.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.Session.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.Session.md
deleted file mode 100644
index 62982698dd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.Session.md
+++ /dev/null
@@ -1,236 +0,0 @@
-A class for running TensorFlow operations.
-
-A `Session` object encapsulates the environment in which `Operation`
-objects are executed, and `Tensor` objects are evaluated. For
-example:
-
-```python
-# Build a graph.
-a = tf.constant(5.0)
-b = tf.constant(6.0)
-c = a * b
-
-# Launch the graph in a session.
-sess = tf.Session()
-
-# Evaluate the tensor `c`.
-print(sess.run(c))
-```
-
-A session may own resources, such as
-[variables](../../api_docs/python/state_ops.md#Variable), [queues](../../api_docs/python/io_ops.md#QueueBase),
-and [readers](../../api_docs/python/io_ops.md#ReaderBase). It is important to release
-these resources when they are no longer required. To do this, either
-invoke the [`close()`](#Session.close) method on the session, or use
-the session as a context manager. The following two examples are
-equivalent:
-
-```python
-# Using the `close()` method.
-sess = tf.Session()
-sess.run(...)
-sess.close()
-
-# Using the context manager.
-with tf.Session() as sess:
-  sess.run(...)
-```
-
-The [`ConfigProto`]
-(https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)
-protocol buffer exposes various configuration options for a
-session. For example, to create a session that uses soft constraints
-for device placement, and log the resulting placement decisions,
-create a session as follows:
-
-```python
-# Launch the graph in a session that allows soft device placement and
-# logs the placement decisions.
-sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
-                                        log_device_placement=True))
-```
-
-- - -
-
-#### `tf.Session.__init__(target='', graph=None, config=None)` {#Session.__init__}
-
-Creates a new TensorFlow session.
-
-If no `graph` argument is specified when constructing the session,
-the default graph will be launched in the session. If you are
-using more than one graph (created with `tf.Graph()` in the same
-process, you will have to use different sessions for each graph,
-but each graph can be used in multiple sessions. In this case, it
-is often clearer to pass the graph to be launched explicitly to
-the session constructor.
-
-##### Args:
-
-
-*  <b>`target`</b>: (Optional.) The execution engine to connect to.
-    Defaults to using an in-process engine. At present, no value
-    other than the empty string is supported.
-*  <b>`graph`</b>: (Optional.) The `Graph` to be launched (described above).
-*  <b>`config`</b>: (Optional.) A [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)
-    protocol buffer with configuration options for the session.
-
-
-- - -
-
-#### `tf.Session.run(fetches, feed_dict=None, options=None, run_metadata=None)` {#Session.run}
-
-Runs the operations and evaluates the tensors in `fetches`.
-
-This method runs one "step" of TensorFlow computation, by
-running the necessary graph fragment to execute every `Operation`
-and evaluate every `Tensor` in `fetches`, substituting the values in
-`feed_dict` for the corresponding input values.
-
-The `fetches` argument may be a list of graph elements or a single
-graph element, and these determine the return value of this
-method. A graph element can be one of the following types:
-
-* If the *i*th element of `fetches` is an
-  [`Operation`](../../api_docs/python/framework.md#Operation), the *i*th
-  return value will be `None`.
-* If the *i*th element of `fetches` is a
-  [`Tensor`](../../api_docs/python/framework.md#Tensor), the *i*th return
-  value will be a numpy ndarray containing the value of that tensor.
-* If the *i*th element of `fetches` is a
-  [`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
-  the *i*th return value will be a
-  [`SparseTensorValue`](../../api_docs/python/sparse_ops.md#SparseTensorValue)
-  containing the value of that sparse tensor.
-* If the *i*th element of `fetches` is produced by a `get_tensor_handle` op,
-  the *i*th return value will be a numpy ndarray containing the handle of
-  that tensor.
-
-The optional `feed_dict` argument allows the caller to override
-the value of tensors in the graph. Each key in `feed_dict` can be
-one of the following types:
-
-* If the key is a [`Tensor`](../../api_docs/python/framework.md#Tensor), the
-  value may be a Python scalar, string, list, or numpy ndarray
-  that can be converted to the same `dtype` as that
-  tensor. Additionally, if the key is a
-  [placeholder](../../api_docs/python/io_ops.md#placeholder), the shape of
-  the value will be checked for compatibility with the placeholder.
-* If the key is a
-  [`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
-  the value should be a
-  [`SparseTensorValue`](../../api_docs/python/sparse_ops.md#SparseTensorValue).
-
-Each value in `feed_dict` must be convertible to a numpy array of the dtype
-of the corresponding key.
-
-The optional `options` argument expects a [`RunOptions`] proto. The options
-allow controlling the behavior of this particular step (e.g. turning tracing
-on).
-
-The optional `run_metadata` argument expects a [`RunMetadata`] proto. When
-appropriate, the non-Tensor output of this step will be collected there. For
-example, when users turn on tracing in `options`, the profiled info will be
-collected into this argument and passed back.
-
-##### Args:
-
-
-*  <b>`fetches`</b>: A single graph element, or a list of graph elements
-    (described above).
-*  <b>`feed_dict`</b>: A dictionary that maps graph elements to values
-    (described above).
-*  <b>`options`</b>: A [`RunOptions`] protocol buffer
-*  <b>`run_metadata`</b>: A [`RunMetadata`] protocol buffer
-
-##### Returns:
-
-  Either a single value if `fetches` is a single graph element, or
-  a list of values if `fetches` is a list (described above).
-
-##### Raises:
-
-
-*  <b>`RuntimeError`</b>: If this `Session` is in an invalid state (e.g. has been
-    closed).
-*  <b>`TypeError`</b>: If `fetches` or `feed_dict` keys are of an inappropriate type.
-*  <b>`ValueError`</b>: If `fetches` or `feed_dict` keys are invalid or refer to a
-    `Tensor` that doesn't exist.
-
-
-- - -
-
-#### `tf.Session.close()` {#Session.close}
-
-Closes this session.
-
-Calling this method frees all resources associated with the session.
-
-##### Raises:
-
-  tf.errors.OpError: Or one of its subclasses if an error occurs while
-    closing the TensorFlow session.
-
-
-
-- - -
-
-#### `tf.Session.graph` {#Session.graph}
-
-The graph that was launched in this session.
-
-
-
-- - -
-
-#### `tf.Session.as_default()` {#Session.as_default}
-
-Returns a context manager that makes this object the default session.
-
-Use with the `with` keyword to specify that calls to
-[`Operation.run()`](../../api_docs/python/framework.md#Operation.run) or
-[`Tensor.run()`](../../api_docs/python/framework.md#Tensor.run) should be
-executed in this session.
-
-```python
-c = tf.constant(..)
-sess = tf.Session()
-
-with sess.as_default():
-  assert tf.get_default_session() is sess
-  print(c.eval())
-```
-
-To get the current default session, use
-[`tf.get_default_session()`](#get_default_session).
-
-
-*N.B.* The `as_default` context manager *does not* close the
-session when you exit the context, and you must close the session
-explicitly.
-
-```python
-c = tf.constant(...)
-sess = tf.Session()
-with sess.as_default():
-  print(c.eval())
-# ...
-with sess.as_default():
-  print(c.eval())
-
-sess.close()
-```
-
-Alternatively, you can use `with tf.Session():` to create a
-session that is automatically closed on exiting the context,
-including when an uncaught exception is raised.
-
-*N.B.* The default graph is a property of the current thread. If you
-create a new thread, and wish to use the default session in that
-thread, you must explicitly add a `with sess.as_default():` in that
-thread's function.
-
-##### Returns:
-
-  A context manager using this session as the default session.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.TFRecordReader.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.TFRecordReader.md
new file mode 100644
index 0000000000..31c6ffacfb
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.TFRecordReader.md
@@ -0,0 +1,145 @@
+A Reader that outputs the records from a TFRecords file.
+
+See ReaderBase for supported methods.
+- - -
+
+#### `tf.TFRecordReader.__init__(name=None)` {#TFRecordReader.__init__}
+
+Create a TFRecordReader.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+
+- - -
+
+#### `tf.TFRecordReader.num_records_produced(name=None)` {#TFRecordReader.num_records_produced}
+
+Returns the number of records this reader has produced.
+
+This is the same as the number of Read executions that have
+succeeded.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.TFRecordReader.num_work_units_completed(name=None)` {#TFRecordReader.num_work_units_completed}
+
+Returns the number of work units this reader has finished processing.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.TFRecordReader.read(queue, name=None)` {#TFRecordReader.read}
+
+Returns the next record (key, value pair) produced by a reader.
+
+Will dequeue a work unit from queue if necessary (e.g. when the
+Reader needs to start reading from a new file since it has
+finished with the previous file).
+
+##### Args:
+
+
+*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
+    to a Queue, with string work items.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of Tensors (key, value).
+
+*  <b>`key`</b>: A string scalar Tensor.
+*  <b>`value`</b>: A string scalar Tensor.
+
+
+- - -
+
+#### `tf.TFRecordReader.reader_ref` {#TFRecordReader.reader_ref}
+
+Op that implements the reader.
+
+
+- - -
+
+#### `tf.TFRecordReader.reset(name=None)` {#TFRecordReader.reset}
+
+Restore a reader to its initial clean state.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.TFRecordReader.restore_state(state, name=None)` {#TFRecordReader.restore_state}
+
+Restore a reader to a previously saved state.
+
+Not all Readers support being restored, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`state`</b>: A string Tensor.
+    Result of a SerializeState of a Reader with matching type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.TFRecordReader.serialize_state(name=None)` {#TFRecordReader.serialize_state}
+
+Produce a string tensor that encodes the state of a reader.
+
+Not all Readers support being serialized, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A string Tensor.
+
+
+- - -
+
+#### `tf.TFRecordReader.supports_serialize` {#TFRecordReader.supports_serialize}
+
+Whether the Reader implementation can serialize its state.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.Variable.from_proto.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.Variable.from_proto.md
new file mode 100644
index 0000000000..5b10d329bc
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.Variable.from_proto.md
@@ -0,0 +1,4 @@
+#### `tf.Variable.from_proto(variable_def)` {#Variable.from_proto}
+
+Returns a `Variable` object created from `variable_def`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.WholeFileReader.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.WholeFileReader.md
deleted file mode 100644
index e168cabc9e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.WholeFileReader.md
+++ /dev/null
@@ -1,148 +0,0 @@
-A Reader that outputs the entire contents of a file as a value.
-
-To use, enqueue filenames in a Queue.  The output of Read will
-be a filename (key) and the contents of that file (value).
-
-See ReaderBase for supported methods.
-- - -
-
-#### `tf.WholeFileReader.__init__(name=None)` {#WholeFileReader.__init__}
-
-Create a WholeFileReader.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-
-- - -
-
-#### `tf.WholeFileReader.num_records_produced(name=None)` {#WholeFileReader.num_records_produced}
-
-Returns the number of records this reader has produced.
-
-This is the same as the number of Read executions that have
-succeeded.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.WholeFileReader.num_work_units_completed(name=None)` {#WholeFileReader.num_work_units_completed}
-
-Returns the number of work units this reader has finished processing.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.WholeFileReader.read(queue, name=None)` {#WholeFileReader.read}
-
-Returns the next record (key, value pair) produced by a reader.
-
-Will dequeue a work unit from queue if necessary (e.g. when the
-Reader needs to start reading from a new file since it has
-finished with the previous file).
-
-##### Args:
-
-
-*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
-    to a Queue, with string work items.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of Tensors (key, value).
-
-*  <b>`key`</b>: A string scalar Tensor.
-*  <b>`value`</b>: A string scalar Tensor.
-
-
-- - -
-
-#### `tf.WholeFileReader.reader_ref` {#WholeFileReader.reader_ref}
-
-Op that implements the reader.
-
-
-- - -
-
-#### `tf.WholeFileReader.reset(name=None)` {#WholeFileReader.reset}
-
-Restore a reader to its initial clean state.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.WholeFileReader.restore_state(state, name=None)` {#WholeFileReader.restore_state}
-
-Restore a reader to a previously saved state.
-
-Not all Readers support being restored, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`state`</b>: A string Tensor.
-    Result of a SerializeState of a Reader with matching type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.WholeFileReader.serialize_state(name=None)` {#WholeFileReader.serialize_state}
-
-Produce a string tensor that encodes the state of a reader.
-
-Not all Readers support being serialized, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A string Tensor.
-
-
-- - -
-
-#### `tf.WholeFileReader.supports_serialize` {#WholeFileReader.supports_serialize}
-
-Whether the Reader implementation can serialize its state.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.abs.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.abs.md
deleted file mode 100644
index 63a0b4c954..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.abs.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.abs(x, name=None)` {#abs}
-
-Computes the absolute value of a tensor.
-
-Given a tensor of real numbers `x`, this operation returns a tensor
-containing the absolute value of each element in `x`. For example, if x is
-an input element and y is an output element, this operation computes
-\\(y = |x|\\).
-
-See [`tf.complex_abs()`](#tf_complex_abs) to compute the absolute value of a complex
-number.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` of type `float`, `double`, `int32`, or `int64`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-   A `Tensor` the same size and type as `x` with absolute values.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.all_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.all_variables.md
new file mode 100644
index 0000000000..904b99f321
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.all_variables.md
@@ -0,0 +1,12 @@
+### `tf.all_variables()` {#all_variables}
+
+Returns all variables that must be saved/restored.
+
+The `Variable()` constructor automatically adds new variables to the graph
+collection `GraphKeys.VARIABLES`. This convenience function returns the
+contents of that collection.
+
+##### Returns:
+
+  A list of `Variable` objects.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.assert_less_equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.assert_less_equal.md
new file mode 100644
index 0000000000..d740746a61
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.assert_less_equal.md
@@ -0,0 +1,35 @@
+### `tf.assert_less_equal(x, y, data=None, summarize=None, name=None)` {#assert_less_equal}
+
+Assert the condition `x <= y` holds element-wise.
+
+Example of adding a dependency to an operation:
+
+```python
+with tf.control_dependencies([tf.assert_less_equal(x, y)]):
+  output = tf.reduce_sum(x)
+```
+
+Example of adding dependency to the tensor being checked:
+
+```python
+x = tf.with_dependencies([tf.assert_less_equal(x, y)], x)
+```
+
+This condition holds if for every pair of (possibly broadcast) elements
+`x[i]`, `y[i]`, we have `x[i] <= y[i]`.
+If both `x` and `y` are empty, this is trivially satisfied.
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`y`</b>: Numeric `Tensor`, same dtype as and broadcastable to `x`.
+*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
+    error message and first few entries of `x`, `y`.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_less_equal"
+
+##### Returns:
+
+  Op that raises `InvalidArgumentError` if `x <= y` is False.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_cholesky_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_cholesky_solve.md
deleted file mode 100644
index 25fcc5c908..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_cholesky_solve.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.batch_cholesky_solve(chol, rhs, name=None)` {#batch_cholesky_solve}
-
-Solve batches of linear eqns `A X = RHS`, given Cholesky factorizations.
-
-```python
-# Solve one linear system (K = 1) for every member of the length 10 batch.
-A = ... # shape 10 x 2 x 2
-RHS = ... # shape 10 x 2 x 1
-chol = tf.batch_cholesky(A)  # shape 10 x 2 x 2
-X = tf.batch_cholesky_solve(chol, RHS)  # shape 10 x 2 x 1
-# tf.matmul(A, X) ~ RHS
-X[3, :, 0]  # Solution to the linear system A[3, :, :] x = RHS[3, :, 0]
-
-# Solve five linear systems (K = 5) for every member of the length 10 batch.
-A = ... # shape 10 x 2 x 2
-RHS = ... # shape 10 x 2 x 5
-...
-X[3, :, 2]  # Solution to the linear system A[3, :, :] x = RHS[3, :, 2]
-```
-
-##### Args:
-
-
-*  <b>`chol`</b>: A `Tensor`.  Must be `float32` or `float64`, shape is `[..., M, M]`.
-    Cholesky factorization of `A`, e.g. `chol = tf.batch_cholesky(A)`.
-    For that reason, only the lower triangular parts (including the diagonal)
-    of the last two dimensions of `chol` are used.  The strictly upper part is
-    assumed to be zero and not accessed.
-*  <b>`rhs`</b>: A `Tensor`, same type as `chol`, shape is `[..., M, K]`.
-*  <b>`name`</b>: A name to give this `Op`.  Defaults to `batch_cholesky_solve`.
-
-##### Returns:
-
-  Solution to `A x = rhs`, shape `[..., M, K]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_fft.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_fft.md
new file mode 100644
index 0000000000..c2ea3aa9c1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_fft.md
@@ -0,0 +1,18 @@
+### `tf.batch_fft(input, name=None)` {#batch_fft}
+
+Compute the 1-dimensional discrete Fourier Transform over the inner-most
+
+dimension of `input`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`.
+  A complex64 tensor of the same shape as `input`. The inner-most
+  dimension of `input` is replaced with its 1D Fourier Transform.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_ifft3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_ifft3d.md
deleted file mode 100644
index 1173a17d6d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_ifft3d.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.batch_ifft3d(input, name=None)` {#batch_ifft3d}
-
-Compute the inverse 3-dimensional discrete Fourier Transform over the inner-most
-
-3 dimensions of `input`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`.
-  A complex64 tensor of the same shape as `input`. The inner-most 3
-  dimensions of `input` are replaced with their inverse 3D Fourier Transform.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_matrix_inverse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_matrix_inverse.md
deleted file mode 100644
index 231056a05c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_matrix_inverse.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.batch_matrix_inverse(input, adjoint=None, name=None)` {#batch_matrix_inverse}
-
-Calculates the inverse of square invertible matrices or their adjoints
-
-(conjugate transposes).
-
-The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-form square matrices. The output is a tensor of the same shape as the input
-containing the inverse for all input submatrices `[..., :, :]`.
-
-The op uses LU decomposition with partial pivoting to compute the inverses.
-
-If a matrix is not invertible there is no guarantee what the op does. It
-may detect the condition and raise an exception or it may simply return a
-garbage result.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
-    Shape is `[..., M, M]`.
-*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`. Shape is `[..., M, M]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_self_adjoint_eig.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_self_adjoint_eig.md
deleted file mode 100644
index 19d6c5319f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.batch_self_adjoint_eig.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.batch_self_adjoint_eig(input, name=None)` {#batch_self_adjoint_eig}
-
-Calculates the Eigen Decomposition of a batch of square self-adjoint matrices.
-
-The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-form square matrices, with the same constraints as the single matrix
-SelfAdjointEig.
-
-The result is a '[..., M+1, M] matrix with [..., 0,:] containing the
-eigenvalues, and subsequent [...,1:, :] containing the eigenvectors.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
-    Shape is `[..., M, M]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`. Shape is `[..., M+1, M]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.bitcast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.bitcast.md
deleted file mode 100644
index 4ded707a89..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.bitcast.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.bitcast(input, type, name=None)` {#bitcast}
-
-Bitcasts a tensor from one type to another without copying data.
-
-Given a tensor `input`, this operation returns a tensor that has the same buffer
-data as `input` with datatype `type`.
-
-If the input datatype `T` is larger than the output datatype `type` then the
-shape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)].
-
-If `T` is smaller than `type`, the operator requires that the rightmost
-dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from
-[..., sizeof(`type`)/sizeof(`T`)] to [...].
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-*  <b>`type`</b>: A `tf.DType` from: `tf.float32, tf.float64, tf.int64, tf.int32, tf.uint8, tf.uint16, tf.int16, tf.int8, tf.complex64, tf.complex128, tf.qint8, tf.quint8, tf.qint32, tf.half`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `type`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.boolean_mask.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.boolean_mask.md
deleted file mode 100644
index e893b8ee63..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.boolean_mask.md
+++ /dev/null
@@ -1,43 +0,0 @@
-### `tf.boolean_mask(tensor, mask, name='boolean_mask')` {#boolean_mask}
-
-Apply boolean mask to tensor.  Numpy equivalent is `tensor[mask]`.
-
-```python
-# 1-D example
-tensor = [0, 1, 2, 3]
-mask = [True, False, True, False]
-boolean_mask(tensor, mask) ==> [0, 2]
-```
-
-In general, `0 < dim(mask) = K <= dim(tensor)`, and `mask`'s shape must match
-the first K dimensions of `tensor`'s shape.  We then have:
-  `boolean_mask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iK,j1,...,jd]`
-where `(i1,...,iK)` is the ith `True` entry of `mask` (row-major order).
-
-##### Args:
-
-
-*  <b>`tensor`</b>: N-D tensor.
-*  <b>`mask`</b>: K-D boolean tensor, K <= N and K must be known statically.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  Tensor populated by entries in `tensor` corresponding to `True` values in
-    `mask`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If shapes do not conform.
-
-
-*  <b>`Examples`</b>: 
-
-```python
-# 2-D example
-tensor = [[1, 2], [3, 4], [5, 6]]
-mask = [True, False, True]
-boolean_mask(tensor, mask) ==> [[1, 2], [5, 6]]
-```
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.ceil.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.ceil.md
deleted file mode 100644
index 34e4a7feed..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.ceil.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.ceil(x, name=None)` {#ceil}
-
-Returns element-wise smallest integer in not less than x.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.check_numerics.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.check_numerics.md
deleted file mode 100644
index 46a8f6f7db..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.check_numerics.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.check_numerics(tensor, message, name=None)` {#check_numerics}
-
-Checks a tensor for NaN and Inf values.
-
-When run, reports an `InvalidArgument` error if `tensor` has any values
-that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is.
-
-##### Args:
-
-
-*  <b>`tensor`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
-*  <b>`message`</b>: A `string`. Prefix of the error message.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.cholesky_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.cholesky_solve.md
new file mode 100644
index 0000000000..7445d3f929
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.cholesky_solve.md
@@ -0,0 +1,35 @@
+### `tf.cholesky_solve(chol, rhs, name=None)` {#cholesky_solve}
+
+Solve linear equations `A X = RHS`, given Cholesky factorization of `A`.
+
+```python
+# Solve one system of linear equations (K = 1).
+A = [[3, 1], [1, 3]]
+RHS = [[2], [22]]  # shape 2 x 1
+chol = tf.cholesky(A)
+X = tf.cholesky_solve(chol, RHS)
+# tf.matmul(A, X) ~ RHS
+X[:, 0]  # Solution to the linear system A x = RHS[:, 0]
+
+# Solve five systems of linear equations (K = 5).
+A = [[3, 1], [1, 3]]
+RHS = [[1, 2, 3, 4, 5], [11, 22, 33, 44, 55]]  # shape 2 x 5
+...
+X[:, 2]  # Solution to the linear system A x = RHS[:, 2]
+```
+
+##### Args:
+
+
+*  <b>`chol`</b>: A `Tensor`.  Must be `float32` or `float64`, shape is `[M, M]`.
+    Cholesky factorization of `A`, e.g. `chol = tf.cholesky(A)`.  For that
+    reason, only the lower triangular part (including the diagonal) of `chol`
+    is used.  The strictly upper part is assumed to be zero and not accessed.
+*  <b>`rhs`</b>: A `Tensor`, same type as `chol`, shape is `[M, K]`, designating `K`
+    systems of linear equations.
+*  <b>`name`</b>: A name to give this `Op`.  Defaults to `cholesky_solve`.
+
+##### Returns:
+
+  Solution to `A X = RHS`, shape `[M, K]`.  The solutions to the `K` systems.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.complex.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.complex.md
deleted file mode 100644
index 55487ea170..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.complex.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.complex(real, imag, name=None)` {#complex}
-
-Converts two real numbers to a complex number.
-
-Given a tensor `real` representing the real part of a complex number, and a
-tensor `imag` representing the imaginary part of a complex number, this
-operation returns complex numbers elementwise of the form \(a + bj\), where
-*a* represents the `real` part and *b* represents the `imag` part.
-
-The input tensors `real` and `imag` must have the same shape.
-
-For example:
-
-```
-# tensor 'real' is [2.25, 3.25]
-# tensor `imag` is [4.75, 5.75]
-tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]]
-```
-
-##### Args:
-
-
-*  <b>`real`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-*  <b>`imag`</b>: A `Tensor`. Must have the same type as `real`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64` or `complex128`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.complex_abs.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.complex_abs.md
new file mode 100644
index 0000000000..1cb76668d6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.complex_abs.md
@@ -0,0 +1,26 @@
+### `tf.complex_abs(x, name=None)` {#complex_abs}
+
+Computes the complex absolute value of a tensor.
+
+Given a tensor `x` of complex numbers, this operation returns a tensor of type
+`float` or `double` that is the absolute value of each element in `x`. All
+elements in `x` must be complex numbers of the form \\(a + bj\\). The
+absolute value is computed as \\( \sqrt{a^2 + b^2}\\).
+
+For example:
+
+```
+# tensor 'x' is [[-2.25 + 4.75j], [-3.25 + 5.75j]]
+tf.complex_abs(x) ==> [5.25594902, 6.60492229]
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` of type `complex64` or `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `float32` or `float64`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.cond.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.cond.md
new file mode 100644
index 0000000000..6e6a9a69bf
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.cond.md
@@ -0,0 +1,54 @@
+### `tf.cond(pred, fn1, fn2, name=None)` {#cond}
+
+Return either fn1() or fn2() based on the boolean predicate `pred`.
+
+`fn1` and `fn2` both return lists of output tensors. `fn1` and `fn2` must have
+the same non-zero number and type of outputs.
+
+Note that the conditional execution applies only to the operations defined in
+fn1 and fn2. Consider the following simple program:
+
+```python
+z = tf.mul(a, b)
+result = tf.cond(x < y, lambda: tf.add(x, z), lambda: tf.square(y))
+```
+
+If x < y, the tf.add operation will be executed and tf.square
+operation will not be executed. Since z is needed for at least one
+branch of the cond, the tf.mul operation is always executed, unconditionally.
+Although this behavior is consistent with the dataflow model of TensorFlow,
+it has occasionally surprised some users who expected a lazier semantics.
+
+##### Args:
+
+
+*  <b>`pred`</b>: A scalar determining whether to return the result of `fn1` or `fn2`.
+*  <b>`fn1`</b>: The callable to be performed if pred is true.
+*  <b>`fn2`</b>: The callable to be performed if pref is false.
+*  <b>`name`</b>: Optional name prefix for the returned tensors.
+
+##### Returns:
+
+  Tensors returned by the call to either `fn1` or `fn2`. If the callables
+  return a singleton list, the element is extracted from the list.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `fn1` or `fn2` is not callable.
+*  <b>`ValueError`</b>: if `fn1` and `fn2` do not return the same number of tensors, or
+              return tensors of different types.
+
+
+*  <b>`Example`</b>: 
+
+```python
+  x = tf.constant(2)
+  y = tf.constant(5)
+  def f1(): return tf.mul(x, 17)
+  def f2(): return tf.add(y, 23)
+  r = cond(tf.less(x, y), f1, f2)
+  # r is set to f1().
+  # Operations in f2 (e.g., tf.add) are not executed.
+```
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.conj.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.conj.md
new file mode 100644
index 0000000000..6df004b0cd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.conj.md
@@ -0,0 +1,28 @@
+### `tf.conj(input, name=None)` {#conj}
+
+Returns the complex conjugate of a complex number.
+
+Given a tensor `input` of complex numbers, this operation returns a tensor of
+complex numbers that are the complex conjugate of each element in `input`. The
+complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the
+real part and *b* is the imaginary part.
+
+The complex conjugate returned by this operation is of the form \\(a - bj\\).
+
+For example:
+
+```
+# tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j]
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.constant.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.constant.md
new file mode 100644
index 0000000000..ff34b6eeb1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.constant.md
@@ -0,0 +1,50 @@
+### `tf.constant(value, dtype=None, shape=None, name='Const')` {#constant}
+
+Creates a constant tensor.
+
+ The resulting tensor is populated with values of type `dtype`, as
+ specified by arguments `value` and (optionally) `shape` (see examples
+ below).
+
+ The argument `value` can be a constant value, or a list of values of type
+ `dtype`. If `value` is a list, then the length of the list must be less
+ than or equal to the number of elements implied by the `shape` argument (if
+ specified). In the case where the list length is less than the number of
+ elements specified by `shape`, the last element in the list will be used
+ to fill the remaining entries.
+
+ The argument `shape` is optional. If present, it specifies the dimensions of
+ the resulting tensor. If not present, the shape of `value` is used.
+
+ If the argument `dtype` is not specified, then the type is inferred from
+ the type of `value`.
+
+ For example:
+
+ ```python
+ # Constant 1-D Tensor populated with value list.
+ tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]
+
+ # Constant 2-D tensor populated with scalar value -1.
+ tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.]
+                                              [-1. -1. -1.]]
+ ```
+
+##### Args:
+
+
+*  <b>`value`</b>: A constant value (or list) of output type `dtype`.
+
+
+*  <b>`dtype`</b>: The type of the elements of the resulting tensor.
+
+
+*  <b>`shape`</b>: Optional dimensions of resulting tensor.
+
+
+*  <b>`name`</b>: Optional name for the tensor.
+
+##### Returns:
+
+  A Constant Tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.MultivariateNormal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.MultivariateNormal.md
new file mode 100644
index 0000000000..258cb03ea8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.MultivariateNormal.md
@@ -0,0 +1,218 @@
+The Multivariate Normal distribution on `R^k`.
+
+The distribution has mean and covariance parameters mu (1-D), sigma (2-D),
+or alternatively mean `mu` and factored covariance (cholesky decomposed
+`sigma`) called `sigma_chol`.
+
+#### Mathematical details
+
+The PDF of this distribution is:
+
+```
+f(x) = (2*pi)^(-k/2) |det(sigma)|^(-1/2) exp(-1/2*(x-mu)^*.sigma^{-1}.(x-mu))
+```
+
+where `.` denotes the inner product on `R^k` and `^*` denotes transpose.
+
+Alternatively, if `sigma` is positive definite, it can be represented in terms
+of its lower triangular cholesky factorization
+
+```sigma = sigma_chol . sigma_chol^*```
+
+and the pdf above allows simpler computation:
+
+```
+|det(sigma)| = reduce_prod(diag(sigma_chol))^2
+x_whitened = sigma^{-1/2} . (x - mu) = tri_solve(sigma_chol, x - mu)
+(x-mu)^* .sigma^{-1} . (x-mu) = x_whitened^* . x_whitened
+```
+
+where `tri_solve()` solves a triangular system of equations.
+
+#### Examples
+
+A single multi-variate Gaussian distribution is defined by a vector of means
+of length `k`, and a covariance matrix of shape `k x k`.
+
+Extra leading dimensions, if provided, allow for batches.
+
+```python
+# Initialize a single 3-variate Gaussian with diagonal covariance.
+mu = [1, 2, 3]
+sigma = [[1, 0, 0], [0, 3, 0], [0, 0, 2]]
+dist = tf.contrib.distributions.MultivariateNormal(mu=mu, sigma=sigma)
+
+# Evaluate this on an observation in R^3, returning a scalar.
+dist.pdf([-1, 0, 1])
+
+# Initialize a batch of two 3-variate Gaussians.
+mu = [[1, 2, 3], [11, 22, 33]]
+sigma = ...  # shape 2 x 3 x 3
+dist = tf.contrib.distributions.MultivariateNormal(mu=mu, sigma=sigma)
+
+# Evaluate this on a two observations, each in R^3, returning a length two
+# tensor.
+x = [[-1, 0, 1], [-11, 0, 11]]  # Shape 2 x 3.
+dist.pdf(x)
+```
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.__init__(mu, sigma=None, sigma_chol=None, name=None)` {#MultivariateNormal.__init__}
+
+Multivariate Normal distributions on `R^k`.
+
+User must provide means `mu`, which are tensors of rank `N+1` (`N >= 0`)
+with the last dimension having length `k`.
+
+User must provide exactly one of `sigma` (the covariance matrices) or
+`sigma_chol` (the cholesky decompositions of the covariance matrices).
+`sigma` or `sigma_chol` must be of rank `N+2`.  The last two dimensions
+must both have length `k`.  The first `N` dimensions correspond to batch
+indices.
+
+If `sigma_chol` is not provided, the batch cholesky factorization of `sigma`
+is calculated for you.
+
+The shapes of `mu` and `sigma` must match for the first `N` dimensions.
+
+Regardless of which parameter is provided, the covariance matrices must all
+be **positive definite** (an error is raised if one of them is not).
+
+##### Args:
+
+
+*  <b>`mu`</b>: (N+1)-D.  `float` or `double` tensor, the means of the distributions.
+*  <b>`sigma`</b>: (N+2)-D.  (optional) `float` or `double` tensor, the covariances
+    of the distribution(s).  The first `N+1` dimensions must match
+    those of `mu`.  Must be batch-positive-definite.
+*  <b>`sigma_chol`</b>: (N+2)-D.  (optional) `float` or `double` tensor, a
+    lower-triangular factorization of `sigma`
+    (`sigma = sigma_chol . sigma_chol^*`).  The first `N+1` dimensions
+    must match those of `mu`.  The tensor itself need not be batch
+    lower triangular: we ignore the upper triangular part.  However,
+    the batch diagonals must be positive (i.e., sigma_chol must be
+    batch-positive-definite).
+*  <b>`name`</b>: The name to give Ops created by the initializer.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if neither sigma nor sigma_chol is provided.
+*  <b>`TypeError`</b>: if mu and sigma (resp. sigma_chol) are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.dtype` {#MultivariateNormal.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.entropy(name=None)` {#MultivariateNormal.entropy}
+
+The entropies of these Multivariate Normals.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropies.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.is_reparameterized` {#MultivariateNormal.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.log_pdf(x, name=None)` {#MultivariateNormal.log_pdf}
+
+Log pdf of observations `x` given these Multivariate Normals.
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.mean` {#MultivariateNormal.mean}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.mu` {#MultivariateNormal.mu}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.pdf(x, name=None)` {#MultivariateNormal.pdf}
+
+The PDF of observations `x` under these Multivariate Normals.
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.sample(n, seed=None, name=None)` {#MultivariateNormal.sample}
+
+Sample `n` observations from the Multivariate Normal Distributions.
+
+##### Args:
+
+
+*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`seed`</b>: Python integer, the random seed.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
+    of the distributions determined by broadcasting the hyperparameters.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.sigma` {#MultivariateNormal.sigma}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormal.sigma_det` {#MultivariateNormal.sigma_det}
+
+
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.fully_connected.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.fully_connected.md
deleted file mode 100644
index da63a14cd9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.fully_connected.md
+++ /dev/null
@@ -1,46 +0,0 @@
-### `tf.contrib.layers.fully_connected(*args, **kwargs)` {#fully_connected}
-
-Adds a fully connected layer.
-
-`fully_connected` creates a variable called `weights`, representing a fully
-connected weight matrix, which is multiplied by the `inputs` to produce a
-`Tensor` of hidden units. If a `normalizer_fn` is provided (such as
-`batch_norm`), it is then applied. Otherwise, if `normalizer_fn` is
-None and a `biases_initializer` is provided then a `biases` variable would be
-created and added the hidden units. Finally, if `activation_fn` is not `None`,
-it is applied to the hidden units as well.
-
-Note: that if `inputs` have a rank greater than 2, then `inputs` is flattened
-prior to the initial matrix multiply by `weights`.
-
-##### Args:
-
-
-*  <b>`inputs`</b>: A tensor of with at least rank 2 and value for the last dimension,
-    i.e. `[batch_size, depth]`, `[None, None, None, channels]`.
-*  <b>`num_outputs`</b>: Integer, the number of output units in the layer.
-*  <b>`activation_fn`</b>: activation function.
-*  <b>`normalizer_fn`</b>: normalization function to use instead of `biases`. If
-    `normalize_fn` is provided then `biases_initializer` and
-    `biases_regularizer` are ignored and `biases` are not created nor added.
-*  <b>`normalizer_params`</b>: normalization function parameters.
-*  <b>`weights_initializer`</b>: An initializer for the weights.
-*  <b>`weights_regularizer`</b>: Optional regularizer for the weights.
-*  <b>`biases_initializer`</b>: An initializer for the biases. If None skip biases.
-*  <b>`biases_regularizer`</b>: Optional regularizer for the biases.
-*  <b>`reuse`</b>: whether or not the layer and its variables should be reused. To be
-    able to reuse the layer scope must be given.
-*  <b>`variables_collections`</b>: Optional list of collections for all the variables or
-    a dictionay containing a different list of collection per variable.
-*  <b>`outputs_collections`</b>: collection to add the outputs.
-*  <b>`scope`</b>: Optional scope for variable_op_scope.
-
-##### Returns:
-
-   the tensor variable representing the result of the series of operations.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if x has rank less than 2 or if its last dimension is not set.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.summarize_activations.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.summarize_activations.md
new file mode 100644
index 0000000000..dc2e7a6044
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.summarize_activations.md
@@ -0,0 +1,4 @@
+### `tf.contrib.layers.summarize_activations(name_filter=None, summarizer=summarize_activation)` {#summarize_activations}
+
+Summarize activations, using `summarize_activation` to summarize.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.xavier_initializer_conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.xavier_initializer_conv2d.md
new file mode 100644
index 0000000000..9deeb48b5b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.layers.xavier_initializer_conv2d.md
@@ -0,0 +1,29 @@
+### `tf.contrib.layers.xavier_initializer_conv2d(uniform=True, seed=None, dtype=tf.float32)` {#xavier_initializer_conv2d}
+
+Returns an initializer performing "Xavier" initialization for weights.
+
+This function implements the weight initialization from:
+
+Xavier Glorot and Yoshua Bengio (2010):
+         Understanding the difficulty of training deep feedforward neural
+         networks. International conference on artificial intelligence and
+         statistics.
+
+This initializer is designed to keep the scale of the gradients roughly the
+same in all layers. In uniform distribution this ends up being the range:
+`x = sqrt(6. / (in + out)); [-x, x]` and for normal distribution a standard
+deviation of `sqrt(3. / (in + out))` is used.
+
+##### Args:
+
+
+*  <b>`uniform`</b>: Whether to use uniform or normal distributed random initialization.
+*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`dtype`</b>: The data type. Only floating point types are supported.
+
+##### Returns:
+
+  An initializer for a weight matrix.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.RunConfig.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.RunConfig.md
deleted file mode 100644
index ffdf8703c0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.RunConfig.md
+++ /dev/null
@@ -1,47 +0,0 @@
-This class specifies the specific configurations for the run.
-
-Parameters:
-  execution_mode: Runners use this flag to execute different tasks, like
-    training vs evaluation. 'all' (the default) executes both training and
-    eval.
-  master: TensorFlow master. Empty string (the default) for local.
-  task: Task id of the replica running the training (default: 0).
-  num_ps_replicas: Number of parameter server tasks to use (default: 0).
-  training_worker_session_startup_stagger_secs: Seconds to sleep between the
-    startup of each worker task session (default: 5).
-  training_worker_max_startup_secs: Max seconds to wait before starting any
-    worker (default: 60).
-  eval_delay_secs: Number of seconds between the beginning of each eval run.
-    If one run takes more than this amount of time, the next run will start
-    immediately once that run completes (default 60).
-  eval_steps: Number of steps to run in each eval (default: 100).
-  num_cores: Number of cores to be used (default: 4).
-  verbose: Controls the verbosity, possible values:
-    0: the algorithm and debug information is muted.
-    1: trainer prints the progress.
-    2: log device placement is printed.
-  gpu_memory_fraction: Fraction of GPU memory used by the process on
-    each GPU uniformly on the same machine.
-  tf_random_seed: Random seed for TensorFlow initializers.
-    Setting this value allows consistency between reruns.
-  keep_checkpoint_max: The maximum number of recent checkpoint files to keep.
-    As new files are created, older files are deleted.
-    If None or 0, all checkpoint files are kept.
-    Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
-  keep_checkpoint_every_n_hours: Number of hours between each checkpoint
-    to be saved. The default value of 10,000 hours effectively disables
-    the feature.
-
-Attributes:
-  tf_master: Tensorflow master.
-  tf_config: Tensorflow Session Config proto.
-  tf_random_seed: Tensorflow random seed.
-  keep_checkpoint_max: Maximum number of checkpoints to keep.
-  keep_checkpoint_every_n_hours: Number of hours between each checkpoint.
-- - -
-
-#### `tf.contrib.learn.RunConfig.__init__(execution_mode='all', master='', task=0, num_ps_replicas=0, training_worker_session_startup_stagger_secs=5, training_worker_max_startup_secs=60, eval_delay_secs=60, eval_steps=100, num_cores=4, verbose=1, gpu_memory_fraction=1, tf_random_seed=42, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000)` {#RunConfig.__init__}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNClassifier.md
new file mode 100644
index 0000000000..03c779259a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNClassifier.md
@@ -0,0 +1,302 @@
+TensorFlow DNN Classifier model.
+
+Parameters:
+  hidden_units: List of hidden units per layer.
+  n_classes: Number of classes in the target.
+  batch_size: Mini batch size.
+  steps: Number of steps to run over data.
+  optimizer: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
+  learning_rate: If this is constant float value, no decay function is used.
+    Instead, a customized decay function can be passed that accepts
+    global_step as parameter and returns a Tensor.
+    e.g. exponential decay function:
+    def exp_decay(global_step):
+        return tf.train.exponential_decay(
+            learning_rate=0.1, global_step,
+            decay_steps=2, decay_rate=0.001)
+  class_weight: None or list of n_classes floats. Weight associated with
+    classes for loss computation. If not given, all classes are
+    supposed to have weight one.
+  continue_training: when continue_training is True, once initialized
+    model will be continuely trained on every call of fit.
+  config: RunConfig object that controls the configurations of the
+    session, e.g. num_cores, gpu_memory_fraction, etc.
+  dropout: When not None, the probability we will drop out a given coordinate.
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.__init__(hidden_units, n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1, dropout=None)` {#TensorFlowDNNClassifier.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.bias_` {#TensorFlowDNNClassifier.bias_}
+
+Returns bias of the DNN's bias layers.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowDNNClassifier.evaluate}
+
+See base class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowDNNClassifier.fit}
+
+Builds a neural network model given provided `model_fn` and training
+data X and y.
+
+Note: called first time constructs the graph and initializers
+variables. Consecutives times it will continue training the same model.
+This logic follows partial_fit() interface in scikit-learn.
+
+To restart learning, create new estimator.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class labels in classification, real numbers in regression).
+
+*  <b>`steps`</b>: int, number of steps to train.
+         If None or 0, train for `self.steps`.
+*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
+    invoke early stopping.
+*  <b>`logdir`</b>: the directory to save the log file that can be used for
+  optional visualization.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.get_params(deep=True)` {#TensorFlowDNNClassifier.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.get_tensor(name)` {#TensorFlowDNNClassifier.get_tensor}
+
+Returns tensor by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.get_tensor_value(name)` {#TensorFlowDNNClassifier.get_tensor_value}
+
+Returns value of the tensor give by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Numpy array - value of the tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.get_variable_names()` {#TensorFlowDNNClassifier.get_variable_names}
+
+Returns list of all variable names in this model.
+
+##### Returns:
+
+  List of names.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.model_dir` {#TensorFlowDNNClassifier.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.partial_fit(x, y)` {#TensorFlowDNNClassifier.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class label in classification, real numbers in regression).
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.predict(x, axis=1, batch_size=None)` {#TensorFlowDNNClassifier.predict}
+
+Predict class or regression for X.
+
+For a classification model, the predicted class for each sample in X is
+returned. For a regression model, the predicted value based on X is
+returned.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`axis`</b>: Which axis to argmax for classification.
+    By default axis 1 (next after batch) is used.
+    Use 2 for sequence predictions.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member
+    variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
+  value.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.predict_proba(x, batch_size=None)` {#TensorFlowDNNClassifier.predict_proba}
+
+Predict class probability of the input samples X.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
+  probabilities for each class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.restore(cls, path, config=None)` {#TensorFlowDNNClassifier.restore}
+
+Restores model from give path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Path to the checkpoints and other model information.
+*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
+      reconfigured.
+
+##### Returns:
+
+  Estimator, object of the subclass of TensorFlowEstimator.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.save(path)` {#TensorFlowDNNClassifier.save}
+
+Saves checkpoints and graph to given path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Folder to save model to.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.set_params(**params)` {#TensorFlowDNNClassifier.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.train(input_fn, steps, monitors=None)` {#TensorFlowDNNClassifier.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.weights_` {#TensorFlowDNNClassifier.weights_}
+
+Returns weights of the DNN weight layers.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowLinearClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowLinearClassifier.md
new file mode 100644
index 0000000000..469aa72b3a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowLinearClassifier.md
@@ -0,0 +1,279 @@
+TensorFlow Linear Classifier model.
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.__init__(n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowLinearClassifier.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.bias_` {#TensorFlowLinearClassifier.bias_}
+
+Returns weights of the linear classifier.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowLinearClassifier.evaluate}
+
+See base class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowLinearClassifier.fit}
+
+Builds a neural network model given provided `model_fn` and training
+data X and y.
+
+Note: called first time constructs the graph and initializers
+variables. Consecutives times it will continue training the same model.
+This logic follows partial_fit() interface in scikit-learn.
+
+To restart learning, create new estimator.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class labels in classification, real numbers in regression).
+
+*  <b>`steps`</b>: int, number of steps to train.
+         If None or 0, train for `self.steps`.
+*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
+    invoke early stopping.
+*  <b>`logdir`</b>: the directory to save the log file that can be used for
+  optional visualization.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.get_params(deep=True)` {#TensorFlowLinearClassifier.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.get_tensor(name)` {#TensorFlowLinearClassifier.get_tensor}
+
+Returns tensor by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.get_tensor_value(name)` {#TensorFlowLinearClassifier.get_tensor_value}
+
+Returns value of the tensor give by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Numpy array - value of the tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.get_variable_names()` {#TensorFlowLinearClassifier.get_variable_names}
+
+Returns list of all variable names in this model.
+
+##### Returns:
+
+  List of names.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.model_dir` {#TensorFlowLinearClassifier.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.partial_fit(x, y)` {#TensorFlowLinearClassifier.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class label in classification, real numbers in regression).
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.predict(x, axis=1, batch_size=None)` {#TensorFlowLinearClassifier.predict}
+
+Predict class or regression for X.
+
+For a classification model, the predicted class for each sample in X is
+returned. For a regression model, the predicted value based on X is
+returned.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`axis`</b>: Which axis to argmax for classification.
+    By default axis 1 (next after batch) is used.
+    Use 2 for sequence predictions.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member
+    variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
+  value.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.predict_proba(x, batch_size=None)` {#TensorFlowLinearClassifier.predict_proba}
+
+Predict class probability of the input samples X.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
+  probabilities for each class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.restore(cls, path, config=None)` {#TensorFlowLinearClassifier.restore}
+
+Restores model from give path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Path to the checkpoints and other model information.
+*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
+      reconfigured.
+
+##### Returns:
+
+  Estimator, object of the subclass of TensorFlowEstimator.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.save(path)` {#TensorFlowLinearClassifier.save}
+
+Saves checkpoints and graph to given path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Folder to save model to.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.set_params(**params)` {#TensorFlowLinearClassifier.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.train(input_fn, steps, monitors=None)` {#TensorFlowLinearClassifier.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.weights_` {#TensorFlowLinearClassifier.weights_}
+
+Returns weights of the linear classifier.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.extract_pandas_matrix.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.extract_pandas_matrix.md
deleted file mode 100644
index c2a275bc66..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.extract_pandas_matrix.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.contrib.learn.extract_pandas_matrix(data)` {#extract_pandas_matrix}
-
-Extracts numpy matrix from pandas DataFrame.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.infer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.infer.md
new file mode 100644
index 0000000000..616e74f3a4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.infer.md
@@ -0,0 +1,4 @@
+### `tf.contrib.learn.infer(restore_checkpoint_path, output_dict, feed_dict=None)` {#infer}
+
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.read_batch_record_features.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.read_batch_record_features.md
new file mode 100644
index 0000000000..aa4e964be1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.read_batch_record_features.md
@@ -0,0 +1,33 @@
+### `tf.contrib.learn.read_batch_record_features(file_pattern, batch_size, features, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name='dequeue_record_examples')` {#read_batch_record_features}
+
+Reads TFRecord, queues, batches and parses `Example` proto.
+
+See more detailed description in `read_examples`.
+
+##### Args:
+
+
+*  <b>`file_pattern`</b>: List of files or pattern of file paths containing
+      `Example` records. See `tf.gfile.Glob` for pattern rules.
+*  <b>`batch_size`</b>: An int or scalar `Tensor` specifying the batch size to use.
+*  <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
+    `VarLenFeature` values.
+*  <b>`randomize_input`</b>: Whether the input should be randomized.
+*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
+    dataset. If None, cycles through the dataset forever. NOTE - If specified,
+    creates a variable that must be initialized, so call
+    tf.initialize_all_variables() as shown in the tests.
+*  <b>`queue_capacity`</b>: Capacity for input queue.
+*  <b>`reader_num_threads`</b>: The number of threads to read examples.
+*  <b>`parser_num_threads`</b>: The number of threads to parse examples.
+*  <b>`name`</b>: Name of resulting op.
+
+##### Returns:
+
+  A dict of `Tensor` or `SparseTensor` objects for each in `features`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: for invalid inputs.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.metrics.set_difference.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.metrics.set_difference.md
new file mode 100644
index 0000000000..452115a428
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.metrics.set_difference.md
@@ -0,0 +1,24 @@
+### `tf.contrib.metrics.set_difference(a, b, aminusb=True, validate_indices=True)` {#set_difference}
+
+Compute set difference of elements in last dimension of `a` and `b`.
+
+All but the last dimension of `a` and `b` must match.
+
+##### Args:
+
+
+*  <b>`a`</b>: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
+      must be sorted in row-major order.
+*  <b>`b`</b>: `Tensor` or `SparseTensor` of the same type as `a`. Must be
+      `SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
+      sorted in row-major order.
+*  <b>`aminusb`</b>: Whether to subtract `b` from `a`, vs vice versa.
+*  <b>`validate_indices`</b>: Whether to validate the order and range of sparse indices
+     in `a` and `b`.
+
+##### Returns:
+
+  A `SparseTensor` with the same rank as `a` and `b`, and all but the last
+  dimension the same. Elements along the last dimension contain the
+  differences.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.metrics.streaming_mean_relative_error.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.metrics.streaming_mean_relative_error.md
deleted file mode 100644
index 3740bbbaad..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.metrics.streaming_mean_relative_error.md
+++ /dev/null
@@ -1,49 +0,0 @@
-### `tf.contrib.metrics.streaming_mean_relative_error(predictions, labels, normalizer, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean_relative_error}
-
-Computes the mean relative error by normalizing with the given values.
-
-The `streaming_mean_relative_error` function creates two local variables,
-`total` and `count` that are used to compute the mean relative absolute error.
-This average is ultimately returned as `mean_relative_error`: an idempotent
-operation that simply divides `total` by `count`. To facilitate the estimation
-of the mean relative error over a stream of data, the function utilizes two
-operations. First, a `relative_errors` operation divides the absolute value
-of the differences between `predictions` and `labels` by the `normalizer`.
-Second, an `update_op` operation whose behavior is dependent on the value of
-`weights`. If `weights` is None, then `update_op` increments `total` with the
-reduced sum of `relative_errors` and increments `count` with the number of
-elements in `relative_errors`. If `weights` is not `None`, then `update_op`
-increments `total` with the reduced sum of the product of `weights` and
-`relative_errors` and increments `count` with the reduced sum of `weights`. In
-addition to performing the updates, `update_op` also returns the
-`mean_relative_error` value.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: A `Tensor` of arbitrary shape.
-*  <b>`labels`</b>: A `Tensor` of the same shape as `predictions`.
-*  <b>`normalizer`</b>: A `Tensor` of the same shape as `predictions`.
-*  <b>`weights`</b>: An optional set of weights of the same shape as `predictions`. If
-    `weights` is not None, the function computes a weighted mean.
-*  <b>`metrics_collections`</b>: An optional list of collections that
-    `mean_relative_error` should be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
-    be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`mean_relative_error`</b>: A tensor representing the current mean, the value of
-    `total` divided by `count`.
-*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
-    appropriately and whose value matches `mean_relative_error`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `weights` is not `None` and its shape doesn't match
-    `predictions` or if either `metrics_collections` or `updates_collections`
-    are not a list or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.metrics.streaming_recall_at_k.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.metrics.streaming_recall_at_k.md
deleted file mode 100644
index dd03b95b69..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.metrics.streaming_recall_at_k.md
+++ /dev/null
@@ -1,52 +0,0 @@
-### `tf.contrib.metrics.streaming_recall_at_k(predictions, labels, k, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_recall_at_k}
-
-Computes the recall@k of the predictions with respect to dense labels.
-
-The `streaming_recall_at_k` function creates two local variables, `total` and
-`count`, that are used to compute the recall@k frequency. This frequency is
-ultimately returned as `recall_at_<k>`: an idempotent operation that simply
-divides `total` by `count`. To facilitate the estimation of recall@k over a
-stream of data, the function utilizes two operations. First, an `in_top_k`
-operation computes a tensor with shape [batch_size] whose elements indicate
-whether or not the corresponding label is in the top `k` predictions of the
-`predictions` `Tensor`. Second, an `update_op` operation whose behavior is
-dependent on the value of `ignore_mask`. If `ignore_mask` is None, then
-`update_op` increments `total` with the number of elements of `in_top_k` that
-are set to `True` and increments `count` with the batch size. If `ignore_mask`
-is not `None`, then `update_op` increments `total` with the number of elements
-in `in_top_k` that are `True` whose corresponding element in `ignore_mask` is
-`False`. In addition to performing the updates, `update_op` also returns the
-recall value.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: A floating point tensor of dimension [batch_size, num_classes]
-*  <b>`labels`</b>: A tensor of dimension [batch_size] whose type is in `int32`,
-    `int64`.
-*  <b>`k`</b>: The number of top elements to look at for computing precision.
-*  <b>`ignore_mask`</b>: An optional, binary tensor whose size matches `labels`. If an
-    element of `ignore_mask` is True, the corresponding prediction and label
-    pair is used to compute the metrics. Otherwise, the pair is ignored.
-*  <b>`metrics_collections`</b>: An optional list of collections that `recall_at_k`
-    should be added to.
-*  <b>`updates_collections`</b>: An optional list of collections `update_op` should be
-    added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`recall_at_k`</b>: A tensor representing the recall@k, the fraction of labels
-    which fall into the top `k` predictions.
-*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
-    appropriately and whose value matches `recall_at_k`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the dimensions of `predictions` and `labels` don't match or
-    if `ignore_mask` is not `None` and its shape doesn't match `predictions`
-    or if either `metrics_collections` or `updates_collections` are not a list
-    or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.decode_csv.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.decode_csv.md
deleted file mode 100644
index f2ebf6945b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.decode_csv.md
+++ /dev/null
@@ -1,26 +0,0 @@
-### `tf.decode_csv(records, record_defaults, field_delim=None, name=None)` {#decode_csv}
-
-Convert CSV records to tensors. Each column maps to one tensor.
-
-RFC 4180 format is expected for the CSV records.
-(https://tools.ietf.org/html/rfc4180)
-Note that we allow leading and trailing spaces with int or float field.
-
-##### Args:
-
-
-*  <b>`records`</b>: A `Tensor` of type `string`.
-    Each string is a record/row in the csv and all records should have
-    the same format.
-*  <b>`record_defaults`</b>: A list of `Tensor` objects with types from: `float32`, `int32`, `int64`, `string`.
-    One tensor per column of the input record, with either a
-    scalar default value for that column or empty if the column is required.
-*  <b>`field_delim`</b>: An optional `string`. Defaults to `","`.
-    delimiter to separate fields in a record.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A list of `Tensor` objects. Has the same type as `record_defaults`.
-  Each tensor will have the same shape as records.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.depth_to_space.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.depth_to_space.md
new file mode 100644
index 0000000000..c0117c82c7
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.depth_to_space.md
@@ -0,0 +1,95 @@
+### `tf.depth_to_space(input, block_size, name=None)` {#depth_to_space}
+
+DepthToSpace for tensors of type T.
+
+Rearranges data from depth into blocks of spatial data.
+This is the reverse transformation of SpaceToDepth. More specifically,
+this op outputs a copy of the input tensor where values from the `depth`
+dimension are moved in spatial blocks to the `height` and `width` dimensions.
+The attr `block_size` indicates the input block size and how the data is moved.
+
+  * Chunks of data of size `block_size * block_size` from depth are rearranged
+    into non-overlapping blocks of size `block_size x block_size`
+  * The width the output tensor is `input_depth * block_size`, whereas the
+    height is `input_height * block_size`.
+  * The depth of the input tensor must be divisible by
+    `block_size * block_size`.
+
+That is, assuming the input is in the shape:
+`[batch, height, width, depth]`,
+the shape of the output will be:
+`[batch, height*block_size, width*block_size, depth/(block_size*block_size)]`
+
+This operation requires that the input tensor be of rank 4, and that
+`block_size` be >=1 and that `block_size * block_size` be a divisor of the
+input depth.
+
+This operation is useful for resizing the activations between convolutions
+(but keeping all data), e.g. instead of pooling. It is also useful for training
+purely convolutional models.
+
+For example, given this input of shape `[1, 1, 1, 4]`, and a block size of 2:
+
+```prettyprint
+x = [[[[1, 2, 3, 4]]]]
+
+```
+
+This operation will output a tensor of shape `[1, 2, 2, 1]`:
+
+```prettyprint
+   [[[[1], [2]],
+     [[3], [4]]]]
+```
+
+Here, the input has a batch of 1 and each batch element has shape `[1, 1, 4]`,
+the corresponding output will have 2x2 elements and will have a depth of
+1 channel (1 = `4 / (block_size * block_size)`).
+The output element shape is `[2, 2, 1]`.
+
+For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`, e.g.
+
+```prettyprint
+x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
+```
+
+This operation, for block size of 2, will return the following tensor of shape
+`[1, 2, 2, 3]`
+
+```prettyprint
+   [[[[1, 2, 3], [4, 5, 6]],
+     [[7, 8, 9], [10, 11, 12]]]]
+
+```
+
+Similarly, for the following input of shape `[1 2 2 4]`, and a block size of 2:
+
+```prettyprint
+x =  [[[[1, 2, 3, 4],
+       [5, 6, 7, 8]],
+      [[9, 10, 11, 12],
+       [13, 14, 15, 16]]]]
+```
+
+the operator will return the following tensor of shape `[1 4 4 1]`:
+
+```prettyprint
+x = [[ [1],   [2],  [5],  [6]],
+     [ [3],   [4],  [7],  [8]],
+     [ [9],  [10], [13],  [14]],
+     [ [11], [12], [15],  [16]]]
+
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`.
+*  <b>`block_size`</b>: An `int`.
+    The size of the spatial block, same as in Space2Depth.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.erf.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.erf.md
new file mode 100644
index 0000000000..3a425b7c4a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.erf.md
@@ -0,0 +1,14 @@
+### `tf.erf(x, name=None)` {#erf}
+
+Computes the Gauss error function of `x` element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.errors.InternalError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.errors.InternalError.md
new file mode 100644
index 0000000000..dd229d2a3d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.errors.InternalError.md
@@ -0,0 +1,12 @@
+Raised when the system experiences an internal error.
+
+This exception is raised when some invariant expected by the runtime
+has been broken. Catching this exception is not recommended.
+
+- - -
+
+#### `tf.errors.InternalError.__init__(node_def, op, message)` {#InternalError.__init__}
+
+Creates an `InternalError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.errors.UnauthenticatedError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.errors.UnauthenticatedError.md
new file mode 100644
index 0000000000..d3344dc6b1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.errors.UnauthenticatedError.md
@@ -0,0 +1,11 @@
+The request does not have valid authentication credentials.
+
+This exception is not currently used.
+
+- - -
+
+#### `tf.errors.UnauthenticatedError.__init__(node_def, op, message)` {#UnauthenticatedError.__init__}
+
+Creates an `UnauthenticatedError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.errors.UnknownError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.errors.UnknownError.md
new file mode 100644
index 0000000000..3e18ec866b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.errors.UnknownError.md
@@ -0,0 +1,15 @@
+Unknown error.
+
+An example of where this error may be returned is if a Status value
+received from another address space belongs to an error-space that
+is not known to this address space. Also errors raised by APIs that
+do not return enough error information may be converted to this
+error.
+
+- - -
+
+#### `tf.errors.UnknownError.__init__(node_def, op, message, error_code=2)` {#UnknownError.__init__}
+
+Creates an `UnknownError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.exp.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.exp.md
new file mode 100644
index 0000000000..6bceeabd27
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.exp.md
@@ -0,0 +1,14 @@
+### `tf.exp(x, name=None)` {#exp}
+
+Computes exponential of x element-wise.  \\(y = e^x\\).
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.fft2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.fft2d.md
deleted file mode 100644
index e480dcb27e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.fft2d.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.fft2d(input, name=None)` {#fft2d}
-
-Compute the 2-dimensional discrete Fourier Transform.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 matrix.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`. The 2D Fourier Transform of `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.floor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.floor.md
deleted file mode 100644
index 4aadcff6ef..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.floor.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.floor(x, name=None)` {#floor}
-
-Returns element-wise largest integer not greater than x.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.floordiv.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.floordiv.md
deleted file mode 100644
index 8f824e867e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.floordiv.md
+++ /dev/null
@@ -1,32 +0,0 @@
-### `tf.floordiv(x, y, name=None)` {#floordiv}
-
-Divides `x / y` elementwise, rounding down for floating point.
-
-The same as `tf.div(x,y)` for integers, but uses `tf.floor(tf.div(x,y))` for
-floating point arguments so that the result is always an integer (though
-possibly an integer represented as floating point).  This op is generated by
-`x // y` floor division in Python 3 and in Python 2.7 with
-`from __future__ import division`.
-
-Note that for efficiency, `floordiv` uses C semantics for negative numbers
-(unlike Python and Numpy).
-
-`x` and `y` must have the same type, and the result will have the same type
-as well.
-
-##### Args:
-
-
-*  <b>`x`</b>: `Tensor` numerator of real numeric type.
-*  <b>`y`</b>: `Tensor` denominator of real numeric type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  `x / y` rounded down (except possibly towards zero for negative integers).
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If the inputs are complex.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.gather_nd.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.gather_nd.md
deleted file mode 100644
index 7c8777a660..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.gather_nd.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.gather_nd(params, indices, name=None)` {#gather_nd}
-
-Gather values from `params` according to `indices`.
-
-`indices` must be integer tensor, containing indices into `params`.
-It must be shape `[d_0, ..., d_N, R]` where `R` is the rank of `params`.
-The innermost dimension of `indices` (with length `R`) corresponds to the
-indices of `params`.
-
-Produces an output tensor with shape `[d_0, ..., d_{n-1}]` where:
-
-    output[i, j, k, ...] = params[indices[i, j, k, ..., :]]
-
-e.g. for `indices` a matrix:
-
-    output[i] = params[indices[i, :]]
-
-##### Args:
-
-
-*  <b>`params`</b>: A `Tensor`. R-D.  The tensor from which to gather values.
-*  <b>`indices`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    (N+1)-D.  Index tensor having shape `[d_0, ..., d_N, R]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `params`.
-  N-D.  Values from `params` gathered from indices given by `indices`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.get_default_session.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.get_default_session.md
new file mode 100644
index 0000000000..c564366e8b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.get_default_session.md
@@ -0,0 +1,16 @@
+### `tf.get_default_session()` {#get_default_session}
+
+Returns the default session for the current thread.
+
+The returned `Session` will be the innermost session on which a
+`Session` or `Session.as_default()` context has been entered.
+
+NOTE: The default session is a property of the current thread. If you
+create a new thread, and wish to use the default session in that
+thread, you must explicitly add a `with sess.as_default():` in that
+thread's function.
+
+##### Returns:
+
+  The default `Session` being used in the current thread.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.get_variable.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.get_variable.md
deleted file mode 100644
index 59e1a1797a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.get_variable.md
+++ /dev/null
@@ -1,72 +0,0 @@
-### `tf.get_variable(name, shape=None, dtype=tf.float32, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True)` {#get_variable}
-
-Gets an existing variable with these parameters or create a new one.
-
-This function prefixes the name with the current variable scope
-and performs reuse checks. See the
-[Variable Scope How To](../../how_tos/variable_scope/index.md)
-for an extensive description of how reusing works. Here is a basic example:
-
-```python
-with tf.variable_scope("foo"):
-    v = tf.get_variable("v", [1])  # v.name == "foo/v:0"
-    w = tf.get_variable("w", [1])  # w.name == "foo/w:0"
-with tf.variable_scope("foo", reuse=True)
-    v1 = tf.get_variable("v")  # The same as v above.
-```
-
-If initializer is `None` (the default), the default initializer passed in
-the variable scope will be used. If that one is `None` too, a
-`UniformUnitScalingInitializer` will be used. The initializer can also be
-a Tensor, in which case the variable is initialized to this value and shape.
-
-Similarly, if the regularizer is `None` (the default), the default regularizer
-passed in the variable scope will be used (if that is `None` too,
-then by default no regularization is performed).
-
-If a partitioner is provided, first a sharded `Variable` is created
-via `_get_partitioned_variable_list`, and the return value is a
-`Tensor` composed of the shards concatenated along the partition axis.
-
-Some useful partitioners are available.  See, e.g.,
-`variable_axis_size_partitioner`.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name of the new or existing variable.
-*  <b>`shape`</b>: Shape of the new or existing variable.
-*  <b>`dtype`</b>: Type of the new or existing variable (defaults to `DT_FLOAT`).
-*  <b>`initializer`</b>: Initializer for the variable if one is created.
-*  <b>`regularizer`</b>: A (Tensor -> Tensor or None) function; the result of
-    applying it on a newly created variable will be added to the collection
-    GraphKeys.REGULARIZATION_LOSSES and can be used for regularization.
-*  <b>`trainable`</b>: If `True` also add the variable to the graph collection
-    `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
-*  <b>`collections`</b>: List of graph collections keys to add the Variable to.
-    Defaults to `[GraphKeys.VARIABLES]` (see tf.Variable).
-    If partitioning is enabled and used, the concatenated return value
-    is also added to collection `GraphKeys.CONCATENATED_VARIABLES`.
-*  <b>`caching_device`</b>: Optional device string or function describing where the
-    Variable should be cached for reading.  Defaults to the Variable's
-    device.  If not `None`, caches on another device.  Typical use is to
-    cache on the device where the Ops using the Variable reside, to
-    deduplicate copying through `Switch` and other conditional statements.
-*  <b>`partitioner`</b>: Optional callable that accepts a fully defined `TensorShape`
-    and `dtype` of the Variable to be created, and returns a list of
-    partitions for each axis (currently only one axis can be partitioned).
-*  <b>`validate_shape`</b>: If False, allows the variable to be initialized with a
-      value of unknown shape. If True, the default, the shape of initial_value
-      must be known.
-
-##### Returns:
-
-  The created or existing variable.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: when creating a new variable and shape is not declared,
-    or when violating reuse during variable creation. Reuse is set inside
-    `variable_scope`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.ifft.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.ifft.md
deleted file mode 100644
index 26582404f6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.ifft.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.ifft(input, name=None)` {#ifft}
-
-Compute the inverse 1-dimensional discrete Fourier Transform.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 vector.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`.
-  The inverse 1D Fourier Transform of `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.image.decode_png.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.image.decode_png.md
deleted file mode 100644
index 4332af7704..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.image.decode_png.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.image.decode_png(contents, channels=None, dtype=None, name=None)` {#decode_png}
-
-Decode a PNG-encoded image to a uint8 or uint16 tensor.
-
-The attr `channels` indicates the desired number of color channels for the
-decoded image.
-
-Accepted values are:
-
-*   0: Use the number of channels in the PNG-encoded image.
-*   1: output a grayscale image.
-*   3: output an RGB image.
-*   4: output an RGBA image.
-
-If needed, the PNG-encoded image is transformed to match the requested number
-of color channels.
-
-##### Args:
-
-
-*  <b>`contents`</b>: A `Tensor` of type `string`. 0-D.  The PNG-encoded image.
-*  <b>`channels`</b>: An optional `int`. Defaults to `0`.
-    Number of color channels for the decoded image.
-*  <b>`dtype`</b>: An optional `tf.DType` from: `tf.uint8, tf.uint16`. Defaults to `tf.uint8`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `dtype`. 3-D with shape `[height, width, channels]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.image.per_image_whitening.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.image.per_image_whitening.md
deleted file mode 100644
index 8f72af6a31..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.image.per_image_whitening.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.image.per_image_whitening(image)` {#per_image_whitening}
-
-Linearly scales `image` to have zero mean and unit norm.
-
-This op computes `(x - mean) / adjusted_stddev`, where `mean` is the average
-of all values in image, and
-`adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements()))`.
-
-`stddev` is the standard deviation of all values in `image`. It is capped
-away from zero to protect against division by 0 when handling uniform images.
-
-Note that this implementation is limited:
-*  It only whitens based on the statistics of an individual image.
-*  It does not take into account the covariance structure.
-
-##### Args:
-
-
-*  <b>`image`</b>: 3-D tensor of shape `[height, width, channels]`.
-
-##### Returns:
-
-  The whitened image with same shape as `image`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if the shape of 'image' is incompatible with this function.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.image.random_flip_up_down.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.image.random_flip_up_down.md
new file mode 100644
index 0000000000..7ed36f5df2
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.image.random_flip_up_down.md
@@ -0,0 +1,24 @@
+### `tf.image.random_flip_up_down(image, seed=None)` {#random_flip_up_down}
+
+Randomly flips an image vertically (upside down).
+
+With a 1 in 2 chance, outputs the contents of `image` flipped along the first
+dimension, which is `height`.  Otherwise output the image as-is.
+
+##### Args:
+
+
+*  <b>`image`</b>: A 3-D tensor of shape `[height, width, channels].`
+*  <b>`seed`</b>: A Python integer. Used to create a random seed. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+
+##### Returns:
+
+  A 3-D tensor of the same type and shape as `image`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the shape of `image` not supported.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.import_graph_def.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.import_graph_def.md
new file mode 100644
index 0000000000..0ff3d621d4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.import_graph_def.md
@@ -0,0 +1,49 @@
+### `tf.import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None, producer_op_list=None)` {#import_graph_def}
+
+Imports the TensorFlow graph in `graph_def` into the Python `Graph`.
+
+This function provides a way to import a serialized TensorFlow
+[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
+protocol buffer, and extract individual objects in the `GraphDef` as
+[`Tensor`](#Tensor) and [`Operation`](#Operation) objects. See
+[`Graph.as_graph_def()`](#Graph.as_graph_def) for a way to create a
+`GraphDef` proto.
+
+##### Args:
+
+
+*  <b>`graph_def`</b>: A `GraphDef` proto containing operations to be imported into
+    the default graph.
+*  <b>`input_map`</b>: A dictionary mapping input names (as strings) in `graph_def`
+    to `Tensor` objects. The values of the named input tensors in the
+    imported graph will be re-mapped to the respective `Tensor` values.
+*  <b>`return_elements`</b>: A list of strings containing operation names in
+    `graph_def` that will be returned as `Operation` objects; and/or
+    tensor names in `graph_def` that will be returned as `Tensor` objects.
+*  <b>`name`</b>: (Optional.) A prefix that will be prepended to the names in
+    `graph_def`. Defaults to `"import"`.
+*  <b>`op_dict`</b>: (Optional.) A dictionary mapping op type names to `OpDef` protos.
+    Must contain an `OpDef` proto for each op type named in `graph_def`.
+    If omitted, uses the `OpDef` protos registered in the global registry.
+*  <b>`producer_op_list`</b>: (Optional.) An `OpList` proto with the (possibly stripped)
+    list of `OpDef`s used by the producer of the graph. If provided, attrs
+    for ops in `graph_def` that are not in `op_dict` that have their default
+    value according to `producer_op_list` will be removed. This will allow
+    some more `GraphDef`s produced by later binaries to be accepted by
+    earlier binaries.
+
+##### Returns:
+
+  A list of `Operation` and/or `Tensor` objects from the imported graph,
+  corresponding to the names in `return_elements`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `graph_def` is not a `GraphDef` proto,
+    `input_map` is not a dictionary mapping strings to `Tensor` objects,
+    or `return_elements` is not a list of strings.
+*  <b>`ValueError`</b>: If `input_map`, or `return_elements` contains names that
+    do not appear in `graph_def`, or `graph_def` is not well-formed (e.g.
+    it refers to an unknown tensor).
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.initialize_all_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.initialize_all_variables.md
new file mode 100644
index 0000000000..9a0e5d8261
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.initialize_all_variables.md
@@ -0,0 +1,10 @@
+### `tf.initialize_all_variables()` {#initialize_all_variables}
+
+Returns an Op that initializes all variables.
+
+This is just a shortcut for `initialize_variables(all_variables())`
+
+##### Returns:
+
+  An Op that initializes all variables in the graph.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.invert_permutation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.invert_permutation.md
deleted file mode 100644
index b12cc7e94c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.invert_permutation.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.invert_permutation(x, name=None)` {#invert_permutation}
-
-Computes the inverse permutation of a tensor.
-
-This operation computes the inverse of an index permutation. It takes a 1-D
-integer tensor `x`, which represents the indices of a zero-based array, and
-swaps each value with its index position. In other words, for an output tensor
-`y` and an input tensor `x`, this operation computes the following:
-
-`y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`
-
-The values must include 0. There can be no duplicate values or negative values.
-
-For example:
-
-```prettyprint
-# tensor `x` is [3, 4, 0, 2, 1]
-invert_permutation(x) ==> [2, 4, 3, 0, 1]
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` of type `int32`. 1-D.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `int32`. 1-D.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.is_nan.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.is_nan.md
new file mode 100644
index 0000000000..1bf3a6825c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.is_nan.md
@@ -0,0 +1,14 @@
+### `tf.is_nan(x, name=None)` {#is_nan}
+
+Returns which elements of x are NaN.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.local_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.local_variables.md
new file mode 100644
index 0000000000..b3612c7cbf
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.local_variables.md
@@ -0,0 +1,8 @@
+### `tf.local_variables()` {#local_variables}
+
+Returns all variables created with collection=[LOCAL_VARIABLES].
+
+##### Returns:
+
+  A list of local Variable objects.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.log.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.log.md
deleted file mode 100644
index 4ce9ddac8c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.log.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.log(x, name=None)` {#log}
-
-Computes natural logarithm of x element-wise.
-
-I.e., \\(y = \log_e x\\).
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matmul.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matmul.md
new file mode 100644
index 0000000000..6602562ecc
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matmul.md
@@ -0,0 +1,46 @@
+### `tf.matmul(a, b, transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=False, name=None)` {#matmul}
+
+Multiplies matrix `a` by matrix `b`, producing `a` * `b`.
+
+The inputs must be two-dimensional matrices, with matching inner dimensions,
+possibly after transposition.
+
+Both matrices must be of the same type. The supported types are:
+`float`, `double`, `int32`, `complex64`.
+
+Either matrix can be transposed on the fly by setting the corresponding flag
+to `True`. This is `False` by default.
+
+If one or both of the matrices contain a lot of zeros, a more efficient
+multiplication algorithm can be used by setting the corresponding
+`a_is_sparse` or `b_is_sparse` flag to `True`. These are `False` by default.
+
+For example:
+
+```python
+# 2-D tensor `a`
+a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3]) => [[1. 2. 3.]
+                                                      [4. 5. 6.]]
+# 2-D tensor `b`
+b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2]) => [[7. 8.]
+                                                         [9. 10.]
+                                                         [11. 12.]]
+c = tf.matmul(a, b) => [[58 64]
+                        [139 154]]
+```
+
+##### Args:
+
+
+*  <b>`a`</b>: `Tensor` of type `float`, `double`, `int32` or `complex64`.
+*  <b>`b`</b>: `Tensor` with same type as `a`.
+*  <b>`transpose_a`</b>: If `True`, `a` is transposed before multiplication.
+*  <b>`transpose_b`</b>: If `True`, `b` is transposed before multiplication.
+*  <b>`a_is_sparse`</b>: If `True`, `a` is treated as a sparse matrix.
+*  <b>`b_is_sparse`</b>: If `True`, `b` is treated as a sparse matrix.
+*  <b>`name`</b>: Name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of the same type as `a`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matrix_inverse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matrix_inverse.md
new file mode 100644
index 0000000000..4172badef5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matrix_inverse.md
@@ -0,0 +1,27 @@
+### `tf.matrix_inverse(input, adjoint=None, name=None)` {#matrix_inverse}
+
+Calculates the inverse of a square invertible matrix or its adjoint (conjugate
+
+transpose).
+
+The op uses LU decomposition with partial pivoting to compute the inverse.
+
+If the matrix is not invertible there is no guarantee what the op does. It
+may detect the condition and raise an exception or it may simply return a
+garbage result.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
+    Shape is `[M, M]`.
+*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+  Shape is `[M, M]`. If `adjoint` is `False` then `output` contains the
+  matrix inverse of `input`. If `adjoint` is `True` then `output` contains the
+  matrix inverse of the adjoint of `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matrix_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matrix_solve.md
deleted file mode 100644
index b33decd2e9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matrix_solve.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.matrix_solve(matrix, rhs, adjoint=None, name=None)` {#matrix_solve}
-
-Solves a system of linear equations. Checks for invertibility.
-
-##### Args:
-
-
-*  <b>`matrix`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
-    Shape is `[M, M]`.
-*  <b>`rhs`</b>: A `Tensor`. Must have the same type as `matrix`. Shape is `[M, K]`.
-*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
-    Boolean indicating whether to solve with `matrix` or its adjoint.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `matrix`.
-  Shape is `[M, K]`. If `adjoint` is `False` then `output` that solves
-  `matrix` * `output` = `rhs`. If `adjoint` is `True` then `output` that solves
-  `adjoint(matrix)` * `output` = `rhs`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matrix_triangular_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matrix_triangular_solve.md
new file mode 100644
index 0000000000..5787145231
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.matrix_triangular_solve.md
@@ -0,0 +1,35 @@
+### `tf.matrix_triangular_solve(matrix, rhs, lower=None, adjoint=None, name=None)` {#matrix_triangular_solve}
+
+Solves a system of linear equations with an upper or lower triangular matrix by
+
+backsubstitution.
+
+`matrix` is a matrix of shape `[M, M]`. If `lower` is `True` then the strictly
+upper triangular part of `matrix` is assumed to be zero and not accessed.
+If `lower` is False then the strictly lower triangular part of `matrix` is
+assumed to be zero and not accessed.
+`rhs` is a matrix of shape [M, K]`.
+
+The output is a matrix of shape `[M, K]`. If `adjoint` is `False` the output
+satisfies the matrix equation `matrix` * `output` = `rhs`.
+If `adjoint` is `False` then `output` satisfies the matrix equation
+`matrix` * `output` = `rhs`.
+If `adjoint` is `True` then `output` satisfies the matrix equation
+`adjoint(matrix)` * `output` = `rhs`.
+
+##### Args:
+
+
+*  <b>`matrix`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
+    Shape is `[M, M]`.
+*  <b>`rhs`</b>: A `Tensor`. Must have the same type as `matrix`. Shape is `[M, K]`.
+*  <b>`lower`</b>: An optional `bool`. Defaults to `True`.
+    Boolean indicating whether `matrix` is lower or upper triangular
+*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
+    Boolean indicating whether to solve with `matrix` or its adjoint.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `matrix`. Shape is `[M, K]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.mod.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.mod.md
deleted file mode 100644
index 5bfe1058a7..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.mod.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.mod(x, y, name=None)` {#mod}
-
-Returns element-wise remainder of division.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.dropout.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.dropout.md
deleted file mode 100644
index 4f2b7c0214..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.dropout.md
+++ /dev/null
@@ -1,38 +0,0 @@
-### `tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)` {#dropout}
-
-Computes dropout.
-
-With probability `keep_prob`, outputs the input element scaled up by
-`1 / keep_prob`, otherwise outputs `0`.  The scaling is so that the expected
-sum is unchanged.
-
-By default, each element is kept or dropped independently.  If `noise_shape`
-is specified, it must be
-[broadcastable](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-to the shape of `x`, and only dimensions with `noise_shape[i] == shape(x)[i]`
-will make independent decisions.  For example, if `shape(x) = [k, l, m, n]`
-and `noise_shape = [k, 1, 1, n]`, each batch and channel component will be
-kept independently and each row and column will be kept or not kept together.
-
-##### Args:
-
-
-*  <b>`x`</b>: A tensor.
-*  <b>`keep_prob`</b>: A scalar `Tensor` with the same type as x. The probability
-    that each element is kept.
-*  <b>`noise_shape`</b>: A 1-D `Tensor` of type `int32`, representing the
-    shape for randomly generated keep/drop flags.
-*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  A Tensor of the same shape of `x`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `keep_prob` is not in `(0, 1]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.in_top_k.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.in_top_k.md
new file mode 100644
index 0000000000..f46780649d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.in_top_k.md
@@ -0,0 +1,33 @@
+### `tf.nn.in_top_k(predictions, targets, k, name=None)` {#in_top_k}
+
+Says whether the targets are in the top `K` predictions.
+
+This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the
+prediction for the target class is among the top `k` predictions among
+all predictions for example `i`. Note that the behavior of `InTopK` differs
+from the `TopK` op in its handling of ties; if multiple classes have the
+same prediction value and straddle the top-`k` boundary, all of those
+classes are considered to be in the top `k`.
+
+More formally, let
+
+  \\(predictions_i\\) be the predictions for all classes for example `i`,
+  \\(targets_i\\) be the target class for example `i`,
+  \\(out_i\\) be the output for example `i`,
+
+$$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
+
+##### Args:
+
+
+*  <b>`predictions`</b>: A `Tensor` of type `float32`.
+    A `batch_size` x `classes` tensor.
+*  <b>`targets`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    A `batch_size` vector of class ids.
+*  <b>`k`</b>: An `int`. Number of top elements to look at for computing precision.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.l2_normalize.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.l2_normalize.md
deleted file mode 100644
index fdcdd71e20..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.l2_normalize.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None)` {#l2_normalize}
-
-Normalizes along dimension `dim` using an L2 norm.
-
-For a 1-D tensor with `dim = 0`, computes
-
-    output = x / sqrt(max(sum(x**2), epsilon))
-
-For `x` with more dimensions, independently normalizes each 1-D slice along
-dimension `dim`.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`.
-*  <b>`dim`</b>: Dimension along which to normalize.
-*  <b>`epsilon`</b>: A lower bound value for the norm. Will use `sqrt(epsilon)` as the
-    divisor if `norm < sqrt(epsilon)`.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  A `Tensor` with the same shape as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.learned_unigram_candidate_sampler.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.learned_unigram_candidate_sampler.md
new file mode 100644
index 0000000000..4f69938e59
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.learned_unigram_candidate_sampler.md
@@ -0,0 +1,53 @@
+### `tf.nn.learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)` {#learned_unigram_candidate_sampler}
+
+Samples a set of classes from a distribution learned during training.
+
+This operation randomly samples a tensor of sampled classes
+(`sampled_candidates`) from the range of integers `[0, range_max)`.
+
+The elements of `sampled_candidates` are drawn without replacement
+(if `unique=True`) or with replacement (if `unique=False`) from
+the base distribution.
+
+The base distribution for this operation is constructed on the fly
+during training.  It is a unigram distribution over the target
+classes seen so far during training.  Every integer in `[0, range_max)`
+begins with a weight of 1, and is incremented by 1 each time it is
+seen as a target class.  The base distribution is not saved to checkpoints,
+so it is reset when the model is reloaded.
+
+In addition, this operation returns tensors `true_expected_count`
+and `sampled_expected_count` representing the number of times each
+of the target classes (`true_classes`) and the sampled
+classes (`sampled_candidates`) is expected to occur in an average
+tensor of sampled classes.  These values correspond to `Q(y|x)`
+defined in [this
+document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
+If `unique=True`, then these are post-rejection probabilities and we
+compute them approximately.
+
+##### Args:
+
+
+*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
+    num_true]`. The target classes.
+*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
+*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
+*  <b>`unique`</b>: A `bool`. Determines whether all sampled classes in a batch are
+    unique.
+*  <b>`range_max`</b>: An `int`. The number of possible classes.
+*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+
+*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
+    The sampled classes.
+*  <b>`true_expected_count`</b>: A tensor of type `float`.  Same shape as
+    `true_classes`. The expected counts under the sampling distribution
+    of each of `true_classes`.
+*  <b>`sampled_expected_count`</b>: A tensor of type `float`. Same shape as
+    `sampled_candidates`. The expected counts under the sampling distribution
+    of each of `sampled_candidates`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.normalize_moments.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.normalize_moments.md
deleted file mode 100644
index d7a6b9cab4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.normalize_moments.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.nn.normalize_moments(counts, mean_ss, variance_ss, shift, name=None)` {#normalize_moments}
-
-Calculate the mean and variance of based on the sufficient statistics.
-
-##### Args:
-
-
-*  <b>`counts`</b>: A `Tensor` containing a the total count of the data (one value).
-*  <b>`mean_ss`</b>: A `Tensor` containing the mean sufficient statistics: the (possibly
-    shifted) sum of the elements to average over.
-*  <b>`variance_ss`</b>: A `Tensor` containing the variance sufficient statistics: the
-    (possibly shifted) squared sum of the data to compute the variance over.
-*  <b>`shift`</b>: A `Tensor` containing the value by which the data is shifted for
-    numerical stability, or `None` if no shift was performed.
-*  <b>`name`</b>: Name used to scope the operations that compute the moments.
-
-##### Returns:
-
-  Two `Tensor` objects: `mean` and `variance`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.relu6.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.relu6.md
new file mode 100644
index 0000000000..9695e557eb
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.nn.relu6.md
@@ -0,0 +1,15 @@
+### `tf.nn.relu6(features, name=None)` {#relu6}
+
+Computes Rectified Linear 6: `min(max(features, 0), 6)`.
+
+##### Args:
+
+
+*  <b>`features`</b>: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
+    `int16`, or `int8`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` with the same type as `features`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.ones.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.ones.md
deleted file mode 100644
index 8a4c9073d0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.ones.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.ones(shape, dtype=tf.float32, name=None)` {#ones}
-
-Creates a tensor with all elements set to 1.
-
-This operation returns a tensor of type `dtype` with shape `shape` and all
-elements set to 1.
-
-For example:
-
-```python
-tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]
-```
-
-##### Args:
-
-
-*  <b>`shape`</b>: Either a list of integers, or a 1-D `Tensor` of type `int32`.
-*  <b>`dtype`</b>: The type of an element in the resulting `Tensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` with all elements set to 1.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.pack.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.pack.md
deleted file mode 100644
index 75a5fbe15c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.pack.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.pack(values, name='pack')` {#pack}
-
-Packs a list of rank-`R` tensors into one rank-`(R+1)` tensor.
-
-Packs tensors in `values` into a tensor with rank one higher than each tensor
-in `values` and shape `[len(values)] + values[0].shape`. The output satisfies
-`output[i, ...] = values[i][...]`.
-
-This is the opposite of unpack.  The numpy equivalent is
-
-    tf.pack([x, y, z]) = np.asarray([x, y, z])
-
-##### Args:
-
-
-*  <b>`values`</b>: A list of `Tensor` objects with the same shape and type.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-
-*  <b>`output`</b>: A packed `Tensor` with the same type as `values`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.random_shuffle.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.random_shuffle.md
deleted file mode 100644
index 14f40d64af..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.random_shuffle.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.random_shuffle(value, seed=None, name=None)` {#random_shuffle}
-
-Randomly shuffles a tensor along its first dimension.
-
-The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
-to one and only one `output[i]`. For example, a mapping that might occur for a
-3x2 tensor is:
-
-```python
-[[1, 2],       [[5, 6],
- [3, 4],  ==>   [1, 2],
- [5, 6]]        [3, 4]]
-```
-
-##### Args:
-
-
-*  <b>`value`</b>: A Tensor to be shuffled.
-*  <b>`seed`</b>: A Python integer. Used to create a random seed for the distribution.
-    See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tensor of same shape and type as `value`, shuffled along its first
-  dimension.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.random_uniform_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.random_uniform_initializer.md
deleted file mode 100644
index 1afd318d3b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.random_uniform_initializer.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.random_uniform_initializer(minval=0.0, maxval=1.0, seed=None, dtype=tf.float32)` {#random_uniform_initializer}
-
-Returns an initializer that generates tensors with a uniform distribution.
-
-##### Args:
-
-
-*  <b>`minval`</b>: a python scalar or a scalar tensor. lower bound of the range
-    of random values to generate.
-*  <b>`maxval`</b>: a python scalar or a scalar tensor. upper bound of the range
-    of random values to generate.
-*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`dtype`</b>: The data type. Only floating point types are supported.
-
-##### Returns:
-
-  An initializer that generates tensors with a uniform distribution.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.reduce_min.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.reduce_min.md
deleted file mode 100644
index c93a902adc..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.reduce_min.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.reduce_min(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_min}
-
-Computes the minimum of elements across dimensions of a tensor.
-
-Reduces `input_tensor` along the dimensions given in `reduction_indices`.
-Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
-entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
-are retained with length 1.
-
-If `reduction_indices` has no entries, all dimensions are reduced, and a
-tensor with a single element is returned.
-
-##### Args:
-
-
-*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
-*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
-    reduces all dimensions.
-*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The reduced tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.scalar_mul.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.scalar_mul.md
deleted file mode 100644
index 5af291597d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.scalar_mul.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.scalar_mul(scalar, x)` {#scalar_mul}
-
-Multiplies a scalar times a `Tensor` or `IndexedSlices` object.
-
-Intended for use in gradient code which might deal with `IndexedSlices`
-objects, which are easy to multiply by a scalar but more expensive to
-multiply with arbitrary tensors.
-
-##### Args:
-
-
-*  <b>`scalar`</b>: A 0-D scalar `Tensor`. Must have known shape.
-*  <b>`x`</b>: A `Tensor` or `IndexedSlices` to be scaled.
-
-##### Returns:
-
-  `scalar * x` of the same type (`Tensor` or `IndexedSlices`) as `x`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if scalar is not a 0-D `scalar`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.scalar_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.scalar_summary.md
deleted file mode 100644
index 1e8c3479e4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.scalar_summary.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.scalar_summary(tags, values, collections=None, name=None)` {#scalar_summary}
-
-Outputs a `Summary` protocol buffer with scalar values.
-
-The input `tags` and `values` must have the same shape.  The generated
-summary has a summary value for each tag-value pair in `tags` and `values`.
-
-##### Args:
-
-
-*  <b>`tags`</b>: A `string` `Tensor`.  Tags for the summaries.
-*  <b>`values`</b>: A real numeric Tensor.  Values for the summaries.
-*  <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
-    added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A scalar `Tensor` of type `string`. The serialized `Summary` protocol
-  buffer.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.segment_max.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.segment_max.md
deleted file mode 100644
index c9d7a28900..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.segment_max.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.segment_max(data, segment_ids, name=None)` {#segment_max}
-
-Computes the maximum along segments of a tensor.
-
-Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation)
-for an explanation of segments.
-
-Computes a tensor such that
-\\(output_i = \max_j(data_j)\\) where `max` is over `j` such
-that `segment_ids[j] == i`.
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/SegmentMax.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    A 1-D tensor whose rank is equal to the rank of `data`'s
-    first dimension.  Values should be sorted and can be repeated.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `data`.
-  Has same shape as data, except for dimension 0 which
-  has size `k`, the number of segments.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.segment_mean.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.segment_mean.md
deleted file mode 100644
index 5d901859a9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.segment_mean.md
+++ /dev/null
@@ -1,32 +0,0 @@
-### `tf.segment_mean(data, segment_ids, name=None)` {#segment_mean}
-
-Computes the mean along segments of a tensor.
-
-Read [the section on
-Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
-of segments.
-
-Computes a tensor such that
-\\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is
-over `j` such that `segment_ids[j] == i` and `N` is the total number of
-values summed.
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/SegmentMean.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    A 1-D tensor whose rank is equal to the rank of `data`'s
-    first dimension.  Values should be sorted and can be repeated.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `data`.
-  Has same shape as data, except for dimension 0 which
-  has size `k`, the number of segments.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.sigmoid.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.sigmoid.md
deleted file mode 100644
index b056a48716..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.sigmoid.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.sigmoid(x, name=None)` {#sigmoid}
-
-Computes sigmoid of `x` element-wise.
-
-Specifically, `y = 1 / (1 + exp(-x))`.
-
-##### Args:
-
-
-*  <b>`x`</b>: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`,
-    or `qint32`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A Tensor with the same type as `x` if `x.dtype != qint32`
-    otherwise the return type is `quint8`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.space_to_depth.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.space_to_depth.md
new file mode 100644
index 0000000000..68706d2e5a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.space_to_depth.md
@@ -0,0 +1,87 @@
+### `tf.space_to_depth(input, block_size, name=None)` {#space_to_depth}
+
+SpaceToDepth for tensors of type T.
+
+Rearranges blocks of spatial data, into depth. More specifically,
+this op outputs a copy of the input tensor where values from the `height`
+and `width` dimensions are moved to the `depth` dimension.
+The attr `block_size` indicates the input block size and how the data is moved.
+
+  * Non-overlapping blocks of size `block_size x block size` are rearranged
+    into depth at each location.
+  * The depth of the output tensor is `input_depth * block_size * block_size`.
+  * The input tensor's height and width must be divisible by block_size.
+
+That is, assuming the input is in the shape:
+`[batch, height, width, depth]`,
+the shape of the output will be:
+`[batch, height/block_size, width/block_size, depth*block_size*block_size]`
+
+This operation requires that the input tensor be of rank 4, and that
+`block_size` be >=1 and a divisor of both the input `height` and `width`.
+
+This operation is useful for resizing the activations between convolutions
+(but keeping all data), e.g. instead of pooling. It is also useful for training
+purely convolutional models.
+
+For example, given this input of shape `[1, 2, 2, 1]`, and block_size of 2:
+
+```prettyprint
+x = [[[[1], [2]],
+      [[3], [4]]]]
+```
+
+This operation will output a tensor of shape `[1, 1, 1, 4]`:
+
+```prettyprint
+[[[[1, 2, 3, 4]]]]
+```
+
+Here, the input has a batch of 1 and each batch element has shape `[2, 2, 1]`,
+the corresponding output will have a single element (i.e. width and height are
+both 1) and will have a depth of 4 channels (1 * block_size * block_size).
+The output element shape is `[1, 1, 4]`.
+
+For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`, e.g.
+
+```prettyprint
+x = [[[[1, 2, 3], [4, 5, 6]],
+      [[7, 8, 9], [10, 11, 12]]]]
+```
+
+This operation, for block_size of 2, will return the following tensor of shape
+`[1, 1, 1, 12]`
+
+```prettyprint
+[[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
+```
+
+Similarly, for the following input of shape `[1 4 4 1]`, and a block size of 2:
+
+```prettyprint
+x = [[[[1],   [2],  [5],  [6]],
+      [[3],   [4],  [7],  [8]],
+      [[9],  [10], [13],  [14]],
+      [[11], [12], [15],  [16]]]]
+```
+
+the operator will return the following tensor of shape `[1 2 2 4]`:
+
+```prettyprint
+x = [[[[1, 2, 3, 4],
+       [5, 6, 7, 8]],
+      [[9, 10, 11, 12],
+       [13, 14, 15, 16]]]]
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`.
+*  <b>`block_size`</b>: An `int`. The size of the spatial block.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.sparse_segment_mean.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.sparse_segment_mean.md
deleted file mode 100644
index d95830b8a9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.sparse_segment_mean.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.sparse_segment_mean(data, indices, segment_ids, name=None)` {#sparse_segment_mean}
-
-Computes the mean along sparse segments of a tensor.
-
-Read [the section on
-Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
-of segments.
-
-Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first
-dimension, selecting a subset of dimension 0, specified by `indices`.
-
-##### Args:
-
-
-*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-*  <b>`indices`</b>: A `Tensor` of type `int32`.
-    A 1-D tensor. Has same rank as `segment_ids`.
-*  <b>`segment_ids`</b>: A `Tensor` of type `int32`.
-    A 1-D tensor. Values should be sorted and can be repeated.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `data`.
-  Has same shape as data, except for dimension 0 which
-  has size `k`, the number of segments.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.stop_gradient.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.stop_gradient.md
new file mode 100644
index 0000000000..53759f49ff
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.stop_gradient.md
@@ -0,0 +1,34 @@
+### `tf.stop_gradient(input, name=None)` {#stop_gradient}
+
+Stops gradient computation.
+
+When executed in a graph, this op outputs its input tensor as-is.
+
+When building ops to compute gradients, this op prevents the contribution of
+its inputs to be taken into account.  Normally, the gradient generator adds ops
+to a graph to compute the derivatives of a specified 'loss' by recursively
+finding out inputs that contributed to its computation.  If you insert this op
+in the graph it inputs are masked from the gradient generator.  They are not
+taken into account for computing gradients.
+
+This is useful any time you want to compute a value with TensorFlow but need
+to pretend that the value was a constant. Some examples include:
+
+*  The *EM* algorithm where the *M-step* should not involve backpropagation
+   through the output of the *E-step*.
+*  Contrastive divergence training of Boltzmann machines where, when
+   differentiating the energy function, the training must not backpropagate
+   through the graph that generated the samples from the model.
+*  Adversarial training, where no backprop should happen through the adversarial
+   example generation process.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.test.assert_equal_graph_def.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.test.assert_equal_graph_def.md
deleted file mode 100644
index 653236cf9f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.test.assert_equal_graph_def.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.test.assert_equal_graph_def(actual, expected)` {#assert_equal_graph_def}
-
-Asserts that two `GraphDef`s are (mostly) the same.
-
-Compares two `GraphDef` protos for equality, ignoring versions and ordering of
-nodes, attrs, and control inputs.  Node names are used to match up nodes
-between the graphs, so the naming of nodes must be consistent.
-
-##### Args:
-
-
-*  <b>`actual`</b>: The `GraphDef` we have.
-*  <b>`expected`</b>: The `GraphDef` we expected.
-
-##### Raises:
-
-
-*  <b>`AssertionError`</b>: If the `GraphDef`s do not match.
-*  <b>`TypeError`</b>: If either argument is not a `GraphDef`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.test.compute_gradient_error.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.test.compute_gradient_error.md
new file mode 100644
index 0000000000..d2c91a66b3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.test.compute_gradient_error.md
@@ -0,0 +1,36 @@
+### `tf.test.compute_gradient_error(x, x_shape, y, y_shape, x_init_value=None, delta=0.001, init_targets=None)` {#compute_gradient_error}
+
+Computes the gradient error.
+
+Computes the maximum error for dy/dx between the computed Jacobian and the
+numerically estimated Jacobian.
+
+This function will modify the tensors passed in as it adds more operations
+and hence changing the consumers of the operations of the input tensors.
+
+This function adds operations to the current session. To compute the error
+using a particular device, such as a GPU, use the standard methods for
+setting a device (e.g. using with sess.graph.device() or setting a device
+function in the session constructor).
+
+##### Args:
+
+
+*  <b>`x`</b>: a tensor or list of tensors
+*  <b>`x_shape`</b>: the dimensions of x as a tuple or an array of ints. If x is a list,
+  then this is the list of shapes.
+
+*  <b>`y`</b>: a tensor
+*  <b>`y_shape`</b>: the dimensions of y as a tuple or an array of ints.
+*  <b>`x_init_value`</b>: (optional) a numpy array of the same shape as "x"
+    representing the initial value of x. If x is a list, this should be a list
+    of numpy arrays.  If this is none, the function will pick a random tensor
+    as the initial value.
+*  <b>`delta`</b>: (optional) the amount of perturbation.
+*  <b>`init_targets`</b>: list of targets to run to initialize model params.
+    TODO(mrry): Remove this argument.
+
+##### Returns:
+
+  The maximum error in between the two Jacobians.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.to_bfloat16.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.to_bfloat16.md
new file mode 100644
index 0000000000..3d55da1110
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.to_bfloat16.md
@@ -0,0 +1,19 @@
+### `tf.to_bfloat16(x, name='ToBFloat16')` {#to_bfloat16}
+
+Casts a tensor to type `bfloat16`.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` or `SparseTensor` with same shape as `x` with type `bfloat16`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `x` cannot be cast to the `bfloat16`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.to_float.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.to_float.md
new file mode 100644
index 0000000000..b45b49b982
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.to_float.md
@@ -0,0 +1,19 @@
+### `tf.to_float(x, name='ToFloat')` {#to_float}
+
+Casts a tensor to type `float32`.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` or `SparseTensor` with same shape as `x` with type `float32`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `x` cannot be cast to the `float32`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.LooperThread.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.LooperThread.md
deleted file mode 100644
index 046f35d718..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.LooperThread.md
+++ /dev/null
@@ -1,215 +0,0 @@
-A thread that runs code repeatedly, optionally on a timer.
-
-This thread class is intended to be used with a `Coordinator`.  It repeatedly
-runs code specified either as `target` and `args` or by the `run_loop()`
-method.
-
-Before each run the thread checks if the coordinator has requested stop.  In
-that case the looper thread terminates immediately.
-
-If the code being run raises an exception, that exception is reported to the
-coordinator and the thread terminates.  The coordinator will then request all
-the other threads it coordinates to stop.
-
-You typically pass looper threads to the supervisor `Join()` method.
-- - -
-
-#### `tf.train.LooperThread.__init__(coord, timer_interval_secs, target=None, args=None, kwargs=None)` {#LooperThread.__init__}
-
-Create a LooperThread.
-
-##### Args:
-
-
-*  <b>`coord`</b>: A Coordinator.
-*  <b>`timer_interval_secs`</b>: Time boundaries at which to call Run(), or None
-    if it should be called back to back.
-*  <b>`target`</b>: Optional callable object that will be executed in the thread.
-*  <b>`args`</b>: Optional arguments to pass to `target` when calling it.
-*  <b>`kwargs`</b>: Optional keyword arguments to pass to `target` when calling it.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If one of the arguments is invalid.
-
-
-- - -
-
-#### `tf.train.LooperThread.daemon` {#LooperThread.daemon}
-
-A boolean value indicating whether this thread is a daemon thread (True) or not (False).
-
-This must be set before start() is called, otherwise RuntimeError is
-raised. Its initial value is inherited from the creating thread; the
-main thread is not a daemon thread and therefore all threads created in
-the main thread default to daemon = False.
-
-The entire Python program exits when no alive non-daemon threads are
-left.
-
-
-- - -
-
-#### `tf.train.LooperThread.getName()` {#LooperThread.getName}
-
-
-
-
-- - -
-
-#### `tf.train.LooperThread.ident` {#LooperThread.ident}
-
-Thread identifier of this thread or None if it has not been started.
-
-This is a nonzero integer. See the thread.get_ident() function. Thread
-identifiers may be recycled when a thread exits and another thread is
-created. The identifier is available even after the thread has exited.
-
-
-- - -
-
-#### `tf.train.LooperThread.isAlive()` {#LooperThread.isAlive}
-
-Return whether the thread is alive.
-
-This method returns True just before the run() method starts until just
-after the run() method terminates. The module function enumerate()
-returns a list of all alive threads.
-
-
-- - -
-
-#### `tf.train.LooperThread.isDaemon()` {#LooperThread.isDaemon}
-
-
-
-
-- - -
-
-#### `tf.train.LooperThread.is_alive()` {#LooperThread.is_alive}
-
-Return whether the thread is alive.
-
-This method returns True just before the run() method starts until just
-after the run() method terminates. The module function enumerate()
-returns a list of all alive threads.
-
-
-- - -
-
-#### `tf.train.LooperThread.join(timeout=None)` {#LooperThread.join}
-
-Wait until the thread terminates.
-
-This blocks the calling thread until the thread whose join() method is
-called terminates -- either normally or through an unhandled exception
-or until the optional timeout occurs.
-
-When the timeout argument is present and not None, it should be a
-floating point number specifying a timeout for the operation in seconds
-(or fractions thereof). As join() always returns None, you must call
-isAlive() after join() to decide whether a timeout happened -- if the
-thread is still alive, the join() call timed out.
-
-When the timeout argument is not present or None, the operation will
-block until the thread terminates.
-
-A thread can be join()ed many times.
-
-join() raises a RuntimeError if an attempt is made to join the current
-thread as that would cause a deadlock. It is also an error to join() a
-thread before it has been started and attempts to do so raises the same
-exception.
-
-
-- - -
-
-#### `tf.train.LooperThread.loop(coord, timer_interval_secs, target, args=None, kwargs=None)` {#LooperThread.loop}
-
-Start a LooperThread that calls a function periodically.
-
-If `timer_interval_secs` is None the thread calls `target(args)`
-repeatedly.  Otherwise `target(args)` is called every `timer_interval_secs`
-seconds.  The thread terminates when a stop of the coordinator is
-requested.
-
-##### Args:
-
-
-*  <b>`coord`</b>: A Coordinator.
-*  <b>`timer_interval_secs`</b>: Number. Time boundaries at which to call `target`.
-*  <b>`target`</b>: A callable object.
-*  <b>`args`</b>: Optional arguments to pass to `target` when calling it.
-*  <b>`kwargs`</b>: Optional keyword arguments to pass to `target` when calling it.
-
-##### Returns:
-
-  The started thread.
-
-
-- - -
-
-#### `tf.train.LooperThread.name` {#LooperThread.name}
-
-A string used for identification purposes only.
-
-It has no semantics. Multiple threads may be given the same name. The
-initial name is set by the constructor.
-
-
-- - -
-
-#### `tf.train.LooperThread.run()` {#LooperThread.run}
-
-
-
-
-- - -
-
-#### `tf.train.LooperThread.run_loop()` {#LooperThread.run_loop}
-
-Called at 'timer_interval_secs' boundaries.
-
-
-- - -
-
-#### `tf.train.LooperThread.setDaemon(daemonic)` {#LooperThread.setDaemon}
-
-
-
-
-- - -
-
-#### `tf.train.LooperThread.setName(name)` {#LooperThread.setName}
-
-
-
-
-- - -
-
-#### `tf.train.LooperThread.start()` {#LooperThread.start}
-
-Start the thread's activity.
-
-It must be called at most once per thread object. It arranges for the
-object's run() method to be invoked in a separate thread of control.
-
-This method will raise a RuntimeError if called more than once on the
-same thread object.
-
-
-- - -
-
-#### `tf.train.LooperThread.start_loop()` {#LooperThread.start_loop}
-
-Called when the thread starts.
-
-
-- - -
-
-#### `tf.train.LooperThread.stop_loop()` {#LooperThread.stop_loop}
-
-Called when the thread stops.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.MomentumOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.MomentumOptimizer.md
new file mode 100644
index 0000000000..45256f65fc
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.MomentumOptimizer.md
@@ -0,0 +1,18 @@
+Optimizer that implements the Momentum algorithm.
+
+- - -
+
+#### `tf.train.MomentumOptimizer.__init__(learning_rate, momentum, use_locking=False, name='Momentum')` {#MomentumOptimizer.__init__}
+
+Construct a new Momentum optimizer.
+
+##### Args:
+
+
+*  <b>`learning_rate`</b>: A `Tensor` or a floating point value.  The learning rate.
+*  <b>`momentum`</b>: A `Tensor` or a floating point value.  The momentum.
+*  <b>`use_locking`</b>: If `True` use locks for update operations.
+*  <b>`name`</b>: Optional name prefix for the operations created when applying
+    gradients.  Defaults to "Momentum".
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.Saver.from_proto.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.Saver.from_proto.md
new file mode 100644
index 0000000000..247f621e8a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.Saver.from_proto.md
@@ -0,0 +1,4 @@
+#### `tf.train.Saver.from_proto(saver_def)` {#Saver.from_proto}
+
+Returns a `Saver` object created from `saver_def`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.Saver.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.Saver.md
new file mode 100644
index 0000000000..8bf255040e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.Saver.md
@@ -0,0 +1,315 @@
+Saves and restores variables.
+
+See [Variables](../../how_tos/variables/index.md)
+for an overview of variables, saving and restoring.
+
+The `Saver` class adds ops to save and restore variables to and from
+*checkpoints*.  It also provides convenience methods to run these ops.
+
+Checkpoints are binary files in a proprietary format which map variable names
+to tensor values.  The best way to examine the contents of a checkpoint is to
+load it using a `Saver`.
+
+Savers can automatically number checkpoint filenames with a provided counter.
+This lets you keep multiple checkpoints at different steps while training a
+model.  For example you can number the checkpoint filenames with the training
+step number.  To avoid filling up disks, savers manage checkpoint files
+automatically. For example, they can keep only the N most recent files, or
+one checkpoint for every N hours of training.
+
+You number checkpoint filenames by passing a value to the optional
+`global_step` argument to `save()`:
+
+```python
+saver.save(sess, 'my-model', global_step=0) ==> filename: 'my-model-0'
+...
+saver.save(sess, 'my-model', global_step=1000) ==> filename: 'my-model-1000'
+```
+
+Additionally, optional arguments to the `Saver()` constructor let you control
+the proliferation of checkpoint files on disk:
+
+* `max_to_keep` indicates the maximum number of recent checkpoint files to
+  keep.  As new files are created, older files are deleted.  If None or 0,
+  all checkpoint files are kept.  Defaults to 5 (that is, the 5 most recent
+  checkpoint files are kept.)
+
+* `keep_checkpoint_every_n_hours`: In addition to keeping the most recent
+  `max_to_keep` checkpoint files, you might want to keep one checkpoint file
+  for every N hours of training.  This can be useful if you want to later
+  analyze how a model progressed during a long training session.  For
+  example, passing `keep_checkpoint_every_n_hours=2` ensures that you keep
+  one checkpoint file for every 2 hours of training.  The default value of
+  10,000 hours effectively disables the feature.
+
+Note that you still have to call the `save()` method to save the model.
+Passing these arguments to the constructor will not save variables
+automatically for you.
+
+A training program that saves regularly looks like:
+
+```python
+...
+# Create a saver.
+saver = tf.train.Saver(...variables...)
+# Launch the graph and train, saving the model every 1,000 steps.
+sess = tf.Session()
+for step in xrange(1000000):
+    sess.run(..training_op..)
+    if step % 1000 == 0:
+        # Append the step number to the checkpoint name:
+        saver.save(sess, 'my-model', global_step=step)
+```
+
+In addition to checkpoint files, savers keep a protocol buffer on disk with
+the list of recent checkpoints. This is used to manage numbered checkpoint
+files and by `latest_checkpoint()`, which makes it easy to discover the path
+to the most recent checkpoint. That protocol buffer is stored in a file named
+'checkpoint' next to the checkpoint files.
+
+If you create several savers, you can specify a different filename for the
+protocol buffer file in the call to `save()`.
+
+- - -
+
+#### `tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None)` {#Saver.__init__}
+
+Creates a `Saver`.
+
+The constructor adds ops to save and restore variables.
+
+`var_list` specifies the variables that will be saved and restored. It can
+be passed as a `dict` or a list:
+
+* A `dict` of names to variables: The keys are the names that will be
+  used to save or restore the variables in the checkpoint files.
+* A list of variables: The variables will be keyed with their op name in
+  the checkpoint files.
+
+For example:
+
+```python
+v1 = tf.Variable(..., name='v1')
+v2 = tf.Variable(..., name='v2')
+
+# Pass the variables as a dict:
+saver = tf.train.Saver({'v1': v1, 'v2': v2})
+
+# Or pass them as a list.
+saver = tf.train.Saver([v1, v2])
+# Passing a list is equivalent to passing a dict with the variable op names
+# as keys:
+saver = tf.train.Saver({v.op.name: v for v in [v1, v2]})
+```
+
+The optional `reshape` argument, if `True`, allows restoring a variable from
+a save file where the variable had a different shape, but the same number
+of elements and type.  This is useful if you have reshaped a variable and
+want to reload it from an older checkpoint.
+
+The optional `sharded` argument, if `True`, instructs the saver to shard
+checkpoints per device.
+
+##### Args:
+
+
+*  <b>`var_list`</b>: A list of `Variable` objects or a dictionary mapping names to
+    variables.  If `None`, defaults to the list of all variables.
+*  <b>`reshape`</b>: If `True`, allows restoring parameters from a checkpoint
+    where the variables have a different shape.
+*  <b>`sharded`</b>: If `True`, shard the checkpoints, one per device.
+*  <b>`max_to_keep`</b>: Maximum number of recent checkpoints to keep.
+    Defaults to 5.
+*  <b>`keep_checkpoint_every_n_hours`</b>: How often to keep checkpoints.
+    Defaults to 10,000 hours.
+*  <b>`name`</b>: String.  Optional name to use as a prefix when adding operations.
+*  <b>`restore_sequentially`</b>: A `Bool`, which if true, causes restore of different
+    variables to happen sequentially within each device.  This can lower
+    memory usage when restoring very large models.
+*  <b>`saver_def`</b>: Optional `SaverDef` proto to use instead of running the
+    builder. This is only useful for specialty code that wants to recreate
+    a `Saver` object for a previously built `Graph` that had a `Saver`.
+    The `saver_def` proto should be the one returned by the
+    `as_saver_def()` call of the `Saver` that was created for that `Graph`.
+*  <b>`builder`</b>: Optional `SaverBuilder` to use if a `saver_def` was not provided.
+    Defaults to `BaseSaverBuilder()`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `var_list` is invalid.
+*  <b>`ValueError`</b>: If any of the keys or values in `var_list` are not unique.
+
+
+- - -
+
+#### `tf.train.Saver.save(sess, save_path, global_step=None, latest_filename=None, meta_graph_suffix='meta', write_meta_graph=True)` {#Saver.save}
+
+Saves variables.
+
+This method runs the ops added by the constructor for saving variables.
+It requires a session in which the graph was launched.  The variables to
+save must also have been initialized.
+
+The method returns the path of the newly created checkpoint file.  This
+path can be passed directly to a call to `restore()`.
+
+##### Args:
+
+
+*  <b>`sess`</b>: A Session to use to save the variables.
+*  <b>`save_path`</b>: String.  Path to the checkpoint filename.  If the saver is
+    `sharded`, this is the prefix of the sharded checkpoint filename.
+*  <b>`global_step`</b>: If provided the global step number is appended to
+    `save_path` to create the checkpoint filename. The optional argument
+    can be a `Tensor`, a `Tensor` name or an integer.
+*  <b>`latest_filename`</b>: Optional name for the protocol buffer file that will
+    contains the list of most recent checkpoint filenames.  That file,
+    kept in the same directory as the checkpoint files, is automatically
+    managed by the saver to keep track of recent checkpoints.  Defaults to
+    'checkpoint'.
+*  <b>`meta_graph_suffix`</b>: Suffix for `MetaGraphDef` file. Defaults to 'meta'.
+*  <b>`write_meta_graph`</b>: `Boolean` indicating whether or not to write the meta
+    graph file.
+
+##### Returns:
+
+  A string: path at which the variables were saved.  If the saver is
+    sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn'
+    is the number of shards created.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sess` is not a `Session`.
+*  <b>`ValueError`</b>: If `latest_filename` contains path components.
+
+
+- - -
+
+#### `tf.train.Saver.restore(sess, save_path)` {#Saver.restore}
+
+Restores previously saved variables.
+
+This method runs the ops added by the constructor for restoring variables.
+It requires a session in which the graph was launched.  The variables to
+restore do not have to have been initialized, as restoring is itself a way
+to initialize variables.
+
+The `save_path` argument is typically a value previously returned from a
+`save()` call, or a call to `latest_checkpoint()`.
+
+##### Args:
+
+
+*  <b>`sess`</b>: A `Session` to use to restore the parameters.
+*  <b>`save_path`</b>: Path where parameters were previously saved.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the given `save_path` does not point to a file.
+
+
+
+Other utility methods.
+
+- - -
+
+#### `tf.train.Saver.last_checkpoints` {#Saver.last_checkpoints}
+
+List of not-yet-deleted checkpoint filenames.
+
+You can pass any of the returned values to `restore()`.
+
+##### Returns:
+
+  A list of checkpoint filenames, sorted from oldest to newest.
+
+
+- - -
+
+#### `tf.train.Saver.set_last_checkpoints(last_checkpoints)` {#Saver.set_last_checkpoints}
+
+DEPRECATED: Use set_last_checkpoints_with_time.
+
+Sets the list of old checkpoint filenames.
+
+##### Args:
+
+
+*  <b>`last_checkpoints`</b>: A list of checkpoint filenames.
+
+##### Raises:
+
+
+*  <b>`AssertionError`</b>: If last_checkpoints is not a list.
+
+
+- - -
+
+#### `tf.train.Saver.as_saver_def()` {#Saver.as_saver_def}
+
+Generates a `SaverDef` representation of this saver.
+
+##### Returns:
+
+  A `SaverDef` proto.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.train.Saver.export_meta_graph(filename=None, collection_list=None, as_text=False)` {#Saver.export_meta_graph}
+
+Writes `MetaGraphDef` to save_path/filename.
+
+##### Args:
+
+
+*  <b>`filename`</b>: Optional meta_graph filename including the path.
+*  <b>`collection_list`</b>: List of string keys to collect.
+*  <b>`as_text`</b>: If `True`, writes the meta_graph as an ASCII proto.
+
+##### Returns:
+
+  A `MetaGraphDef` proto.
+
+
+- - -
+
+#### `tf.train.Saver.from_proto(saver_def)` {#Saver.from_proto}
+
+Returns a `Saver` object created from `saver_def`.
+
+
+- - -
+
+#### `tf.train.Saver.set_last_checkpoints_with_time(last_checkpoints_with_time)` {#Saver.set_last_checkpoints_with_time}
+
+Sets the list of old checkpoint filenames and timestamps.
+
+##### Args:
+
+
+*  <b>`last_checkpoints_with_time`</b>: A list of tuples of checkpoint filenames and
+    timestamps.
+
+##### Raises:
+
+
+*  <b>`AssertionError`</b>: If last_checkpoints_with_time is not a list.
+
+
+- - -
+
+#### `tf.train.Saver.to_proto()` {#Saver.to_proto}
+
+Converts this `Saver` to a `SaverDef` protocol buffer.
+
+##### Returns:
+
+  A `SaverDef` protocol buffer.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.SummaryWriter.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.SummaryWriter.md
new file mode 100644
index 0000000000..a7f5aef5f1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.SummaryWriter.md
@@ -0,0 +1,170 @@
+Writes `Summary` protocol buffers to event files.
+
+The `SummaryWriter` class provides a mechanism to create an event file in a
+given directory and add summaries and events to it. The class updates the
+file contents asynchronously. This allows a training program to call methods
+to add data to the file directly from the training loop, without slowing down
+training.
+
+- - -
+
+#### `tf.train.SummaryWriter.__init__(logdir, graph=None, max_queue=10, flush_secs=120, graph_def=None)` {#SummaryWriter.__init__}
+
+Creates a `SummaryWriter` and an event file.
+
+On construction the summary writer creates a new event file in `logdir`.
+This event file will contain `Event` protocol buffers constructed when you
+call one of the following functions: `add_summary()`, `add_session_log()`,
+`add_event()`, or `add_graph()`.
+
+If you pass a `Graph` to the constructor it is added to
+the event file. (This is equivalent to calling `add_graph()` later).
+
+TensorBoard will pick the graph from the file and display it graphically so
+you can interactively explore the graph you built. You will usually pass
+the graph from the session in which you launched it:
+
+```python
+...create a graph...
+# Launch the graph in a session.
+sess = tf.Session()
+# Create a summary writer, add the 'graph' to the event file.
+writer = tf.train.SummaryWriter(<some-directory>, sess.graph)
+```
+
+The other arguments to the constructor control the asynchronous writes to
+the event file:
+
+*  `flush_secs`: How often, in seconds, to flush the added summaries
+   and events to disk.
+*  `max_queue`: Maximum number of summaries or events pending to be
+   written to disk before one of the 'add' calls block.
+
+##### Args:
+
+
+*  <b>`logdir`</b>: A string. Directory where event file will be written.
+*  <b>`graph`</b>: A `Graph` object, such as `sess.graph`.
+*  <b>`max_queue`</b>: Integer. Size of the queue for pending events and summaries.
+*  <b>`flush_secs`</b>: Number. How often, in seconds, to flush the
+    pending events and summaries to disk.
+*  <b>`graph_def`</b>: DEPRECATED: Use the `graph` argument instead.
+
+
+
+- - -
+
+#### `tf.train.SummaryWriter.add_summary(summary, global_step=None)` {#SummaryWriter.add_summary}
+
+Adds a `Summary` protocol buffer to the event file.
+
+This method wraps the provided summary in an `Event` protocol buffer
+and adds it to the event file.
+
+You can pass the result of evaluating any summary op, using
+[`Session.run()`](client.md#Session.run) or
+[`Tensor.eval()`](framework.md#Tensor.eval), to this
+function. Alternatively, you can pass a `tf.Summary` protocol
+buffer that you populate with your own data. The latter is
+commonly done to report evaluation results in event files.
+
+##### Args:
+
+
+*  <b>`summary`</b>: A `Summary` protocol buffer, optionally serialized as a string.
+*  <b>`global_step`</b>: Number. Optional global step value to record with the
+    summary.
+
+
+- - -
+
+#### `tf.train.SummaryWriter.add_session_log(session_log, global_step=None)` {#SummaryWriter.add_session_log}
+
+Adds a `SessionLog` protocol buffer to the event file.
+
+This method wraps the provided session in an `Event` procotol buffer
+and adds it to the event file.
+
+##### Args:
+
+
+*  <b>`session_log`</b>: A `SessionLog` protocol buffer.
+*  <b>`global_step`</b>: Number. Optional global step value to record with the
+    summary.
+
+
+- - -
+
+#### `tf.train.SummaryWriter.add_event(event)` {#SummaryWriter.add_event}
+
+Adds an event to the event file.
+
+##### Args:
+
+
+*  <b>`event`</b>: An `Event` protocol buffer.
+
+
+- - -
+
+#### `tf.train.SummaryWriter.add_graph(graph, global_step=None, graph_def=None)` {#SummaryWriter.add_graph}
+
+Adds a `Graph` to the event file.
+
+The graph described by the protocol buffer will be displayed by
+TensorBoard. Most users pass a graph in the constructor instead.
+
+##### Args:
+
+
+*  <b>`graph`</b>: A `Graph` object, such as `sess.graph`.
+*  <b>`global_step`</b>: Number. Optional global step counter to record with the
+    graph.
+*  <b>`graph_def`</b>: DEPRECATED. Use the `graph` parameter instead.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If both graph and graph_def are passed to the method.
+
+
+- - -
+
+#### `tf.train.SummaryWriter.add_run_metadata(run_metadata, tag, global_step=None)` {#SummaryWriter.add_run_metadata}
+
+Adds a metadata information for a single session.run() call.
+
+##### Args:
+
+
+*  <b>`run_metadata`</b>: A `RunMetadata` protobuf object.
+*  <b>`tag`</b>: The tag name for this metadata.
+*  <b>`global_step`</b>: Number. Optional global step counter to record with the
+    StepStats.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the provided tag was already used for this type of event.
+
+
+
+- - -
+
+#### `tf.train.SummaryWriter.flush()` {#SummaryWriter.flush}
+
+Flushes the event file to disk.
+
+Call this method to make sure that all pending events have been written to
+disk.
+
+
+- - -
+
+#### `tf.train.SummaryWriter.close()` {#SummaryWriter.close}
+
+Flushes the event file to disk and close the file.
+
+Call this method when you do not need the summary writer anymore.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.export_meta_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.export_meta_graph.md
new file mode 100644
index 0000000000..c09e6783c6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.export_meta_graph.md
@@ -0,0 +1,24 @@
+### `tf.train.export_meta_graph(filename=None, meta_info_def=None, graph_def=None, saver_def=None, collection_list=None, as_text=False)` {#export_meta_graph}
+
+Returns `MetaGraphDef` proto. Optionally writes it to filename.
+
+This function exports the graph, saver, and collection objects into
+`MetaGraphDef` protocol buffer with the intension of it being imported
+at a later time or location to restart training, run inference, or be
+a subgraph.
+
+##### Args:
+
+
+*  <b>`filename`</b>: Optional filename including the path for writing the
+    generated `MetaGraphDef` protocol buffer.
+*  <b>`meta_info_def`</b>: `MetaInfoDef` protocol buffer.
+*  <b>`graph_def`</b>: `GraphDef` protocol buffer.
+*  <b>`saver_def`</b>: `SaverDef` protocol buffer.
+*  <b>`collection_list`</b>: List of string keys to collect.
+*  <b>`as_text`</b>: If `True`, writes the `MetaGraphDef` as an ASCII proto.
+
+##### Returns:
+
+  A `MetaGraphDef` proto.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.range_input_producer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.range_input_producer.md
deleted file mode 100644
index fa73440d88..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.range_input_producer.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.train.range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None)` {#range_input_producer}
-
-Produces the integers from 0 to limit-1 in a queue.
-
-##### Args:
-
-
-*  <b>`limit`</b>: An int32 scalar tensor.
-*  <b>`num_epochs`</b>: An integer (optional). If specified, `range_input_producer`
-    produces each integer `num_epochs` times before generating an
-    OutOfRange error. If not specified, `range_input_producer` can cycle
-    through the integers an unlimited number of times.
-*  <b>`shuffle`</b>: Boolean. If true, the integers are randomly shuffled within each
-    epoch.
-*  <b>`seed`</b>: An integer (optional). Seed used if shuffle == True.
-*  <b>`capacity`</b>: An integer. Sets the queue capacity.
-*  <b>`shared_name`</b>: (optional). If set, this queue will be shared under the given
-    name across multiple sessions.
-*  <b>`name`</b>: A name for the operations (optional).
-
-##### Returns:
-
-  A Queue with the output integers.  A `QueueRunner` for the Queue
-  is added to the current `Graph`'s `QUEUE_RUNNER` collection.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.shuffle_batch.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.shuffle_batch.md
deleted file mode 100644
index bf2591801b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.shuffle_batch.md
+++ /dev/null
@@ -1,74 +0,0 @@
-### `tf.train.shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, num_threads=1, seed=None, enqueue_many=False, shapes=None, shared_name=None, name=None)` {#shuffle_batch}
-
-Creates batches by randomly shuffling tensors.
-
-This function adds the following to the current `Graph`:
-
-* A shuffling queue into which tensors from `tensors` are enqueued.
-* A `dequeue_many` operation to create batches from the queue.
-* A `QueueRunner` to `QUEUE_RUNNER` collection, to enqueue the tensors
-  from `tensors`.
-
-If `enqueue_many` is `False`, `tensors` is assumed to represent a
-single example.  An input tensor with shape `[x, y, z]` will be output
-as a tensor with shape `[batch_size, x, y, z]`.
-
-If `enqueue_many` is `True`, `tensors` is assumed to represent a
-batch of examples, where the first dimension is indexed by example,
-and all members of `tensors` should have the same size in the
-first dimension.  If an input tensor has shape `[*, x, y, z]`, the
-output will have shape `[batch_size, x, y, z]`.
-
-The `capacity` argument controls the how long the prefetching is allowed to
-grow the queues.
-
-The returned operation is a dequeue operation and will throw
-`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
-operation is feeding another input queue, its queue runner will catch
-this exception, however, if this operation is used in your main thread
-you are responsible for catching this yourself.
-
-For example:
-
-```python
-# Creates batches of 32 images and 32 labels.
-image_batch, label_batch = tf.train.shuffle_batch(
-      [single_image, single_label],
-      batch_size=32,
-      num_threads=4,
-      capacity=50000,
-      min_after_dequeue=10000)
-```
-
-*N.B.:* You must ensure that either (i) the `shapes` argument is
-passed, or (ii) all of the tensors in `tensors` must have
-fully-defined shapes. `ValueError` will be raised if neither of
-these conditions holds.
-
-##### Args:
-
-
-*  <b>`tensors`</b>: The list or dictionary of tensors to enqueue.
-*  <b>`batch_size`</b>: The new batch size pulled from the queue.
-*  <b>`capacity`</b>: An integer. The maximum number of elements in the queue.
-*  <b>`min_after_dequeue`</b>: Minimum number elements in the queue after a
-    dequeue, used to ensure a level of mixing of elements.
-*  <b>`num_threads`</b>: The number of threads enqueuing `tensor_list`.
-*  <b>`seed`</b>: Seed for the random shuffling within the queue.
-*  <b>`enqueue_many`</b>: Whether each tensor in `tensor_list` is a single example.
-*  <b>`shapes`</b>: (Optional) The shapes for each example.  Defaults to the
-    inferred shapes for `tensor_list`.
-*  <b>`shared_name`</b>: (Optional) If set, this queue will be shared under the given
-    name across multiple sessions.
-*  <b>`name`</b>: (Optional) A name for the operations.
-
-##### Returns:
-
-  A list or dictionary of tensors with the types as `tensors`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be
-    inferred from the elements of `tensors`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.shuffle_batch_join.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.shuffle_batch_join.md
deleted file mode 100644
index ab9e1be4a3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.shuffle_batch_join.md
+++ /dev/null
@@ -1,68 +0,0 @@
-### `tf.train.shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, shared_name=None, name=None)` {#shuffle_batch_join}
-
-Create batches by randomly shuffling tensors.
-
-The `tensors_list` argument is a list of tuples of tensors, or a list of
-dictionaries of tensors.  Each element in the list is treated similarily
-to the `tensors` argument of `tf.train.shuffle_batch()`.
-
-This version enqueues a different list of tensors in different threads.
-It adds the following to the current `Graph`:
-
-* A shuffling queue into which tensors from `tensors_list` are enqueued.
-* A `dequeue_many` operation to create batches from the queue.
-* A `QueueRunner` to `QUEUE_RUNNER` collection, to enqueue the tensors
-  from `tensors_list`.
-
-`len(tensors_list)` threads will be started, with thread `i` enqueuing
-the tensors from `tensors_list[i]`. `tensors_list[i1][j]` must match
-`tensors_list[i2][j]` in type and shape, except in the first dimension if
-`enqueue_many` is true.
-
-If `enqueue_many` is `False`, each `tensors_list[i]` is assumed
-to represent a single example.  An input tensor with shape `[x, y, z]`
-will be output as a tensor with shape `[batch_size, x, y, z]`.
-
-If `enqueue_many` is `True`, `tensors_list[i]` is assumed to
-represent a batch of examples, where the first dimension is indexed
-by example, and all members of `tensors_list[i]` should have the
-same size in the first dimension.  If an input tensor has shape `[*, x,
-y, z]`, the output will have shape `[batch_size, x, y, z]`.
-
-The `capacity` argument controls the how long the prefetching is allowed to
-grow the queues.
-
-The returned operation is a dequeue operation and will throw
-`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
-operation is feeding another input queue, its queue runner will catch
-this exception, however, if this operation is used in your main thread
-you are responsible for catching this yourself.
-
-##### Args:
-
-
-*  <b>`tensors_list`</b>: A list of tuples or dictionaries of tensors to enqueue.
-*  <b>`batch_size`</b>: An integer. The new batch size pulled from the queue.
-*  <b>`capacity`</b>: An integer. The maximum number of elements in the queue.
-*  <b>`min_after_dequeue`</b>: Minimum number elements in the queue after a
-    dequeue, used to ensure a level of mixing of elements.
-*  <b>`seed`</b>: Seed for the random shuffling within the queue.
-*  <b>`enqueue_many`</b>: Whether each tensor in `tensor_list_list` is a single
-    example.
-*  <b>`shapes`</b>: (Optional) The shapes for each example.  Defaults to the
-    inferred shapes for `tensors_list[i]`.
-*  <b>`shared_name`</b>: (optional). If set, this queue will be shared under the given
-    name across multiple sessions.
-*  <b>`name`</b>: (Optional) A name for the operations.
-
-##### Returns:
-
-  A list or dictionary of tensors with the same number and types as
-  `tensors_list[i]`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be
-    inferred from the elements of `tensors_list`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.slice_input_producer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.slice_input_producer.md
new file mode 100644
index 0000000000..da888d0fc2
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.slice_input_producer.md
@@ -0,0 +1,35 @@
+### `tf.train.slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None)` {#slice_input_producer}
+
+Produces a slice of each `Tensor` in `tensor_list`.
+
+Implemented using a Queue -- a `QueueRunner` for the Queue
+is added to the current `Graph`'s `QUEUE_RUNNER` collection.
+
+##### Args:
+
+
+*  <b>`tensor_list`</b>: A list of `Tensor` objects. Every `Tensor` in
+    `tensor_list` must have the same size in the first dimension.
+*  <b>`num_epochs`</b>: An integer (optional). If specified, `slice_input_producer`
+    produces each slice `num_epochs` times before generating
+    an `OutOfRange` error. If not specified, `slice_input_producer` can cycle
+    through the slices an unlimited number of times.
+*  <b>`shuffle`</b>: Boolean. If true, the integers are randomly shuffled within each
+    epoch.
+*  <b>`seed`</b>: An integer (optional). Seed used if shuffle == True.
+*  <b>`capacity`</b>: An integer. Sets the queue capacity.
+*  <b>`shared_name`</b>: (optional). If set, this queue will be shared under the given
+    name across multiple sessions.
+*  <b>`name`</b>: A name for the operations (optional).
+
+##### Returns:
+
+  A list of tensors, one for each element of `tensor_list`.  If the tensor
+  in `tensor_list` has shape `[N, a, b, .., z]`, then the corresponding output
+  tensor will have shape `[a, b, ..., z]`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `slice_input_producer` produces nothing from `tensor_list`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.write_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.write_graph.md
deleted file mode 100644
index eea9025321..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.train.write_graph.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.train.write_graph(graph_def, logdir, name, as_text=True)` {#write_graph}
-
-Writes a graph proto to a file.
-
-The graph is written as a binary proto unless `as_text` is `True`.
-
-```python
-v = tf.Variable(0, name='my_variable')
-sess = tf.Session()
-tf.train.write_graph(sess.graph_def, '/tmp/my-model', 'train.pbtxt')
-```
-
-##### Args:
-
-
-*  <b>`graph_def`</b>: A `GraphDef` protocol buffer.
-*  <b>`logdir`</b>: Directory where to write the graph. This can refer to remote
-    filesystems, such as Google Cloud Storage (GCS).
-*  <b>`name`</b>: Filename for the graph.
-*  <b>`as_text`</b>: If `True`, writes the graph as an ASCII proto.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.truediv.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.truediv.md
new file mode 100644
index 0000000000..0ccb1b2217
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.truediv.md
@@ -0,0 +1,31 @@
+### `tf.truediv(x, y, name=None)` {#truediv}
+
+Divides x / y elementwise, always producing floating point results.
+
+The same as `tf.div` for floating point arguments, but casts integer arguments
+to floating point before dividing so that the result is always floating point.
+This op is generated by normal `x / y` division in Python 3 and in Python 2.7
+with `from __future__ import division`.  If you want integer division that
+rounds down, use `x // y` or `tf.floordiv`.
+
+`x` and `y` must have the same numeric type.  If the inputs are floating
+point, the output will have the same type.  If the inputs are integral, the
+inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`
+and `int64` (matching the behavior of Numpy).
+
+##### Args:
+
+
+*  <b>`x`</b>: `Tensor` numerator of numeric type.
+*  <b>`y`</b>: `Tensor` denominator of numeric type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  `x / y` evaluated in floating point.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `x` and `y` have different dtypes.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.variable_op_scope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.variable_op_scope.md
new file mode 100644
index 0000000000..e3ab6e5d2e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.variable_op_scope.md
@@ -0,0 +1,56 @@
+### `tf.variable_op_scope(values, name_or_scope, default_name=None, initializer=None, regularizer=None, caching_device=None, partitioner=None, reuse=None)` {#variable_op_scope}
+
+Returns a context manager for defining an op that creates variables.
+
+This context manager validates that the given `values` are from the
+same graph, ensures that graph is the default graph, and pushes a
+name scope and a variable scope.
+
+If `name_or_scope` is not None, it is used as is in the variable scope. If
+`scope` is None, then `default_name` is used.  In that case, if the same name
+has been previously used in the same scope, it will made unique be appending
+`_N` to it.
+
+This is intended to be used when defining generic ops and so reuse is always
+inherited.
+
+For example, to define a new Python op called `my_op_with_vars`:
+
+```python
+def my_op_with_vars(a, b, scope=None):
+  with tf.variable_op_scope([a, b], scope, "MyOp") as scope:
+    a = tf.convert_to_tensor(a, name="a")
+    b = tf.convert_to_tensor(b, name="b")
+    c = tf.get_variable('c')
+    # Define some computation that uses `a`, `b`, and `c`.
+    return foo_op(..., name=scope)
+```
+
+##### Args:
+
+
+*  <b>`values`</b>: The list of `Tensor` arguments that are passed to the op function.
+*  <b>`name_or_scope`</b>: The name argument that is passed to the op function,
+    this name_or_scope is not uniquified in the variable scope.
+*  <b>`default_name`</b>: The default name to use if the `name_or_scope` argument is
+    `None`, this name will be uniquified. If name_or_scope is provided it
+    won't be used and therefore it is not required and can be None.
+*  <b>`initializer`</b>: The default initializer to pass to variable scope.
+*  <b>`regularizer`</b>: The default regularizer for variables within this scope.
+*  <b>`caching_device`</b>: The default caching device for variables within this scope.
+*  <b>`partitioner`</b>: The default partitioner for variables within this scope.
+*  <b>`reuse`</b>: `True` or `None`; if `True`, we go into reuse mode for this scope as
+    well as all sub-scopes; if `None`, we just inherit the parent scope reuse.
+
+
+##### Returns:
+
+  A context manager for use in defining a Python op.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: when trying to reuse within a create scope, or create within
+    a reuse scope, or if reuse is not `None` or `True`.
+*  <b>`TypeError`</b>: when the types of some arguments are not appropriate.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.Graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.Graph.md
new file mode 100644
index 0000000000..762a117664
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.Graph.md
@@ -0,0 +1,783 @@
+A TensorFlow computation, represented as a dataflow graph.
+
+A `Graph` contains a set of
+[`Operation`](../../api_docs/python/framework.md#Operation) objects,
+which represent units of computation; and
+[`Tensor`](../../api_docs/python/framework.md#Tensor) objects, which represent
+the units of data that flow between operations.
+
+A default `Graph` is always registered, and accessible by calling
+[`tf.get_default_graph()`](../../api_docs/python/framework.md#get_default_graph).
+To add an operation to the default graph, simply call one of the functions
+that defines a new `Operation`:
+
+```
+c = tf.constant(4.0)
+assert c.graph is tf.get_default_graph()
+```
+
+Another typical usage involves the
+[`Graph.as_default()`](../../api_docs/python/framework.md#Graph.as_default)
+context manager, which overrides the current default graph for the
+lifetime of the context:
+
+```python
+g = tf.Graph()
+with g.as_default():
+  # Define operations and tensors in `g`.
+  c = tf.constant(30.0)
+  assert c.graph is g
+```
+
+Important note: This class *is not* thread-safe for graph construction. All
+operations should be created from a single thread, or external
+synchronization must be provided. Unless otherwise specified, all methods
+are not thread-safe.
+
+- - -
+
+#### `tf.Graph.__init__()` {#Graph.__init__}
+
+Creates a new, empty Graph.
+
+
+- - -
+
+#### `tf.Graph.as_default()` {#Graph.as_default}
+
+Returns a context manager that makes this `Graph` the default graph.
+
+This method should be used if you want to create multiple graphs
+in the same process. For convenience, a global default graph is
+provided, and all ops will be added to this graph if you do not
+create a new graph explicitly. Use this method with the `with` keyword
+to specify that ops created within the scope of a block should be
+added to this graph.
+
+The default graph is a property of the current thread. If you
+create a new thread, and wish to use the default graph in that
+thread, you must explicitly add a `with g.as_default():` in that
+thread's function.
+
+The following code examples are equivalent:
+
+```python
+# 1. Using Graph.as_default():
+g = tf.Graph()
+with g.as_default():
+  c = tf.constant(5.0)
+  assert c.graph is g
+
+# 2. Constructing and making default:
+with tf.Graph().as_default() as g:
+  c = tf.constant(5.0)
+  assert c.graph is g
+```
+
+##### Returns:
+
+  A context manager for using this graph as the default graph.
+
+
+- - -
+
+#### `tf.Graph.as_graph_def(from_version=None, add_shapes=False)` {#Graph.as_graph_def}
+
+Returns a serialized `GraphDef` representation of this graph.
+
+The serialized `GraphDef` can be imported into another `Graph`
+(using [`import_graph_def()`](#import_graph_def)) or used with the
+[C++ Session API](../../api_docs/cc/index.md).
+
+This method is thread-safe.
+
+##### Args:
+
+
+*  <b>`from_version`</b>: Optional.  If this is set, returns a `GraphDef`
+    containing only the nodes that were added to this graph since
+    its `version` property had the given value.
+*  <b>`add_shapes`</b>: If true, adds an "_output_shapes" list attr to each
+    node with the inferred shapes of each of its outputs.
+
+##### Returns:
+
+  A [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
+  protocol buffer.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the `graph_def` would be too large.
+
+
+- - -
+
+#### `tf.Graph.finalize()` {#Graph.finalize}
+
+Finalizes this graph, making it read-only.
+
+After calling `g.finalize()`, no new operations can be added to
+`g`.  This method is used to ensure that no operations are added
+to a graph when it is shared between multiple threads, for example
+when using a [`QueueRunner`](../../api_docs/python/train.md#QueueRunner).
+
+
+- - -
+
+#### `tf.Graph.finalized` {#Graph.finalized}
+
+True if this graph has been finalized.
+
+
+
+- - -
+
+#### `tf.Graph.control_dependencies(control_inputs)` {#Graph.control_dependencies}
+
+Returns a context manager that specifies control dependencies.
+
+Use with the `with` keyword to specify that all operations constructed
+within the context should have control dependencies on
+`control_inputs`. For example:
+
+```python
+with g.control_dependencies([a, b, c]):
+  # `d` and `e` will only run after `a`, `b`, and `c` have executed.
+  d = ...
+  e = ...
+```
+
+Multiple calls to `control_dependencies()` can be nested, and in
+that case a new `Operation` will have control dependencies on the union
+of `control_inputs` from all active contexts.
+
+```python
+with g.control_dependencies([a, b]):
+  # Ops constructed here run after `a` and `b`.
+  with g.control_dependencies([c, d]):
+    # Ops constructed here run after `a`, `b`, `c`, and `d`.
+```
+
+You can pass None to clear the control dependencies:
+
+```python
+with g.control_dependencies([a, b]):
+  # Ops constructed here run after `a` and `b`.
+  with g.control_dependencies(None):
+    # Ops constructed here run normally, not waiting for either `a` or `b`.
+    with g.control_dependencies([c, d]):
+      # Ops constructed here run after `c` and `d`, also not waiting
+      # for either `a` or `b`.
+```
+
+*N.B.* The control dependencies context applies *only* to ops that
+are constructed within the context. Merely using an op or tensor
+in the context does not add a control dependency. The following
+example illustrates this point:
+
+```python
+# WRONG
+def my_func(pred, tensor):
+  t = tf.matmul(tensor, tensor)
+  with tf.control_dependencies([pred]):
+    # The matmul op is created outside the context, so no control
+    # dependency will be added.
+    return t
+
+# RIGHT
+def my_func(pred, tensor):
+  with tf.control_dependencies([pred]):
+    # The matmul op is created in the context, so a control dependency
+    # will be added.
+    return tf.matmul(tensor, tensor)
+```
+
+##### Args:
+
+
+*  <b>`control_inputs`</b>: A list of `Operation` or `Tensor` objects which
+    must be executed or computed before running the operations
+    defined in the context.  Can also be `None` to clear the control
+    dependencies.
+
+##### Returns:
+
+ A context manager that specifies control dependencies for all
+ operations constructed within the context.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `control_inputs` is not a list of `Operation` or
+    `Tensor` objects.
+
+
+- - -
+
+#### `tf.Graph.device(device_name_or_function)` {#Graph.device}
+
+Returns a context manager that specifies the default device to use.
+
+The `device_name_or_function` argument may either be a device name
+string, a device function, or None:
+
+* If it is a device name string, all operations constructed in
+  this context will be assigned to the device with that name, unless
+  overridden by a nested `device()` context.
+* If it is a function, it will be treated as a function from
+  Operation objects to device name strings, and invoked each time
+  a new Operation is created. The Operation will be assigned to
+  the device with the returned name.
+* If it is None, all `device()` invocations from the enclosing context
+  will be ignored.
+
+For information about the valid syntax of device name strings, see
+the documentation in
+[`DeviceNameUtils`](https://www.tensorflow.org/code/tensorflow/core/util/device_name_utils.h).
+
+For example:
+
+```python
+with g.device('/gpu:0'):
+  # All operations constructed in this context will be placed
+  # on GPU 0.
+  with g.device(None):
+    # All operations constructed in this context will have no
+    # assigned device.
+
+# Defines a function from `Operation` to device string.
+def matmul_on_gpu(n):
+  if n.type == "MatMul":
+    return "/gpu:0"
+  else:
+    return "/cpu:0"
+
+with g.device(matmul_on_gpu):
+  # All operations of type "MatMul" constructed in this context
+  # will be placed on GPU 0; all other operations will be placed
+  # on CPU 0.
+```
+
+**N.B.** The device scope may be overridden by op wrappers or
+other library code. For example, a variable assignment op
+`v.assign()` must be colocated with the `tf.Variable` `v`, and
+incompatible device scopes will be ignored.
+
+##### Args:
+
+
+*  <b>`device_name_or_function`</b>: The device name or function to use in
+    the context.
+
+##### Returns:
+
+  A context manager that specifies the default device to use for newly
+  created ops.
+
+
+- - -
+
+#### `tf.Graph.name_scope(name)` {#Graph.name_scope}
+
+Returns a context manager that creates hierarchical names for operations.
+
+A graph maintains a stack of name scopes. A `with name_scope(...):`
+statement pushes a new name onto the stack for the lifetime of the context.
+
+The `name` argument will be interpreted as follows:
+
+* A string (not ending with '/') will create a new name scope, in which
+  `name` is appended to the prefix of all operations created in the
+  context. If `name` has been used before, it will be made unique by
+  calling `self.unique_name(name)`.
+* A scope previously captured from a `with g.name_scope(...) as
+  scope:` statement will be treated as an "absolute" name scope, which
+  makes it possible to re-enter existing scopes.
+* A value of `None` or the empty string will reset the current name scope
+  to the top-level (empty) name scope.
+
+For example:
+
+```python
+with tf.Graph().as_default() as g:
+  c = tf.constant(5.0, name="c")
+  assert c.op.name == "c"
+  c_1 = tf.constant(6.0, name="c")
+  assert c_1.op.name == "c_1"
+
+  # Creates a scope called "nested"
+  with g.name_scope("nested") as scope:
+    nested_c = tf.constant(10.0, name="c")
+    assert nested_c.op.name == "nested/c"
+
+    # Creates a nested scope called "inner".
+    with g.name_scope("inner"):
+      nested_inner_c = tf.constant(20.0, name="c")
+      assert nested_inner_c.op.name == "nested/inner/c"
+
+    # Create a nested scope called "inner_1".
+    with g.name_scope("inner"):
+      nested_inner_1_c = tf.constant(30.0, name="c")
+      assert nested_inner_1_c.op.name == "nested/inner_1/c"
+
+      # Treats `scope` as an absolute name scope, and
+      # switches to the "nested/" scope.
+      with g.name_scope(scope):
+        nested_d = tf.constant(40.0, name="d")
+        assert nested_d.op.name == "nested/d"
+
+        with g.name_scope(""):
+          e = tf.constant(50.0, name="e")
+          assert e.op.name == "e"
+```
+
+The name of the scope itself can be captured by `with
+g.name_scope(...) as scope:`, which stores the name of the scope
+in the variable `scope`. This value can be used to name an
+operation that represents the overall result of executing the ops
+in a scope. For example:
+
+```python
+inputs = tf.constant(...)
+with g.name_scope('my_layer') as scope:
+  weights = tf.Variable(..., name="weights")
+  biases = tf.Variable(..., name="biases")
+  affine = tf.matmul(inputs, weights) + biases
+  output = tf.nn.relu(affine, name=scope)
+```
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the scope.
+
+##### Returns:
+
+  A context manager that installs `name` as a new name scope.
+
+
+
+A `Graph` instance supports an arbitrary number of "collections"
+that are identified by name. For convenience when building a large
+graph, collections can store groups of related objects: for
+example, the `tf.Variable` uses a collection (named
+[`tf.GraphKeys.VARIABLES`](../../api_docs/python/framework.md#GraphKeys)) for
+all variables that are created during the construction of a graph. The caller
+may define additional collections by specifying a new name.
+
+- - -
+
+#### `tf.Graph.add_to_collection(name, value)` {#Graph.add_to_collection}
+
+Stores `value` in the collection with the given `name`.
+
+Note that collections are not sets, so it is possible to add a value to
+a collection several times.
+
+##### Args:
+
+
+*  <b>`name`</b>: The key for the collection. The `GraphKeys` class
+    contains many standard names for collections.
+*  <b>`value`</b>: The value to add to the collection.
+
+
+- - -
+
+#### `tf.Graph.add_to_collections(names, value)` {#Graph.add_to_collections}
+
+Stores `value` in the collections given by `names`.
+
+Note that collections are not sets, so it is possible to add a value to
+a collection several times. This function makes sure that duplicates in
+`names` are ignored, but it will not check for pre-existing membership of
+`value` in any of the collections in `names`.
+
+`names` can be any iterable, but if `names` is a string, it is treated as a
+single collection name.
+
+##### Args:
+
+
+*  <b>`names`</b>: The keys for the collections to add to. The `GraphKeys` class
+    contains many standard names for collections.
+*  <b>`value`</b>: The value to add to the collections.
+
+
+- - -
+
+#### `tf.Graph.get_collection(name, scope=None)` {#Graph.get_collection}
+
+Returns a list of values in the collection with the given `name`.
+
+This is different from `get_collection_ref()` which always returns the
+actual collection list if it exists in that it returns a new list each time
+it is called.
+
+##### Args:
+
+
+*  <b>`name`</b>: The key for the collection. For example, the `GraphKeys` class
+    contains many standard names for collections.
+*  <b>`scope`</b>: (Optional.) If supplied, the resulting list is filtered to include
+    only items whose `name` attribute matches using `re.match`. Items
+    without a `name` attribute are never returned if a scope is supplied and
+    the choice or `re.match` means that a `scope` without special tokens
+    filters by prefix.
+
+##### Returns:
+
+  The list of values in the collection with the given `name`, or
+  an empty list if no value has been added to that collection. The
+  list contains the values in the order under which they were
+  collected.
+
+
+- - -
+
+#### `tf.Graph.get_collection_ref(name)` {#Graph.get_collection_ref}
+
+Returns a list of values in the collection with the given `name`.
+
+If the collection exists, this returns the list itself, which can
+be modified in place to change the collection.  If the collection does
+not exist, it is created as an empty list and the list is returned.
+
+This is different from `get_collection()` which always returns a copy of
+the collection list if it exists and never creates an empty collection.
+
+##### Args:
+
+
+*  <b>`name`</b>: The key for the collection. For example, the `GraphKeys` class
+    contains many standard names for collections.
+
+##### Returns:
+
+  The list of values in the collection with the given `name`, or an empty
+  list if no value has been added to that collection.
+
+
+
+- - -
+
+#### `tf.Graph.as_graph_element(obj, allow_tensor=True, allow_operation=True)` {#Graph.as_graph_element}
+
+Returns the object referred to by `obj`, as an `Operation` or `Tensor`.
+
+This function validates that `obj` represents an element of this
+graph, and gives an informative error message if it is not.
+
+This function is the canonical way to get/validate an object of
+one of the allowed types from an external argument reference in the
+Session API.
+
+This method may be called concurrently from multiple threads.
+
+##### Args:
+
+
+*  <b>`obj`</b>: A `Tensor`, an `Operation`, or the name of a tensor or operation.
+    Can also be any object with an `_as_graph_element()` method that returns
+    a value of one of these types.
+*  <b>`allow_tensor`</b>: If true, `obj` may refer to a `Tensor`.
+*  <b>`allow_operation`</b>: If true, `obj` may refer to an `Operation`.
+
+##### Returns:
+
+  The `Tensor` or `Operation` in the Graph corresponding to `obj`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `obj` is not a type we support attempting to convert
+    to types.
+*  <b>`ValueError`</b>: If `obj` is of an appropriate type but invalid. For
+    example, an invalid string.
+*  <b>`KeyError`</b>: If `obj` is not an object in the graph.
+
+
+- - -
+
+#### `tf.Graph.get_operation_by_name(name)` {#Graph.get_operation_by_name}
+
+Returns the `Operation` with the given `name`.
+
+This method may be called concurrently from multiple threads.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name of the `Operation` to return.
+
+##### Returns:
+
+  The `Operation` with the given `name`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `name` is not a string.
+*  <b>`KeyError`</b>: If `name` does not correspond to an operation in this graph.
+
+
+- - -
+
+#### `tf.Graph.get_tensor_by_name(name)` {#Graph.get_tensor_by_name}
+
+Returns the `Tensor` with the given `name`.
+
+This method may be called concurrently from multiple threads.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name of the `Tensor` to return.
+
+##### Returns:
+
+  The `Tensor` with the given `name`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `name` is not a string.
+*  <b>`KeyError`</b>: If `name` does not correspond to a tensor in this graph.
+
+
+- - -
+
+#### `tf.Graph.get_operations()` {#Graph.get_operations}
+
+Return the list of operations in the graph.
+
+You can modify the operations in place, but modifications
+to the list such as inserts/delete have no effect on the
+list of operations known to the graph.
+
+This method may be called concurrently from multiple threads.
+
+##### Returns:
+
+  A list of Operations.
+
+
+
+- - -
+
+#### `tf.Graph.seed` {#Graph.seed}
+
+The graph-level random seed of this graph.
+
+
+- - -
+
+#### `tf.Graph.unique_name(name, mark_as_used=True)` {#Graph.unique_name}
+
+Return a unique operation name for `name`.
+
+Note: You rarely need to call `unique_name()` directly.  Most of
+the time you just need to create `with g.name_scope()` blocks to
+generate structured names.
+
+`unique_name` is used to generate structured names, separated by
+`"/"`, to help identify operations when debugging a graph.
+Operation names are displayed in error messages reported by the
+TensorFlow runtime, and in various visualization tools such as
+TensorBoard.
+
+If `mark_as_used` is set to `True`, which is the default, a new
+unique name is created and marked as in use. If it's set to `False`,
+the unique name is returned without actually being marked as used.
+This is useful when the caller simply wants to know what the name
+to be created will be.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name for an operation.
+*  <b>`mark_as_used`</b>: Whether to mark this name as being used.
+
+##### Returns:
+
+  A string to be passed to `create_op()` that will be used
+  to name the operation being created.
+
+
+- - -
+
+#### `tf.Graph.version` {#Graph.version}
+
+Returns a version number that increases as ops are added to the graph.
+
+Note that this is unrelated to the
+[GraphDef version](#Graph.graph_def_version).
+
+
+- - -
+
+#### `tf.Graph.graph_def_versions` {#Graph.graph_def_versions}
+
+The GraphDef version information of this graph.
+
+For details on the meaning of each version, see [`GraphDef`]
+(https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto).
+
+##### Returns:
+
+  A `VersionDef`.
+
+
+
+- - -
+
+#### `tf.Graph.create_op(op_type, inputs, dtypes, input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True)` {#Graph.create_op}
+
+Creates an `Operation` in this graph.
+
+This is a low-level interface for creating an `Operation`. Most
+programs will not call this method directly, and instead use the
+Python op constructors, such as `tf.constant()`, which add ops to
+the default graph.
+
+##### Args:
+
+
+*  <b>`op_type`</b>: The `Operation` type to create. This corresponds to the
+    `OpDef.name` field for the proto that defines the operation.
+*  <b>`inputs`</b>: A list of `Tensor` objects that will be inputs to the `Operation`.
+*  <b>`dtypes`</b>: A list of `DType` objects that will be the types of the tensors
+    that the operation produces.
+*  <b>`input_types`</b>: (Optional.) A list of `DType`s that will be the types of
+    the tensors that the operation consumes. By default, uses the base
+    `DType` of each input in `inputs`. Operations that expect
+    reference-typed inputs must specify `input_types` explicitly.
+*  <b>`name`</b>: (Optional.) A string name for the operation. If not specified, a
+    name is generated based on `op_type`.
+*  <b>`attrs`</b>: (Optional.) A dictionary where the key is the attribute name (a
+    string) and the value is the respective `attr` attribute of the
+    `NodeDef` proto that will represent the operation (an `AttrValue`
+    proto).
+*  <b>`op_def`</b>: (Optional.) The `OpDef` proto that describes the `op_type` that
+    the operation will have.
+*  <b>`compute_shapes`</b>: (Optional.) If True, shape inference will be performed
+    to compute the shapes of the outputs.
+*  <b>`compute_device`</b>: (Optional.) If True, device functions will be executed
+    to compute the device property of the Operation.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if any of the inputs is not a `Tensor`.
+*  <b>`ValueError`</b>: if colocation conflicts with existing device assignment.
+
+##### Returns:
+
+  An `Operation` object.
+
+
+- - -
+
+#### `tf.Graph.gradient_override_map(op_type_map)` {#Graph.gradient_override_map}
+
+EXPERIMENTAL: A context manager for overriding gradient functions.
+
+This context manager can be used to override the gradient function
+that will be used for ops within the scope of the context.
+
+For example:
+
+```python
+@tf.RegisterGradient("CustomSquare")
+def _custom_square_grad(op, grad):
+  # ...
+
+with tf.Graph().as_default() as g:
+  c = tf.constant(5.0)
+  s_1 = tf.square(c)  # Uses the default gradient for tf.square.
+  with g.gradient_override_map({"Square": "CustomSquare"}):
+    s_2 = tf.square(s_2)  # Uses _custom_square_grad to compute the
+                          # gradient of s_2.
+```
+
+##### Args:
+
+
+*  <b>`op_type_map`</b>: A dictionary mapping op type strings to alternative op
+    type strings.
+
+##### Returns:
+
+  A context manager that sets the alternative op type to be used for one
+  or more ops created in that context.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `op_type_map` is not a dictionary mapping strings to
+    strings.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.Graph.colocate_with(op, ignore_existing=False)` {#Graph.colocate_with}
+
+Returns a context manager that specifies an op to colocate with.
+
+Note: this function is not for public use, only for internal libraries.
+
+For example:
+
+```python
+a = tf.Variable([1.0])
+with g.colocate_with(a):
+  b = tf.constant(1.0)
+  c = tf.add(a, b)
+```
+
+`b` and `c` will always be colocated with `a`, no matter where `a`
+is eventually placed.
+
+##### Args:
+
+
+*  <b>`op`</b>: The op to colocate all created ops with.
+*  <b>`ignore_existing`</b>: If true, only applies colocation of this op within
+    the context, rather than applying all colocation properties
+    on the stack.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if op is None.
+
+##### Yields:
+
+  A context manager that specifies the op with which to colocate
+  newly created ops.
+
+
+- - -
+
+#### `tf.Graph.get_all_collection_keys()` {#Graph.get_all_collection_keys}
+
+Returns a list of collections used in this graph.
+
+
+- - -
+
+#### `tf.Graph.is_feedable(tensor)` {#Graph.is_feedable}
+
+Returns `True` if and only if `tensor` is feedable.
+
+
+- - -
+
+#### `tf.Graph.prevent_feeding(tensor)` {#Graph.prevent_feeding}
+
+Marks the given `tensor` as unfeedable in this graph.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.IndexedSlices.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.IndexedSlices.md
new file mode 100644
index 0000000000..435a178205
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.IndexedSlices.md
@@ -0,0 +1,93 @@
+A sparse representation of a set of tensor slices at given indices.
+
+This class is a simple wrapper for a pair of `Tensor` objects:
+
+* `values`: A `Tensor` of any dtype with shape `[D0, D1, ..., Dn]`.
+* `indices`: A 1-D integer `Tensor` with shape `[D0]`.
+
+An `IndexedSlices` is typically used to represent a subset of a larger
+tensor `dense` of shape `[LARGE0, D1, .. , DN]` where `LARGE0 >> D0`.
+The values in `indices` are the indices in the first dimension of
+the slices that have been extracted from the larger tensor.
+
+The dense tensor `dense` represented by an `IndexedSlices` `slices` has
+
+```python
+dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]
+```
+
+The `IndexedSlices` class is used principally in the definition of
+gradients for operations that have sparse gradients
+(e.g. [`tf.gather`](../../api_docs/python/array_ops.md#gather)).
+
+Contrast this representation with
+[`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
+which uses multi-dimensional indices and scalar values.
+
+- - -
+
+#### `tf.IndexedSlices.__init__(values, indices, dense_shape=None)` {#IndexedSlices.__init__}
+
+Creates an `IndexedSlices`.
+
+
+
+- - -
+
+#### `tf.IndexedSlices.values` {#IndexedSlices.values}
+
+A `Tensor` containing the values of the slices.
+
+
+- - -
+
+#### `tf.IndexedSlices.indices` {#IndexedSlices.indices}
+
+A 1-D `Tensor` containing the indices of the slices.
+
+
+- - -
+
+#### `tf.IndexedSlices.dense_shape` {#IndexedSlices.dense_shape}
+
+A 1-D `Tensor` containing the shape of the corresponding dense tensor.
+
+
+
+- - -
+
+#### `tf.IndexedSlices.name` {#IndexedSlices.name}
+
+The name of this `IndexedSlices`.
+
+
+- - -
+
+#### `tf.IndexedSlices.dtype` {#IndexedSlices.dtype}
+
+The `DType` of elements in this tensor.
+
+
+- - -
+
+#### `tf.IndexedSlices.device` {#IndexedSlices.device}
+
+The name of the device on which `values` will be produced, or `None`.
+
+
+- - -
+
+#### `tf.IndexedSlices.op` {#IndexedSlices.op}
+
+The `Operation` that produces `values` as an output.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.IndexedSlices.graph` {#IndexedSlices.graph}
+
+The `Graph` that contains the values, indices, and shape tensors.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.SparseTensorValue.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.SparseTensorValue.md
new file mode 100644
index 0000000000..efa3314f23
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.SparseTensorValue.md
@@ -0,0 +1,22 @@
+SparseTensorValue(indices, values, shape)
+- - -
+
+#### `tf.SparseTensorValue.indices` {#SparseTensorValue.indices}
+
+Alias for field number 0
+
+
+- - -
+
+#### `tf.SparseTensorValue.shape` {#SparseTensorValue.shape}
+
+Alias for field number 2
+
+
+- - -
+
+#### `tf.SparseTensorValue.values` {#SparseTensorValue.values}
+
+Alias for field number 1
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.WholeFileReader.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.WholeFileReader.md
new file mode 100644
index 0000000000..e168cabc9e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.WholeFileReader.md
@@ -0,0 +1,148 @@
+A Reader that outputs the entire contents of a file as a value.
+
+To use, enqueue filenames in a Queue.  The output of Read will
+be a filename (key) and the contents of that file (value).
+
+See ReaderBase for supported methods.
+- - -
+
+#### `tf.WholeFileReader.__init__(name=None)` {#WholeFileReader.__init__}
+
+Create a WholeFileReader.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+
+- - -
+
+#### `tf.WholeFileReader.num_records_produced(name=None)` {#WholeFileReader.num_records_produced}
+
+Returns the number of records this reader has produced.
+
+This is the same as the number of Read executions that have
+succeeded.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.WholeFileReader.num_work_units_completed(name=None)` {#WholeFileReader.num_work_units_completed}
+
+Returns the number of work units this reader has finished processing.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.WholeFileReader.read(queue, name=None)` {#WholeFileReader.read}
+
+Returns the next record (key, value pair) produced by a reader.
+
+Will dequeue a work unit from queue if necessary (e.g. when the
+Reader needs to start reading from a new file since it has
+finished with the previous file).
+
+##### Args:
+
+
+*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
+    to a Queue, with string work items.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of Tensors (key, value).
+
+*  <b>`key`</b>: A string scalar Tensor.
+*  <b>`value`</b>: A string scalar Tensor.
+
+
+- - -
+
+#### `tf.WholeFileReader.reader_ref` {#WholeFileReader.reader_ref}
+
+Op that implements the reader.
+
+
+- - -
+
+#### `tf.WholeFileReader.reset(name=None)` {#WholeFileReader.reset}
+
+Restore a reader to its initial clean state.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.WholeFileReader.restore_state(state, name=None)` {#WholeFileReader.restore_state}
+
+Restore a reader to a previously saved state.
+
+Not all Readers support being restored, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`state`</b>: A string Tensor.
+    Result of a SerializeState of a Reader with matching type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.WholeFileReader.serialize_state(name=None)` {#WholeFileReader.serialize_state}
+
+Produce a string tensor that encodes the state of a reader.
+
+Not all Readers support being serialized, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A string Tensor.
+
+
+- - -
+
+#### `tf.WholeFileReader.supports_serialize` {#WholeFileReader.supports_serialize}
+
+Whether the Reader implementation can serialize its state.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.add_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.add_n.md
new file mode 100644
index 0000000000..c214a46057
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.add_n.md
@@ -0,0 +1,15 @@
+### `tf.add_n(inputs, name=None)` {#add_n}
+
+Add all input tensors element wise.
+
+##### Args:
+
+
+*  <b>`inputs`</b>: A list of at least 1 `Tensor` objects of the same type in: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+    Must all be the same size and shape.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `inputs`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.assert_rank_at_least.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.assert_rank_at_least.md
new file mode 100644
index 0000000000..1b33f3401b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.assert_rank_at_least.md
@@ -0,0 +1,37 @@
+### `tf.assert_rank_at_least(x, rank, data=None, summarize=None, name=None)` {#assert_rank_at_least}
+
+Assert `x` has rank equal to `rank` or higher.
+
+Example of adding a dependency to an operation:
+
+```python
+with tf.control_dependencies([tf.assert_rank_at_least(x, 2)]):
+  output = tf.reduce_sum(x)
+```
+
+Example of adding dependency to the tensor being checked:
+
+```python
+x = tf.with_dependencies([tf.assert_rank_at_least(x, 2)], x)
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`rank`</b>: Scalar `Tensor`.
+*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
+    error message and first few entries of `x`.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).
+    Defaults to "assert_rank_at_least".
+
+##### Returns:
+
+  Op raising `InvalidArgumentError` unless `x` has specified rank or higher.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If static checks determine `x` has wrong rank.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.assert_variables_initialized.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.assert_variables_initialized.md
new file mode 100644
index 0000000000..ef61848aa8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.assert_variables_initialized.md
@@ -0,0 +1,24 @@
+### `tf.assert_variables_initialized(var_list=None)` {#assert_variables_initialized}
+
+Returns an Op to check if variables are initialized.
+
+NOTE: This function is obsolete and will be removed in 6 months.  Please
+change your implementation to use `report_uninitialized_variables()`.
+
+When run, the returned Op will raise the exception `FailedPreconditionError`
+if any of the variables has not yet been initialized.
+
+Note: This function is implemented by trying to fetch the values of the
+variables. If one of the variables is not initialized a message may be
+logged by the C++ runtime. This is expected.
+
+##### Args:
+
+
+*  <b>`var_list`</b>: List of `Variable` objects to check. Defaults to the
+    value of `all_variables().`
+
+##### Returns:
+
+  An Op, or None if there are no variables.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_cholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_cholesky.md
new file mode 100644
index 0000000000..487680f50b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_cholesky.md
@@ -0,0 +1,20 @@
+### `tf.batch_cholesky(input, name=None)` {#batch_cholesky}
+
+Calculates the Cholesky decomposition of a batch of square matrices.
+
+The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+form square matrices, with the same constraints as the single matrix Cholesky
+decomposition above. The output is a tensor of the same shape as the input
+containing the Cholesky decompositions for all input submatrices `[..., :, :]`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
+    Shape is `[..., M, M]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`. Shape is `[..., M, M]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_fft2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_fft2d.md
deleted file mode 100644
index e7a2c7b943..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_fft2d.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.batch_fft2d(input, name=None)` {#batch_fft2d}
-
-Compute the 2-dimensional discrete Fourier Transform over the inner-most
-
-2 dimensions of `input`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`.
-  A complex64 tensor of the same shape as `input`. The inner-most 2
-  dimensions of `input` are replaced with their 2D Fourier Transform.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_fft3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_fft3d.md
new file mode 100644
index 0000000000..10c2ea3bf6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_fft3d.md
@@ -0,0 +1,18 @@
+### `tf.batch_fft3d(input, name=None)` {#batch_fft3d}
+
+Compute the 3-dimensional discrete Fourier Transform over the inner-most 3
+
+dimensions of `input`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`.
+  A complex64 tensor of the same shape as `input`. The inner-most 3
+  dimensions of `input` are replaced with their 3D Fourier Transform.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_ifft.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_ifft.md
deleted file mode 100644
index c4b865425b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_ifft.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.batch_ifft(input, name=None)` {#batch_ifft}
-
-Compute the inverse 1-dimensional discrete Fourier Transform over the inner-most
-
-dimension of `input`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`.
-  A complex64 tensor of the same shape as `input`. The inner-most
-  dimension of `input` is replaced with its inverse 1D Fourier Transform.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_matrix_band_part.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_matrix_band_part.md
deleted file mode 100644
index d9c208a460..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_matrix_band_part.md
+++ /dev/null
@@ -1,60 +0,0 @@
-### `tf.batch_matrix_band_part(input, num_lower, num_upper, name=None)` {#batch_matrix_band_part}
-
-Copy a tensor setting everything outside a central band in each innermost matrix
-
-to zero.
-
-The `band` part is computed as follows:
-Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a
-tensor with the same shape where
-
-`band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
-
-The indicator function 'in_band(m, n)` is one if
-`(num_lower < 0 || (m-n) <= num_lower)) &&
-(num_upper < 0 || (n-m) <= num_upper)`, and zero otherwise.
-
-For example:
-
-```prettyprint
-# if 'input' is [[ 0,  1,  2, 3]
-                 [-1,  0,  1, 2]
-                 [-2, -1,  0, 1]
-                 [-3, -2, -1, 0]],
-
-tf.batch_matrix_band_part(input, 1, -1) ==> [[ 0,  1,  2, 3]
-                                             [-1,  0,  1, 2]
-                                             [ 0, -1,  0, 1]
-                                             [ 0,  0, -1, 0]],
-
-tf.batch_matrix_band_part(input, 2, 1) ==> [[ 0,  1,  0, 0]
-                                            [-1,  0,  1, 0]
-                                            [-2, -1,  0, 1]
-                                            [ 0, -2, -1, 0]]
-```
-
-Useful special cases:
-
-```prettyprint
- tf.batch_matrix_band_part(input, 0, -1) ==> Upper triangular part.
- tf.batch_matrix_band_part(input, -1, 0) ==> Lower triangular part.
- tf.batch_matrix_band_part(input, 0, 0) ==> Diagonal.
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Rank `k` tensor.
-*  <b>`num_lower`</b>: A `Tensor` of type `int64`.
-    0-D tensor. Number of subdiagonals to keep. If negative, keep entire
-    lower triangle.
-*  <b>`num_upper`</b>: A `Tensor` of type `int64`.
-    0-D tensor. Number of superdiagonals to keep. If negative, keep
-    entire upper triangle.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-  Rank `k` tensor of the same shape as input. The extracted banded tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_matrix_triangular_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_matrix_triangular_solve.md
new file mode 100644
index 0000000000..297e19088d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.batch_matrix_triangular_solve.md
@@ -0,0 +1,39 @@
+### `tf.batch_matrix_triangular_solve(matrix, rhs, lower=None, adjoint=None, name=None)` {#batch_matrix_triangular_solve}
+
+Solves systems of linear equations with upper or lower triangular matrices by
+
+backsubstitution.
+
+`matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form
+square matrices. If `lower` is `True` then the strictly upper triangular part
+of each inner-most matrix is assumed to be zero and not accessed.
+If `lower` is False then the strictly lower triangular part of each inner-most
+matrix is assumed to be zero and not accessed.
+`rhs` is a tensor of shape [..., M, K]`.
+
+The output is a tensor of shape `[..., M, K]`. If `adjoint` is `True` then the
+innermost matrices in output` satisfy matrix equations
+`matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
+If `adjoint` is `False` then the strictly then the  innermost matrices in
+`output` satisfy matrix equations
+`adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`.
+
+##### Args:
+
+
+*  <b>`matrix`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
+    Shape is `[..., M, M]`.
+*  <b>`rhs`</b>: A `Tensor`. Must have the same type as `matrix`.
+    Shape is `[..., M, K]`.
+*  <b>`lower`</b>: An optional `bool`. Defaults to `True`.
+    Boolean indicating whether the innermost matrices in `matrix` are
+    lower or upper triangular.
+*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
+    Boolean indicating whether to solve with `matrix` or its (block-wise)
+    adjoint.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `matrix`. Shape is `[..., M, K]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.bytes.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.bytes.md
deleted file mode 100644
index 5353507e39..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.bytes.md
+++ /dev/null
@@ -1,4 +0,0 @@
-str(object='') -> string
-
-Return a nice string representation of the object.
-If the argument is a string, the return value is the same object.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.cast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.cast.md
new file mode 100644
index 0000000000..9571f87afe
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.cast.md
@@ -0,0 +1,30 @@
+### `tf.cast(x, dtype, name=None)` {#cast}
+
+Casts a tensor to a new type.
+
+The operation casts `x` (in case of `Tensor`) or `x.values`
+(in case of `SparseTensor`) to `dtype`.
+
+For example:
+
+```python
+# tensor `a` is [1.8, 2.2], dtype=tf.float
+tf.cast(a, tf.int32) ==> [1, 2]  # dtype=tf.int32
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
+*  <b>`dtype`</b>: The destination type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` or `SparseTensor` with same shape as `x`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `x` cannot be cast to the `dtype`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.complex.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.complex.md
new file mode 100644
index 0000000000..55487ea170
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.complex.md
@@ -0,0 +1,30 @@
+### `tf.complex(real, imag, name=None)` {#complex}
+
+Converts two real numbers to a complex number.
+
+Given a tensor `real` representing the real part of a complex number, and a
+tensor `imag` representing the imaginary part of a complex number, this
+operation returns complex numbers elementwise of the form \(a + bj\), where
+*a* represents the `real` part and *b* represents the `imag` part.
+
+The input tensors `real` and `imag` must have the same shape.
+
+For example:
+
+```
+# tensor 'real' is [2.25, 3.25]
+# tensor `imag` is [4.75, 5.75]
+tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]]
+```
+
+##### Args:
+
+
+*  <b>`real`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`imag`</b>: A `Tensor`. Must have the same type as `real`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64` or `complex128`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.cond.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.cond.md
deleted file mode 100644
index 6e6a9a69bf..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.cond.md
+++ /dev/null
@@ -1,54 +0,0 @@
-### `tf.cond(pred, fn1, fn2, name=None)` {#cond}
-
-Return either fn1() or fn2() based on the boolean predicate `pred`.
-
-`fn1` and `fn2` both return lists of output tensors. `fn1` and `fn2` must have
-the same non-zero number and type of outputs.
-
-Note that the conditional execution applies only to the operations defined in
-fn1 and fn2. Consider the following simple program:
-
-```python
-z = tf.mul(a, b)
-result = tf.cond(x < y, lambda: tf.add(x, z), lambda: tf.square(y))
-```
-
-If x < y, the tf.add operation will be executed and tf.square
-operation will not be executed. Since z is needed for at least one
-branch of the cond, the tf.mul operation is always executed, unconditionally.
-Although this behavior is consistent with the dataflow model of TensorFlow,
-it has occasionally surprised some users who expected a lazier semantics.
-
-##### Args:
-
-
-*  <b>`pred`</b>: A scalar determining whether to return the result of `fn1` or `fn2`.
-*  <b>`fn1`</b>: The callable to be performed if pred is true.
-*  <b>`fn2`</b>: The callable to be performed if pref is false.
-*  <b>`name`</b>: Optional name prefix for the returned tensors.
-
-##### Returns:
-
-  Tensors returned by the call to either `fn1` or `fn2`. If the callables
-  return a singleton list, the element is extracted from the list.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `fn1` or `fn2` is not callable.
-*  <b>`ValueError`</b>: if `fn1` and `fn2` do not return the same number of tensors, or
-              return tensors of different types.
-
-
-*  <b>`Example`</b>: 
-
-```python
-  x = tf.constant(2)
-  y = tf.constant(5)
-  def f1(): return tf.mul(x, 17)
-  def f2(): return tf.add(y, 23)
-  r = cond(tf.less(x, y), f1, f2)
-  # r is set to f1().
-  # Operations in f2 (e.g., tf.add) are not executed.
-```
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DiscreteDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DiscreteDistribution.md
new file mode 100644
index 0000000000..6e78e38ebe
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DiscreteDistribution.md
@@ -0,0 +1,139 @@
+Base class for discrete probability distributions.
+
+`DiscreteDistribution` defines the API for the likelihood functions `pmf` and
+`log_pmf` of discrete probability distributions.
+
+Subclasses must override both `pmf` and `log_pmf` but one can call this base
+class's implementation.
+
+See `BaseDistribution` for more information on the API for probability
+distributions.
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.batch_shape(name=None)` {#DiscreteDistribution.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.cdf(value, name='cdf')` {#DiscreteDistribution.cdf}
+
+Cumulative distribution function.
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.dtype` {#DiscreteDistribution.dtype}
+
+dtype of samples from this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.entropy(name=None)` {#DiscreteDistribution.entropy}
+
+Entropy of the distribution in nats.
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.event_shape(name=None)` {#DiscreteDistribution.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.get_batch_shape()` {#DiscreteDistribution.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.get_event_shape()` {#DiscreteDistribution.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.log_cdf(value, name='log_cdf')` {#DiscreteDistribution.log_cdf}
+
+Log CDF.
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.log_pmf(value, name='log_pmf')` {#DiscreteDistribution.log_pmf}
+
+Log of the probability mass function.
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.mean` {#DiscreteDistribution.mean}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.name` {#DiscreteDistribution.name}
+
+Name to prepend to all ops.
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.pmf(value, name='pmf')` {#DiscreteDistribution.pmf}
+
+Probability mass function.
+
+
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.sample(n, seed=None, name=None)` {#DiscreteDistribution.sample}
+
+Generate `n` samples.
+
+##### Args:
+
+
+*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
+      with values of type `self.dtype`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Uniform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Uniform.md
new file mode 100644
index 0000000000..ad6008c9f6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Uniform.md
@@ -0,0 +1,216 @@
+Uniform distribution with `a` and `b` parameters.
+
+The PDF of this distribution is constant between [`a`, `b`], and 0 elsewhere.
+- - -
+
+#### `tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, name='Uniform')` {#Uniform.__init__}
+
+Construct Uniform distributions with `a` and `b`.
+
+The parameters `a` and `b` must be shaped in a way that supports
+broadcasting (e.g. `b - a` is a valid operation).
+
+Here are examples without broadcasting:
+
+```python
+# Without broadcasting
+u1 = Uniform(3.0, 4.0)  # a single uniform distribution [3, 4]
+u2 = Uniform([1.0, 2.0], [3.0, 4.0])  # 2 distributions [1, 3], [2, 4]
+u3 = Uniform([[1.0, 2.0],
+              [3.0, 4.0]],
+             [[1.5, 2.5],
+              [3.5, 4.5]])  # 4 distributions
+```
+
+And with broadcasting:
+
+```python
+u1 = Uniform(3.0, [5.0, 6.0, 7.0])  # 3 distributions
+```
+
+##### Args:
+
+
+*  <b>`a`</b>: `float` or `double` tensor, the minimum endpoint.
+*  <b>`b`</b>: `float` or `double` tensor, the maximum endpoint. Must be > `a`.
+*  <b>`name`</b>: The name to prefix Ops created by this distribution class.
+
+##### Raises:
+
+
+*  <b>`InvalidArgumentError`</b>: if `a >= b`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.a` {#Uniform.a}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.b` {#Uniform.b}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.batch_shape(name='batch_shape')` {#Uniform.batch_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.cdf(x, name='cdf')` {#Uniform.cdf}
+
+CDF of observations in `x` under these Uniform distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`. If `x` is `nan`, will
+      return `nan`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.dtype` {#Uniform.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.entropy(name='entropy')` {#Uniform.entropy}
+
+The entropy of Uniform distribution(s).
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.event_shape(name='event_shape')` {#Uniform.event_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.get_batch_shape()` {#Uniform.get_batch_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.get_event_shape()` {#Uniform.get_event_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.is_reparameterized` {#Uniform.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.log_cdf(x, name='log_cdf')` {#Uniform.log_cdf}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.log_pdf(x, name='log_pdf')` {#Uniform.log_pdf}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.mean` {#Uniform.mean}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.name` {#Uniform.name}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.pdf(x, name='pdf')` {#Uniform.pdf}
+
+The PDF of observations in `x` under these Uniform distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`. If `x` is `nan`, will
+      return `nan`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.range` {#Uniform.range}
+
+`b - a`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.sample(n, seed=None, name='sample')` {#Uniform.sample}
+
+Sample `n` observations from the Uniform Distributions.
+
+##### Args:
+
+
+*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`seed`</b>: Python integer, the random seed.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
+      with values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.variance` {#Uniform.variance}
+
+
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.normal_conjugates_known_sigma_posterior.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.normal_conjugates_known_sigma_posterior.md
deleted file mode 100644
index ae8eb00890..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.normal_conjugates_known_sigma_posterior.md
+++ /dev/null
@@ -1,48 +0,0 @@
-### `tf.contrib.distributions.normal_conjugates_known_sigma_posterior(prior, sigma, s, n)` {#normal_conjugates_known_sigma_posterior}
-
-Posterior Normal distribution with conjugate prior on the mean.
-
-This model assumes that `n` observations (with sum `s`) come from a
-Normal with unknown mean `mu` (described by the Normal `prior`)
-and known variance `sigma^2`.  The "known sigma posterior" is
-the distribution of the unknown `mu`.
-
-Accepts a prior Normal distribution object, having parameters
-`mu0` and `sigma0`, as well as known `sigma` values of the predictive
-distribution(s) (also assumed Normal),
-and statistical estimates `s` (the sum(s) of the observations) and
-`n` (the number(s) of observations).
-
-Returns a posterior (also Normal) distribution object, with parameters
-`(mu', sigma'^2)`, where:
-
-```
-mu ~ N(mu', sigma'^2)
-sigma'^2 = 1/(1/sigma0^2 + n/sigma^2),
-mu' = (mu0/sigma0^2 + s/sigma^2) * sigma'^2.
-```
-
-Distribution parameters from `prior`, as well as `sigma`, `s`, and `n`.
-will broadcast in the case of multidimensional sets of parameters.
-
-##### Args:
-
-
-*  <b>`prior`</b>: `Normal` object of type `dtype`:
-    the prior distribution having parameters `(mu0, sigma0)`.
-*  <b>`sigma`</b>: tensor of type `dtype`, taking values `sigma > 0`.
-    The known stddev parameter(s).
-*  <b>`s`</b>: Tensor of type `dtype`.  The sum(s) of observations.
-*  <b>`n`</b>: Tensor of type `int`.  The number(s) of observations.
-
-##### Returns:
-
-  A new Normal posterior distribution object for the unknown observation
-  mean `mu`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if dtype of `s` does not match `dtype`, or `prior` is not a
-    Normal object.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.ffmpeg.decode_audio.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.ffmpeg.decode_audio.md
deleted file mode 100644
index 31b9cba01f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.ffmpeg.decode_audio.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.contrib.ffmpeg.decode_audio(contents, file_format=None, samples_per_second=None, channel_count=None)` {#decode_audio}
-
-Create an op that decodes the contents of an audio file.
-
-##### Args:
-
-
-*  <b>`contents`</b>: The binary contents of the audio file to decode. This is a
-      scalar.
-*  <b>`file_format`</b>: A string specifying which format the contents will conform
-      to. This can be mp3, ogg, or wav.
-*  <b>`samples_per_second`</b>: The number of samples per second that is assumed.
-      In some cases, resampling will occur to generate the correct sample
-      rate.
-*  <b>`channel_count`</b>: The number of channels that should be created from the
-      audio contents. If the contents have more than this number, then
-      some channels will be merged or dropped. If contents has fewer than
-      this, then additional channels will be created from the existing ones.
-
-##### Returns:
-
-  A rank 2 tensor that has time along dimension 0 and channels along
-  dimension 1. Dimension 0 will be `samples_per_second * length` wide, and
-  dimension 1 will be `channel_count` wide.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.apply_regularization.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.apply_regularization.md
new file mode 100644
index 0000000000..8216a4fa25
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.apply_regularization.md
@@ -0,0 +1,27 @@
+### `tf.contrib.layers.apply_regularization(regularizer, weights_list=None)` {#apply_regularization}
+
+Returns the summed penalty by applying `regularizer` to the `weights_list`.
+
+Adding a regularization penalty over the layer weights and embedding weights
+can help prevent overfitting the training data. Regularization over layer
+biases is less common/useful, but assuming proper data preprocessing/mean
+subtraction, it usually shouldn't hurt much either.
+
+##### Args:
+
+
+*  <b>`regularizer`</b>: A function that takes a single `Tensor` argument and returns
+    a scalar `Tensor` output.
+*  <b>`weights_list`</b>: List of weights `Tensors` or `Variables` to apply
+    `regularizer` over. Defaults to the `GraphKeys.WEIGHTS` collection if
+    `None`.
+
+##### Returns:
+
+  A scalar representing the overall regularization penalty.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `regularizer` does not return a scalar output.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.sum_regularizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.sum_regularizer.md
new file mode 100644
index 0000000000..ee05583b04
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.sum_regularizer.md
@@ -0,0 +1,14 @@
+### `tf.contrib.layers.sum_regularizer(regularizer_list)` {#sum_regularizer}
+
+Returns a function that applies the sum of multiple regularizers.
+
+##### Args:
+
+
+*  <b>`regularizer_list`</b>: A list of regularizers to apply.
+
+##### Returns:
+
+  A function with signature `sum_reg(weights, name=None)` that applies the
+  sum of all the input regularizers.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.summarize_tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.summarize_tensor.md
new file mode 100644
index 0000000000..872ba5c9d4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.summarize_tensor.md
@@ -0,0 +1,18 @@
+### `tf.contrib.layers.summarize_tensor(tensor, tag=None)` {#summarize_tensor}
+
+Summarize a tensor using a suitable summary type.
+
+This function adds a summary op for `tensor`. The type of summary depends on
+the shape of `tensor`. For scalars, a `scalar_summary` is created, for all
+other tensors, `histogram_summary` is used.
+
+##### Args:
+
+
+*  <b>`tensor`</b>: The tensor to summarize
+*  <b>`tag`</b>: The tag to use, if None then use tensor's op's name.
+
+##### Returns:
+
+  The summary op created or None for string tensors.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.xavier_initializer_conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.xavier_initializer_conv2d.md
deleted file mode 100644
index 9deeb48b5b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.layers.xavier_initializer_conv2d.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.contrib.layers.xavier_initializer_conv2d(uniform=True, seed=None, dtype=tf.float32)` {#xavier_initializer_conv2d}
-
-Returns an initializer performing "Xavier" initialization for weights.
-
-This function implements the weight initialization from:
-
-Xavier Glorot and Yoshua Bengio (2010):
-         Understanding the difficulty of training deep feedforward neural
-         networks. International conference on artificial intelligence and
-         statistics.
-
-This initializer is designed to keep the scale of the gradients roughly the
-same in all layers. In uniform distribution this ends up being the range:
-`x = sqrt(6. / (in + out)); [-x, x]` and for normal distribution a standard
-deviation of `sqrt(3. / (in + out))` is used.
-
-##### Args:
-
-
-*  <b>`uniform`</b>: Whether to use uniform or normal distributed random initialization.
-*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`dtype`</b>: The data type. Only floating point types are supported.
-
-##### Returns:
-
-  An initializer for a weight matrix.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.ModeKeys.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.ModeKeys.md
deleted file mode 100644
index 83e0bd4119..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.ModeKeys.md
+++ /dev/null
@@ -1,7 +0,0 @@
-Standard names for model modes.
-
-The following standard keys are defined:
-
-* `TRAIN`: training mode.
-* `EVAL`: evaluation mode.
-* `INFER`: inference mode.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.TensorFlowDNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.TensorFlowDNNRegressor.md
deleted file mode 100644
index 182b81de75..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.TensorFlowDNNRegressor.md
+++ /dev/null
@@ -1,302 +0,0 @@
-TensorFlow DNN Regressor model.
-
-Parameters:
-  hidden_units: List of hidden units per layer.
-  batch_size: Mini batch size.
-  steps: Number of steps to run over data.
-  optimizer: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
-  learning_rate: If this is constant float value, no decay function is
-    used. Instead, a customized decay function can be passed that accepts
-    global_step as parameter and returns a Tensor.
-    e.g. exponential decay function:
-    def exp_decay(global_step):
-        return tf.train.exponential_decay(
-            learning_rate=0.1, global_step,
-            decay_steps=2, decay_rate=0.001)
-  continue_training: when continue_training is True, once initialized
-    model will be continuely trained on every call of fit.
-  config: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc.
-  verbose: Controls the verbosity, possible values:
-    0: the algorithm and debug information is muted.
-    1: trainer prints the progress.
-    2: log device placement is printed.
-  dropout: When not None, the probability we will drop out a given coordinate.
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.__init__(hidden_units, n_classes=0, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, continue_training=False, config=None, verbose=1, dropout=None)` {#TensorFlowDNNRegressor.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.bias_` {#TensorFlowDNNRegressor.bias_}
-
-Returns bias of the DNN's bias layers.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowDNNRegressor.evaluate}
-
-See base class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowDNNRegressor.fit}
-
-Builds a neural network model given provided `model_fn` and training
-data X and y.
-
-Note: called first time constructs the graph and initializers
-variables. Consecutives times it will continue training the same model.
-This logic follows partial_fit() interface in scikit-learn.
-
-To restart learning, create new estimator.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class labels in classification, real numbers in regression).
-
-*  <b>`steps`</b>: int, number of steps to train.
-         If None or 0, train for `self.steps`.
-*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
-    invoke early stopping.
-*  <b>`logdir`</b>: the directory to save the log file that can be used for
-  optional visualization.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.get_params(deep=True)` {#TensorFlowDNNRegressor.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.get_tensor(name)` {#TensorFlowDNNRegressor.get_tensor}
-
-Returns tensor by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.get_tensor_value(name)` {#TensorFlowDNNRegressor.get_tensor_value}
-
-Returns value of the tensor give by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.get_variable_names()` {#TensorFlowDNNRegressor.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.model_dir` {#TensorFlowDNNRegressor.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.partial_fit(x, y)` {#TensorFlowDNNRegressor.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class label in classification, real numbers in regression).
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.predict(x, axis=1, batch_size=None)` {#TensorFlowDNNRegressor.predict}
-
-Predict class or regression for X.
-
-For a classification model, the predicted class for each sample in X is
-returned. For a regression model, the predicted value based on X is
-returned.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`axis`</b>: Which axis to argmax for classification.
-    By default axis 1 (next after batch) is used.
-    Use 2 for sequence predictions.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member
-    variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
-  value.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.predict_proba(x, batch_size=None)` {#TensorFlowDNNRegressor.predict_proba}
-
-Predict class probability of the input samples X.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
-  probabilities for each class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.restore(cls, path, config=None)` {#TensorFlowDNNRegressor.restore}
-
-Restores model from give path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Path to the checkpoints and other model information.
-*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
-      reconfigured.
-
-##### Returns:
-
-  Estimator, object of the subclass of TensorFlowEstimator.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.save(path)` {#TensorFlowDNNRegressor.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.set_params(**params)` {#TensorFlowDNNRegressor.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.train(input_fn, steps, monitors=None)` {#TensorFlowDNNRegressor.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNRegressor.weights_` {#TensorFlowDNNRegressor.weights_}
-
-Returns weights of the DNN weight layers.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.TensorFlowRNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.TensorFlowRNNRegressor.md
deleted file mode 100644
index d23cd65402..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.TensorFlowRNNRegressor.md
+++ /dev/null
@@ -1,312 +0,0 @@
-TensorFlow RNN Regressor model.
-
-Parameters:
-  rnn_size: The size for rnn cell, e.g. size of your word embeddings.
-  cell_type: The type of rnn cell, including rnn, gru, and lstm.
-  num_layers: The number of layers of the rnn model.
-  input_op_fn: Function that will transform the input tensor, such as
-    creating word embeddings, byte list, etc. This takes
-    an argument X for input and returns transformed X.
-  bidirectional: boolean, Whether this is a bidirectional rnn.
-  sequence_length: If sequence_length is provided, dynamic calculation is
-    performed. This saves computational time when unrolling past max sequence
-    length.
-  initial_state: An initial state for the RNN. This must be a tensor of
-    appropriate type and shape [batch_size x cell.state_size].
-  batch_size: Mini batch size.
-  steps: Number of steps to run over data.
-  optimizer: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
-  learning_rate: If this is constant float value, no decay function is
-    used. Instead, a customized decay function can be passed that accepts
-    global_step as parameter and returns a Tensor.
-    e.g. exponential decay function:
-    def exp_decay(global_step):
-        return tf.train.exponential_decay(
-            learning_rate=0.1, global_step,
-            decay_steps=2, decay_rate=0.001)
-  continue_training: when continue_training is True, once initialized
-    model will be continuely trained on every call of fit.
-  config: RunConfig object that controls the configurations of the
-    session, e.g. num_cores, gpu_memory_fraction, etc.
-  verbose: Controls the verbosity, possible values:
-    0: the algorithm and debug information is muted.
-    1: trainer prints the progress.
-    2: log device placement is printed.
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.__init__(rnn_size, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None, n_classes=0, batch_size=32, steps=50, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowRNNRegressor.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.bias_` {#TensorFlowRNNRegressor.bias_}
-
-Returns bias of the rnn layer.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowRNNRegressor.evaluate}
-
-See base class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowRNNRegressor.fit}
-
-Builds a neural network model given provided `model_fn` and training
-data X and y.
-
-Note: called first time constructs the graph and initializers
-variables. Consecutives times it will continue training the same model.
-This logic follows partial_fit() interface in scikit-learn.
-
-To restart learning, create new estimator.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class labels in classification, real numbers in regression).
-
-*  <b>`steps`</b>: int, number of steps to train.
-         If None or 0, train for `self.steps`.
-*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
-    invoke early stopping.
-*  <b>`logdir`</b>: the directory to save the log file that can be used for
-  optional visualization.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.get_params(deep=True)` {#TensorFlowRNNRegressor.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.get_tensor(name)` {#TensorFlowRNNRegressor.get_tensor}
-
-Returns tensor by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.get_tensor_value(name)` {#TensorFlowRNNRegressor.get_tensor_value}
-
-Returns value of the tensor give by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.get_variable_names()` {#TensorFlowRNNRegressor.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.model_dir` {#TensorFlowRNNRegressor.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.partial_fit(x, y)` {#TensorFlowRNNRegressor.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class label in classification, real numbers in regression).
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.predict(x, axis=1, batch_size=None)` {#TensorFlowRNNRegressor.predict}
-
-Predict class or regression for X.
-
-For a classification model, the predicted class for each sample in X is
-returned. For a regression model, the predicted value based on X is
-returned.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`axis`</b>: Which axis to argmax for classification.
-    By default axis 1 (next after batch) is used.
-    Use 2 for sequence predictions.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member
-    variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
-  value.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.predict_proba(x, batch_size=None)` {#TensorFlowRNNRegressor.predict_proba}
-
-Predict class probability of the input samples X.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
-  probabilities for each class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.restore(cls, path, config=None)` {#TensorFlowRNNRegressor.restore}
-
-Restores model from give path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Path to the checkpoints and other model information.
-*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
-      reconfigured.
-
-##### Returns:
-
-  Estimator, object of the subclass of TensorFlowEstimator.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.save(path)` {#TensorFlowRNNRegressor.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.set_params(**params)` {#TensorFlowRNNRegressor.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.train(input_fn, steps, monitors=None)` {#TensorFlowRNNRegressor.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNRegressor.weights_` {#TensorFlowRNNRegressor.weights_}
-
-Returns weights of the rnn layer.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.extract_dask_labels.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.extract_dask_labels.md
new file mode 100644
index 0000000000..15831ce758
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.extract_dask_labels.md
@@ -0,0 +1,4 @@
+### `tf.contrib.learn.extract_dask_labels(labels)` {#extract_dask_labels}
+
+Extract data from dask.Series for labels
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.read_batch_examples.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.read_batch_examples.md
new file mode 100644
index 0000000000..c5cec0542a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.read_batch_examples.md
@@ -0,0 +1,39 @@
+### `tf.contrib.learn.read_batch_examples(file_pattern, batch_size, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, num_threads=1, name=None)` {#read_batch_examples}
+
+Adds operations to read, queue, batch `Example` protos.
+
+Given file pattern (or list of files), will setup a queue for file names,
+read `Example` proto using provided `reader`, use batch queue to create
+batches of examples of size `batch_size`.
+
+All queue runners are added to the queue runners collection, and may be
+started via `start_queue_runners`.
+
+All ops are added to the default graph.
+
+##### Args:
+
+
+*  <b>`file_pattern`</b>: List of files or pattern of file paths containing
+      `Example` records. See `tf.gfile.Glob` for pattern rules.
+*  <b>`batch_size`</b>: An int or scalar `Tensor` specifying the batch size to use.
+*  <b>`reader`</b>: A function or class that returns an object with
+    `read` method, (filename tensor) -> (example tensor).
+*  <b>`randomize_input`</b>: Whether the input should be randomized.
+*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
+    dataset. If `None`, cycles through the dataset forever.
+    NOTE - If specified, creates a variable that must be initialized, so call
+    `tf.initialize_all_variables()` as shown in the tests.
+*  <b>`queue_capacity`</b>: Capacity for input queue.
+*  <b>`num_threads`</b>: The number of threads enqueuing examples.
+*  <b>`name`</b>: Name of resulting op.
+
+##### Returns:
+
+  String `Tensor` of batched `Example` proto.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: for invalid inputs.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.read_batch_record_features.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.read_batch_record_features.md
deleted file mode 100644
index aa4e964be1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.read_batch_record_features.md
+++ /dev/null
@@ -1,33 +0,0 @@
-### `tf.contrib.learn.read_batch_record_features(file_pattern, batch_size, features, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name='dequeue_record_examples')` {#read_batch_record_features}
-
-Reads TFRecord, queues, batches and parses `Example` proto.
-
-See more detailed description in `read_examples`.
-
-##### Args:
-
-
-*  <b>`file_pattern`</b>: List of files or pattern of file paths containing
-      `Example` records. See `tf.gfile.Glob` for pattern rules.
-*  <b>`batch_size`</b>: An int or scalar `Tensor` specifying the batch size to use.
-*  <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
-    `VarLenFeature` values.
-*  <b>`randomize_input`</b>: Whether the input should be randomized.
-*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
-    dataset. If None, cycles through the dataset forever. NOTE - If specified,
-    creates a variable that must be initialized, so call
-    tf.initialize_all_variables() as shown in the tests.
-*  <b>`queue_capacity`</b>: Capacity for input queue.
-*  <b>`reader_num_threads`</b>: The number of threads to read examples.
-*  <b>`parser_num_threads`</b>: The number of threads to parse examples.
-*  <b>`name`</b>: Name of resulting op.
-
-##### Returns:
-
-  A dict of `Tensor` or `SparseTensor` objects for each in `features`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: for invalid inputs.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.accuracy.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.accuracy.md
new file mode 100644
index 0000000000..f41fb78e31
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.accuracy.md
@@ -0,0 +1,23 @@
+### `tf.contrib.metrics.accuracy(predictions, labels, weights=None)` {#accuracy}
+
+Computes the percentage of times that predictions matches labels.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: the predicted values, a `Tensor` whose dtype and shape
+               matches 'labels'.
+*  <b>`labels`</b>: the ground truth values, a `Tensor` of any shape and
+          integer or string dtype.
+*  <b>`weights`</b>: None or `Tensor` of float values to reweight the accuracy.
+
+##### Returns:
+
+  Accuracy `Tensor`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if dtypes don't match or
+              if dtype is not integer or string.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.auc_using_histogram.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.auc_using_histogram.md
deleted file mode 100644
index 01f67e402c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.auc_using_histogram.md
+++ /dev/null
@@ -1,38 +0,0 @@
-### `tf.contrib.metrics.auc_using_histogram(boolean_labels, scores, score_range, nbins=100, collections=None, check_shape=True, name=None)` {#auc_using_histogram}
-
-AUC computed by maintaining histograms.
-
-Rather than computing AUC directly, this Op maintains Variables containing
-histograms of the scores associated with `True` and `False` labels.  By
-comparing these the AUC is generated, with some discretization error.
-See: "Efficient AUC Learning Curve Calculation" by Bouckaert.
-
-This AUC Op updates in `O(batch_size + nbins)` time and works well even with
-large class imbalance.  The accuracy is limited by discretization error due
-to finite number of bins.  If scores are concentrated in a fewer bins,
-accuracy is lower.  If this is a concern, we recommend trying different
-numbers of bins and comparing results.
-
-##### Args:
-
-
-*  <b>`boolean_labels`</b>: 1-D boolean `Tensor`.  Entry is `True` if the corresponding
-    record is in class.
-*  <b>`scores`</b>: 1-D numeric `Tensor`, same shape as boolean_labels.
-*  <b>`score_range`</b>: `Tensor` of shape `[2]`, same dtype as `scores`.  The min/max
-    values of score that we expect.  Scores outside range will be clipped.
-*  <b>`nbins`</b>: Integer number of bins to use.  Accuracy strictly increases as the
-    number of bins increases.
-*  <b>`collections`</b>: List of graph collections keys. Internal histogram Variables
-    are added to these collections. Defaults to `[GraphKeys.LOCAL_VARIABLES]`.
-*  <b>`check_shape`</b>: Boolean.  If `True`, do a runtime shape check on the scores
-    and labels.
-*  <b>`name`</b>: A name for this Op.  Defaults to "auc_using_histogram".
-
-##### Returns:
-
-
-*  <b>`auc`</b>: `float32` scalar `Tensor`.  Fetching this converts internal histograms
-    to auc value.
-*  <b>`update_op`</b>: `Op`, when run, updates internal histograms.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.set_union.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.set_union.md
new file mode 100644
index 0000000000..bb378fe2a2
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.set_union.md
@@ -0,0 +1,23 @@
+### `tf.contrib.metrics.set_union(a, b, validate_indices=True)` {#set_union}
+
+Compute set union of elements in last dimension of `a` and `b`.
+
+All but the last dimension of `a` and `b` must match.
+
+##### Args:
+
+
+*  <b>`a`</b>: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
+      must be sorted in row-major order.
+*  <b>`b`</b>: `Tensor` or `SparseTensor` of the same type as `a`. Must be
+      `SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
+      sorted in row-major order.
+*  <b>`validate_indices`</b>: Whether to validate the order and range of sparse indices
+     in `a` and `b`.
+
+##### Returns:
+
+  A `SparseTensor` with the same rank as `a` and `b`, and all but the last
+  dimension the same. Elements along the last dimension contain the
+  unions.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.streaming_auc.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.streaming_auc.md
new file mode 100644
index 0000000000..2d444fac54
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.metrics.streaming_auc.md
@@ -0,0 +1,58 @@
+### `tf.contrib.metrics.streaming_auc(predictions, labels, ignore_mask=None, num_thresholds=200, metrics_collections=None, updates_collections=None, name=None)` {#streaming_auc}
+
+Computes the approximate AUC via a Riemann sum.
+
+The `streaming_auc` function creates four local variables, `true_positives`,
+`true_negatives`, `false_positives` and `false_negatives` that are used to
+compute the AUC. To discretize the AUC curve, a linearly spaced set of
+thresholds is used to compute pairs of recall and precision values. The area
+under the curve is therefore computed using the height of the recall values
+by the false positive rate.
+
+This value is ultimately returned as `auc`, an idempotent
+operation the computes the area under a discretized curve of precision versus
+recall values (computed using the afformentioned variables). The
+`num_thresholds` variable controls the degree of discretization with larger
+numbers of thresholds more closely approximating the true AUC.
+
+To faciliate the estimation of the AUC over a stream of data, the function
+creates an `update_op` operation whose behavior is dependent on the value of
+`ignore_mask`. If `ignore_mask` is None, then `update_op` increments the
+`true_positives`, `true_negatives`, `false_positives` and `false_negatives`
+counts with the number of each found in the current `predictions` and `labels`
+`Tensors`. If `ignore_mask` is not `None`, then the increment is performed
+using only the elements of `predictions` and `labels` whose corresponding
+value in `ignore_mask` is `False`. In addition to performing the updates,
+`update_op` also returns the `auc`.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: A floating point `Tensor` of arbitrary shape and whose values
+    are in the range `[0, 1]`.
+*  <b>`labels`</b>: A binary `Tensor` whose shape matches `predictions`.
+*  <b>`ignore_mask`</b>: An optional, binary tensor whose size matches `predictions`.
+*  <b>`num_thresholds`</b>: The number of thresholds to use when discretizing the roc
+    curve.
+*  <b>`metrics_collections`</b>: An optional list of collections that `auc` should be
+    added to.
+*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
+    be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`auc`</b>: A scalar tensor representing the current area-under-curve.
+*  <b>`update_op`</b>: An operation that increments the `true_positives`,
+    `true_negatives`, `false_positives` and `false_negatives` variables
+    appropriately and whose value matches `auc`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the shape of `predictions` and `labels` do not match or if
+    `ignore_mask` is not `None` and its shape doesn't match `predictions` or
+    if either `metrics_collections` or `updates_collections` are not a list or
+    tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.util.make_ndarray.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.util.make_ndarray.md
deleted file mode 100644
index 7b2a81d48e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.util.make_ndarray.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.contrib.util.make_ndarray(tensor)` {#make_ndarray}
-
-Create a numpy ndarray from a tensor.
-
-Create a numpy ndarray with the same shape and data as the tensor.
-
-##### Args:
-
-
-*  <b>`tensor`</b>: A TensorProto.
-
-##### Returns:
-
-  A numpy array with the tensor contents.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if tensor has unsupported type.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.util.ops_used_by_graph_def.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.util.ops_used_by_graph_def.md
deleted file mode 100644
index 38a9cc4f43..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.util.ops_used_by_graph_def.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.contrib.util.ops_used_by_graph_def(graph_def)` {#ops_used_by_graph_def}
-
-Collect the list of ops used by a graph.
-
-Does not validate that the ops are all registered.
-
-##### Args:
-
-
-*  <b>`graph_def`</b>: A `GraphDef` proto, as from `graph.as_graph_def()`.
-
-##### Returns:
-
-  A list of strings, each naming an op used by the graph.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.util.stripped_op_list_for_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.util.stripped_op_list_for_graph.md
new file mode 100644
index 0000000000..23bfb28542
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.util.stripped_op_list_for_graph.md
@@ -0,0 +1,23 @@
+### `tf.contrib.util.stripped_op_list_for_graph(graph_def)` {#stripped_op_list_for_graph}
+
+Collect the stripped OpDefs for ops used by a graph.
+
+This function computes the `stripped_op_list` field of `MetaGraphDef` and
+similar protos.  The result can be communicated from the producer to the
+consumer, which can then use the C++ function
+`RemoveNewDefaultAttrsFromGraphDef` to improve forwards compatibility.
+
+##### Args:
+
+
+*  <b>`graph_def`</b>: A `GraphDef` proto, as from `graph.as_graph_def()`.
+
+##### Returns:
+
+  An `OpList` of ops used by the graph.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If an unregistered op is used.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.convert_to_tensor_or_indexed_slices.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.convert_to_tensor_or_indexed_slices.md
deleted file mode 100644
index 0c65e8327c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.convert_to_tensor_or_indexed_slices.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.convert_to_tensor_or_indexed_slices(value, dtype=None, name=None, as_ref=False)` {#convert_to_tensor_or_indexed_slices}
-
-Converts the given object to a `Tensor` or an `IndexedSlices`.
-
-If `value` is an `IndexedSlices` or `SparseTensor` it is returned
-unmodified. Otherwise, it is converted to a `Tensor` using
-`convert_to_tensor()`.
-
-##### Args:
-
-
-*  <b>`value`</b>: An `IndexedSlices`, `SparseTensor`, or an object that can be consumed
-    by `convert_to_tensor()`.
-*  <b>`dtype`</b>: (Optional.) The required `DType` of the returned `Tensor` or
-    `IndexedSlices`.
-*  <b>`name`</b>: (Optional.) A name to use if a new `Tensor` is created.
-*  <b>`as_ref`</b>: True if the caller wants the results as ref tensors.
-
-##### Returns:
-
-  An `Tensor`, `IndexedSlices`, or `SparseTensor` based on `value`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `dtype` does not match the element type of `value`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.depth_to_space.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.depth_to_space.md
deleted file mode 100644
index c0117c82c7..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.depth_to_space.md
+++ /dev/null
@@ -1,95 +0,0 @@
-### `tf.depth_to_space(input, block_size, name=None)` {#depth_to_space}
-
-DepthToSpace for tensors of type T.
-
-Rearranges data from depth into blocks of spatial data.
-This is the reverse transformation of SpaceToDepth. More specifically,
-this op outputs a copy of the input tensor where values from the `depth`
-dimension are moved in spatial blocks to the `height` and `width` dimensions.
-The attr `block_size` indicates the input block size and how the data is moved.
-
-  * Chunks of data of size `block_size * block_size` from depth are rearranged
-    into non-overlapping blocks of size `block_size x block_size`
-  * The width the output tensor is `input_depth * block_size`, whereas the
-    height is `input_height * block_size`.
-  * The depth of the input tensor must be divisible by
-    `block_size * block_size`.
-
-That is, assuming the input is in the shape:
-`[batch, height, width, depth]`,
-the shape of the output will be:
-`[batch, height*block_size, width*block_size, depth/(block_size*block_size)]`
-
-This operation requires that the input tensor be of rank 4, and that
-`block_size` be >=1 and that `block_size * block_size` be a divisor of the
-input depth.
-
-This operation is useful for resizing the activations between convolutions
-(but keeping all data), e.g. instead of pooling. It is also useful for training
-purely convolutional models.
-
-For example, given this input of shape `[1, 1, 1, 4]`, and a block size of 2:
-
-```prettyprint
-x = [[[[1, 2, 3, 4]]]]
-
-```
-
-This operation will output a tensor of shape `[1, 2, 2, 1]`:
-
-```prettyprint
-   [[[[1], [2]],
-     [[3], [4]]]]
-```
-
-Here, the input has a batch of 1 and each batch element has shape `[1, 1, 4]`,
-the corresponding output will have 2x2 elements and will have a depth of
-1 channel (1 = `4 / (block_size * block_size)`).
-The output element shape is `[2, 2, 1]`.
-
-For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`, e.g.
-
-```prettyprint
-x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
-```
-
-This operation, for block size of 2, will return the following tensor of shape
-`[1, 2, 2, 3]`
-
-```prettyprint
-   [[[[1, 2, 3], [4, 5, 6]],
-     [[7, 8, 9], [10, 11, 12]]]]
-
-```
-
-Similarly, for the following input of shape `[1 2 2 4]`, and a block size of 2:
-
-```prettyprint
-x =  [[[[1, 2, 3, 4],
-       [5, 6, 7, 8]],
-      [[9, 10, 11, 12],
-       [13, 14, 15, 16]]]]
-```
-
-the operator will return the following tensor of shape `[1 4 4 1]`:
-
-```prettyprint
-x = [[ [1],   [2],  [5],  [6]],
-     [ [3],   [4],  [7],  [8]],
-     [ [9],  [10], [13],  [14]],
-     [ [11], [12], [15],  [16]]]
-
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`.
-*  <b>`block_size`</b>: An `int`.
-    The size of the spatial block, same as in Space2Depth.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.erf.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.erf.md
deleted file mode 100644
index 3a425b7c4a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.erf.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.erf(x, name=None)` {#erf}
-
-Computes the Gauss error function of `x` element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.errors.DeadlineExceededError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.errors.DeadlineExceededError.md
deleted file mode 100644
index e8ef3be06e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.errors.DeadlineExceededError.md
+++ /dev/null
@@ -1,11 +0,0 @@
-Raised when a deadline expires before an operation could complete.
-
-This exception is not currently used.
-
-- - -
-
-#### `tf.errors.DeadlineExceededError.__init__(node_def, op, message)` {#DeadlineExceededError.__init__}
-
-Creates a `DeadlineExceededError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.errors.NotFoundError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.errors.NotFoundError.md
deleted file mode 100644
index 49fec3c55c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.errors.NotFoundError.md
+++ /dev/null
@@ -1,14 +0,0 @@
-Raised when a requested entity (e.g., a file or directory) was not found.
-
-For example, running the
-[`tf.WholeFileReader.read()`](../../api_docs/python/io_ops.md#WholeFileReader)
-operation could raise `NotFoundError` if it receives the name of a file that
-does not exist.
-
-- - -
-
-#### `tf.errors.NotFoundError.__init__(node_def, op, message)` {#NotFoundError.__init__}
-
-Creates a `NotFoundError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.exp.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.exp.md
deleted file mode 100644
index 6bceeabd27..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.exp.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.exp(x, name=None)` {#exp}
-
-Computes exponential of x element-wise.  \\(y = e^x\\).
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.gather_nd.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.gather_nd.md
new file mode 100644
index 0000000000..7c8777a660
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.gather_nd.md
@@ -0,0 +1,30 @@
+### `tf.gather_nd(params, indices, name=None)` {#gather_nd}
+
+Gather values from `params` according to `indices`.
+
+`indices` must be integer tensor, containing indices into `params`.
+It must be shape `[d_0, ..., d_N, R]` where `R` is the rank of `params`.
+The innermost dimension of `indices` (with length `R`) corresponds to the
+indices of `params`.
+
+Produces an output tensor with shape `[d_0, ..., d_{n-1}]` where:
+
+    output[i, j, k, ...] = params[indices[i, j, k, ..., :]]
+
+e.g. for `indices` a matrix:
+
+    output[i] = params[indices[i, :]]
+
+##### Args:
+
+
+*  <b>`params`</b>: A `Tensor`. R-D.  The tensor from which to gather values.
+*  <b>`indices`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    (N+1)-D.  Index tensor having shape `[d_0, ..., d_N, R]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `params`.
+  N-D.  Values from `params` gathered from indices given by `indices`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.get_collection_ref.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.get_collection_ref.md
new file mode 100644
index 0000000000..c393da2233
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.get_collection_ref.md
@@ -0,0 +1,20 @@
+### `tf.get_collection_ref(key)` {#get_collection_ref}
+
+Wrapper for `Graph.get_collection_ref()` using the default graph.
+
+See [`Graph.get_collection_ref()`](../../api_docs/python/framework.md#Graph.get_collection_ref)
+for more details.
+
+##### Args:
+
+
+*  <b>`key`</b>: The key for the collection. For example, the `GraphKeys` class
+    contains many standard names for collections.
+
+##### Returns:
+
+  The list of values in the collection with the given `name`, or an empty
+  list if no value has been added to that collection.  Note that this returns
+  the collection list itself, which can be modified in place to change the
+  collection.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.gradients.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.gradients.md
deleted file mode 100644
index ea710b2a15..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.gradients.md
+++ /dev/null
@@ -1,48 +0,0 @@
-### `tf.gradients(ys, xs, grad_ys=None, name='gradients', colocate_gradients_with_ops=False, gate_gradients=False, aggregation_method=None)` {#gradients}
-
-Constructs symbolic partial derivatives of sum of `ys` w.r.t. x in `xs`.
-
-`ys` and `xs` are each a `Tensor` or a list of tensors.  `grad_ys`
-is a list of `Tensor`, holding the gradients received by the
-`ys`. The list must be the same length as `ys`.
-
-`gradients()` adds ops to the graph to output the partial
-derivatives of `ys` with respect to `xs`.  It returns a list of
-`Tensor` of length `len(xs)` where each tensor is the `sum(dy/dx)`
-for y in `ys`.
-
-`grad_ys` is a list of tensors of the same length as `ys` that holds
-the initial gradients for each y in `ys`.  When `grad_ys` is None,
-we fill in a tensor of '1's of the shape of y for each y in `ys`.  A
-user can provide their own initial `grad_ys` to compute the
-derivatives using a different initial gradient for each y (e.g., if
-one wanted to weight the gradient differently for each value in
-each y).
-
-##### Args:
-
-
-*  <b>`ys`</b>: A `Tensor` or list of tensors to be differentiated.
-*  <b>`xs`</b>: A `Tensor` or list of tensors to be used for differentiation.
-*  <b>`grad_ys`</b>: Optional. A `Tensor` or list of tensors the same size as
-    `ys` and holding the gradients computed for each y in `ys`.
-*  <b>`name`</b>: Optional name to use for grouping all the gradient ops together.
-    defaults to 'gradients'.
-*  <b>`colocate_gradients_with_ops`</b>: If True, try colocating gradients with
-    the corresponding op.
-*  <b>`gate_gradients`</b>: If True, add a tuple around the gradients returned
-    for an operations.  This avoids some race conditions.
-*  <b>`aggregation_method`</b>: Specifies the method used to combine gradient terms.
-    Accepted values are constants defined in the class `AggregationMethod`.
-
-##### Returns:
-
-  A list of `sum(dy/dx)` for each x in `xs`.
-
-##### Raises:
-
-
-*  <b>`LookupError`</b>: if one of the operations between `x` and `y` does not
-    have a registered gradient function.
-*  <b>`ValueError`</b>: if the arguments are invalid.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.greater.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.greater.md
new file mode 100644
index 0000000000..c629a0286f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.greater.md
@@ -0,0 +1,15 @@
+### `tf.greater(x, y, name=None)` {#greater}
+
+Returns the truth value of (x > y) element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.identity.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.identity.md
deleted file mode 100644
index 13f1318601..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.identity.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.identity(input, name=None)` {#identity}
-
-Return a tensor with the same shape and contents as the input tensor or value.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.ifft3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.ifft3d.md
deleted file mode 100644
index 35d58888ac..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.ifft3d.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.ifft3d(input, name=None)` {#ifft3d}
-
-Compute the inverse 3-dimensional discrete Fourier Transform.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 3-D tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`.
-  The inverse 3D Fourier Transform of `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.adjust_contrast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.adjust_contrast.md
deleted file mode 100644
index 2fbf1b3e2a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.adjust_contrast.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.image.adjust_contrast(images, contrast_factor)` {#adjust_contrast}
-
-Adjust contrast of RGB or grayscale images.
-
-This is a convenience method that converts an RGB image to float
-representation, adjusts its contrast, and then converts it back to the
-original data type. If several adjustments are chained it is advisable to
-minimize the number of redundant conversions.
-
-`images` is a tensor of at least 3 dimensions.  The last 3 dimensions are
-interpreted as `[height, width, channels]`.  The other dimensions only
-represent a collection of images, such as `[batch, height, width, channels].`
-
-Contrast is adjusted independently for each channel of each image.
-
-For each channel, this Op computes the mean of the image pixels in the
-channel and then adjusts each component `x` of each pixel to
-`(x - mean) * contrast_factor + mean`.
-
-##### Args:
-
-
-*  <b>`images`</b>: Images to adjust.  At least 3-D.
-*  <b>`contrast_factor`</b>: A float multiplier for adjusting contrast.
-
-##### Returns:
-
-  The contrast-adjusted image or images.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.adjust_hue.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.adjust_hue.md
new file mode 100644
index 0000000000..e334e26184
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.adjust_hue.md
@@ -0,0 +1,26 @@
+### `tf.image.adjust_hue(image, delta, name=None)` {#adjust_hue}
+
+Adjust hue of an RGB image.
+
+This is a convenience method that converts an RGB image to float
+representation, converts it to HSV, add an offset to the hue channel, converts
+back to RGB and then back to the original data type. If several adjustments
+are chained it is advisable to minimize the number of redundant conversions.
+
+`image` is an RGB image.  The image hue is adjusted by converting the
+image to HSV and rotating the hue channel (H) by
+`delta`.  The image is then converted back to RGB.
+
+`delta` must be in the interval `[-1, 1]`.
+
+##### Args:
+
+
+*  <b>`image`</b>: RGB image or images. Size of the last dimension must be 3.
+*  <b>`delta`</b>: float.  How much to add to the hue channel.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  Adjusted image(s), same shape and DType as `image`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.adjust_saturation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.adjust_saturation.md
deleted file mode 100644
index 1829271ff6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.adjust_saturation.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.image.adjust_saturation(image, saturation_factor, name=None)` {#adjust_saturation}
-
-Adjust saturation of an RGB image.
-
-This is a convenience method that converts an RGB image to float
-representation, converts it to HSV, add an offset to the saturation channel,
-converts back to RGB and then back to the original data type. If several
-adjustments are chained it is advisable to minimize the number of redundant
-conversions.
-
-`image` is an RGB image.  The image saturation is adjusted by converting the
-image to HSV and multiplying the saturation (S) channel by
-`saturation_factor` and clipping. The image is then converted back to RGB.
-
-##### Args:
-
-
-*  <b>`image`</b>: RGB image or images. Size of the last dimension must be 3.
-*  <b>`saturation_factor`</b>: float. Factor to multiply the saturation by.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  Adjusted image(s), same shape and DType as `image`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.extract_glimpse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.extract_glimpse.md
new file mode 100644
index 0000000000..e0ca72e2c5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.extract_glimpse.md
@@ -0,0 +1,31 @@
+### `tf.image.extract_glimpse(input, size, offsets, centered=None, normalized=None, uniform_noise=None, name=None)` {#extract_glimpse}
+
+Extracts a glimpse from the input tensor.
+
+Returns a set of windows called glimpses extracted at location
+`offsets` from the input tensor. If the windows only partially
+overlaps the inputs, the non overlapping areas will be filled with
+random noise.
+
+The result is a 4-D tensor of shape `[batch_size, glimpse_height,
+glimpse_width, channels]`. The channels and batch dimensions are the
+same as that of the input tensor. The height and width of the output
+windows are specified in the `size` parameter.
+
+The argument `normalized` and `centered` controls how the windows are
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `float32`.
+*  <b>`size`</b>: A `Tensor` of type `int32`.
+*  <b>`offsets`</b>: A `Tensor` of type `float32`.
+*  <b>`centered`</b>: An optional `bool`. Defaults to `True`.
+*  <b>`normalized`</b>: An optional `bool`. Defaults to `True`.
+*  <b>`uniform_noise`</b>: An optional `bool`. Defaults to `True`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `float32`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.grayscale_to_rgb.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.grayscale_to_rgb.md
deleted file mode 100644
index 755b66141b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.grayscale_to_rgb.md
+++ /dev/null
@@ -1,17 +0,0 @@
-### `tf.image.grayscale_to_rgb(images, name=None)` {#grayscale_to_rgb}
-
-Converts one or more images from Grayscale to RGB.
-
-Outputs a tensor of the same `DType` and rank as `images`.  The size of the
-last dimension of the output is 3, containing the RGB value of the pixels.
-
-##### Args:
-
-
-*  <b>`images`</b>: The Grayscale tensor to convert. Last dimension must be size 1.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The converted grayscale image(s).
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.random_hue.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.random_hue.md
new file mode 100644
index 0000000000..09a4ebc17f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image.random_hue.md
@@ -0,0 +1,28 @@
+### `tf.image.random_hue(image, max_delta, seed=None)` {#random_hue}
+
+Adjust the hue of an RGB image by a random factor.
+
+Equivalent to `adjust_hue()` but uses a `delta` randomly
+picked in the interval `[-max_delta, max_delta]`.
+
+`max_delta` must be in the interval `[0, 0.5]`.
+
+##### Args:
+
+
+*  <b>`image`</b>: RGB image or images. Size of the last dimension must be 3.
+*  <b>`max_delta`</b>: float.  Maximum value for the random delta.
+*  <b>`seed`</b>: An operation-specific seed. It will be used in conjunction
+    with the graph-level seed to determine the real seeds that will be
+    used in this operation. Please see the documentation of
+    set_random_seed for its interaction with the graph-level random seed.
+
+##### Returns:
+
+  3-D float tensor of shape `[height, width, channels]`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `max_delta` is invalid.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image_summary.md
new file mode 100644
index 0000000000..5df729544b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.image_summary.md
@@ -0,0 +1,48 @@
+### `tf.image_summary(tag, tensor, max_images=3, collections=None, name=None)` {#image_summary}
+
+Outputs a `Summary` protocol buffer with images.
+
+The summary has up to `max_images` summary values containing images. The
+images are built from `tensor` which must be 4-D with shape `[batch_size,
+height, width, channels]` and where `channels` can be:
+
+*  1: `tensor` is interpreted as Grayscale.
+*  3: `tensor` is interpreted as RGB.
+*  4: `tensor` is interpreted as RGBA.
+
+The images have the same number of channels as the input tensor. For float
+input, the values are normalized one image at a time to fit in the range
+`[0, 255]`.  `uint8` values are unchanged.  The op uses two different
+normalization algorithms:
+
+*  If the input values are all positive, they are rescaled so the largest one
+   is 255.
+
+*  If any input value is negative, the values are shifted so input value 0.0
+   is at 127.  They are then rescaled so that either the smallest value is 0,
+   or the largest one is 255.
+
+The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
+build the `tag` of the summary values:
+
+*  If `max_images` is 1, the summary value tag is '*tag*/image'.
+*  If `max_images` is greater than 1, the summary value tags are
+   generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
+
+##### Args:
+
+
+*  <b>`tag`</b>: A scalar `Tensor` of type `string`. Used to build the `tag`
+    of the summary values.
+*  <b>`tensor`</b>: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
+    width, channels]` where `channels` is 1, 3, or 4.
+*  <b>`max_images`</b>: Max number of batch elements to generate images for.
+*  <b>`collections`</b>: Optional list of ops.GraphKeys.  The collections to add the
+    summary to.  Defaults to [ops.GraphKeys.SUMMARIES]
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A scalar `Tensor` of type `string`. The serialized `Summary` protocol
+  buffer.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.is_non_decreasing.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.is_non_decreasing.md
new file mode 100644
index 0000000000..f10ff932c0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.is_non_decreasing.md
@@ -0,0 +1,25 @@
+### `tf.is_non_decreasing(x, name=None)` {#is_non_decreasing}
+
+Returns `True` if `x` is non-decreasing.
+
+Elements of `x` are compared in row-major order.  The tensor `[x[0],...]`
+is non-decreasing if for every adjacent pair we have `x[i] <= x[i+1]`.
+If `x` has less than two elements, it is trivially non-decreasing.
+
+See also:  `is_strictly_increasing`
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`name`</b>: A name for this operation (optional).  Defaults to "is_non_decreasing"
+
+##### Returns:
+
+  Boolean `Tensor`, equal to `True` iff `x` is non-decreasing.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `x` is not a numeric tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.is_strictly_increasing.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.is_strictly_increasing.md
new file mode 100644
index 0000000000..bdaedd519e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.is_strictly_increasing.md
@@ -0,0 +1,26 @@
+### `tf.is_strictly_increasing(x, name=None)` {#is_strictly_increasing}
+
+Returns `True` if `x` is strictly increasing.
+
+Elements of `x` are compared in row-major order.  The tensor `[x[0],...]`
+is strictly increasing if for every adjacent pair we have `x[i] < x[i+1]`.
+If `x` has less than two elements, it is trivially strictly increasing.
+
+See also:  `is_non_decreasing`
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`name`</b>: A name for this operation (optional).
+    Defaults to "is_strictly_increasing"
+
+##### Returns:
+
+  Boolean `Tensor`, equal to `True` iff `x` is strictly increasing.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `x` is not a numeric tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.less_equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.less_equal.md
deleted file mode 100644
index 65d7eb5084..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.less_equal.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.less_equal(x, y, name=None)` {#less_equal}
-
-Returns the truth value of (x <= y) element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.make_template.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.make_template.md
deleted file mode 100644
index bb0cff57cd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.make_template.md
+++ /dev/null
@@ -1,105 +0,0 @@
-### `tf.make_template(name_, func_, create_scope_now_=False, **kwargs)` {#make_template}
-
-Given an arbitrary function, wrap it so that it does variable sharing.
-
-This wraps `func_` in a Template and partially evaluates it. Templates are
-functions that create variables the first time they are called and reuse them
-thereafter. In order for `func_` to be compatible with a `Template` it must
-have the following properties:
-
-* The function should create all trainable variables and any variables that
-   should be reused by calling `tf.get_variable`. If a trainable variable is
-   created using `tf.Variable`, then a ValueError will be thrown. Variables
-   that are intended to be locals can be created by specifying
-   `tf.Variable(..., trainable=false)`.
-* The function may use variable scopes and other templates internally to
-    create and reuse variables, but it shouldn't use `tf.get_variables` to
-    capture variables that are defined outside of the scope of the function.
-* Internal scopes and variable names should not depend on any arguments that
-    are not supplied to `make_template`. In general you will get a ValueError
-    telling you that you are trying to reuse a variable that doesn't exist
-    if you make a mistake.
-
-In the following example, both `z` and `w` will be scaled by the same `y`. It
-is important to note that if we didn't assign `scalar_name` and used a
-different name for z and w that a `ValueError` would be thrown because it
-couldn't reuse the variable.
-
-```python
-def my_op(x, scalar_name):
-  var1 = tf.get_variable(scalar_name,
-                         shape=[],
-                         initializer=tf.constant_initializer(1))
-  return x * var1
-
-scale_by_y = tf.make_template('scale_by_y', my_op, scalar_name='y')
-
-z = scale_by_y(input1)
-w = scale_by_y(input2)
-```
-
-As a safe-guard, the returned function will raise a `ValueError` after the
-first call if trainable variables are created by calling `tf.Variable`.
-
-If all of these are true, then 2 properties are enforced by the template:
-
-1. Calling the same template multiple times will share all non-local
-    variables.
-2. Two different templates are guaranteed to be unique, unless you reenter the
-    same variable scope as the initial definition of a template and redefine
-    it. An examples of this exception:
-
-```python
-def my_op(x, scalar_name):
-  var1 = tf.get_variable(scalar_name,
-                         shape=[],
-                         initializer=tf.constant_initializer(1))
-  return x * var1
-
-with tf.variable_scope('scope') as vs:
-  scale_by_y = tf.make_template('scale_by_y', my_op, scalar_name='y')
-  z = scale_by_y(input1)
-  w = scale_by_y(input2)
-
-# Creates a template that reuses the variables above.
-with tf.variable_scope(vs, reuse=True):
-  scale_by_y2 = tf.make_template('scale_by_y', my_op, scalar_name='y')
-  z2 = scale_by_y2(input1)
-  w2 = scale_by_y2(input2)
-```
-
-Depending on the value of `create_scope_now_`, the full variable scope may be
-captured either at the time of first call or at the time of construction. If
-this option is set to True, then all Tensors created by repeated calls to the
-template will have an extra trailing _N+1 to their name, as the first time the
-scope is entered in the Template constructor no Tensors are created.
-
-Note: `name_`, `func_` and `create_scope_now_` have a trailing underscore to
-reduce the likelihood of collisions with kwargs.
-
-##### Args:
-
-
-*  <b>`name_`</b>: A name for the scope created by this template. If necessary, the name
-    will be made unique by appending `_N` to the name.
-*  <b>`func_`</b>: The function to wrap.
-*  <b>`create_scope_now_`</b>: Boolean controlling whether the scope should be created
-    when the template is constructed or when the template is called. Default
-    is False, meaning the scope is created when the template is called.
-*  <b>`**kwargs`</b>: Keyword arguments to apply to `func_`.
-
-##### Returns:
-
-  A function to encapsulate a set of variables which should be created once
-  and reused. An enclosing scope will created, either where `make_template`
-  is called, or wherever the result is called, depending on the value of
-  `create_scope_now_`. Regardless of the value, the first time the template
-  is called it will enter the scope with no reuse, and call `func_` to create
-  variables, which are guaranteed to be unique. All subsequent calls will
-  re-enter the scope and reuse those variables.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if the name is None.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.multinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.multinomial.md
deleted file mode 100644
index b5bf7a30a5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.multinomial.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.multinomial(logits, num_samples, seed=None, name=None)` {#multinomial}
-
-Draws samples from a multinomial distribution.
-
-Example:
-
-  samples = tf.multinomial(tf.log([[0.5, 0.5]]), 10)
-  # samples has shape [1, 10], where each value is either 0 or 1.
-
-  samples = tf.multinomial([[1, -1, -1]], 10)
-  # samples is equivalent to tf.zeros([1, 10], dtype=tf.int64).
-
-##### Args:
-
-
-*  <b>`logits`</b>: 2-D Tensor with shape `[batch_size, num_classes]`.  Each slice
-    `[i, :]` represents the unnormalized log probabilities for all classes.
-*  <b>`num_samples`</b>: 0-D.  Number of independent samples to draw for each row slice.
-*  <b>`seed`</b>: A Python integer. Used to create a random seed for the distribution.
-    See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`name`</b>: Optional name for the operation.
-
-##### Returns:
-
-  The drawn samples of shape `[batch_size, num_samples]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.avg_pool3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.avg_pool3d.md
new file mode 100644
index 0000000000..76503e0567
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.avg_pool3d.md
@@ -0,0 +1,24 @@
+### `tf.nn.avg_pool3d(input, ksize, strides, padding, name=None)` {#avg_pool3d}
+
+Performs 3D average pooling on the input.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+    Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
+*  <b>`ksize`</b>: A list of `ints` that has length `>= 5`.
+    1-D tensor of length 5. The size of the window for each dimension of
+    the input tensor. Must have `ksize[0] = ksize[1] = 1`.
+*  <b>`strides`</b>: A list of `ints` that has length `>= 5`.
+    1-D tensor of length 5. The stride of the sliding window for each
+    dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
+    The type of padding algorithm to use.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+  The average pooled output tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.bias_add.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.bias_add.md
deleted file mode 100644
index 1eea161f23..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.bias_add.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.nn.bias_add(value, bias, data_format=None, name=None)` {#bias_add}
-
-Adds `bias` to `value`.
-
-This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D.
-Broadcasting is supported, so `value` may have any number of dimensions.
-Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the
-case where both types are quantized.
-
-##### Args:
-
-
-*  <b>`value`</b>: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`,
-    `int16`, `int8`, or `complex64`.
-*  <b>`bias`</b>: A 1-D `Tensor` with size matching the last dimension of `value`.
-    Must be the same type as `value` unless `value` is a quantized type,
-    in which case a different quantized type may be used.
-*  <b>`data_format`</b>: A string. 'NHWC' and 'NCHW' are supported.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` with the same type as `value`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.depthwise_conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.depthwise_conv2d.md
new file mode 100644
index 0000000000..7bacc20da8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.depthwise_conv2d.md
@@ -0,0 +1,37 @@
+### `tf.nn.depthwise_conv2d(input, filter, strides, padding, name=None)` {#depthwise_conv2d}
+
+Depthwise 2-D convolution.
+
+Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
+and a filter tensor of shape
+`[filter_height, filter_width, in_channels, channel_multiplier]`
+containing `in_channels` convolutional filters of depth 1, `depthwise_conv2d`
+applies a different filter to each input channel (expanding from 1 channel
+to `channel_multiplier` channels for each), then concatenates the results
+together.  The output has `in_channels * channel_multiplier` channels.
+
+In detail,
+
+    output[b, i, j, k * channel_multiplier + q] =
+        sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+                     filter[di, dj, k, q]
+
+Must have `strides[0] = strides[3] = 1`.  For the most common case of the
+same horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
+
+##### Args:
+
+
+*  <b>`input`</b>: 4-D with shape `[batch, in_height, in_width, in_channels]`.
+*  <b>`filter`</b>: 4-D with shape
+    `[filter_height, filter_width, in_channels, channel_multiplier]`.
+*  <b>`strides`</b>: 1-D of size 4.  The stride of the sliding window for each
+    dimension of `input`.
+*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`.  The padding algorithm.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  A 4-D `Tensor` of shape
+  `[batch, out_height, out_width, in_channels * channel_multiplier].`
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.embedding_lookup_sparse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.embedding_lookup_sparse.md
new file mode 100644
index 0000000000..03997f7813
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.embedding_lookup_sparse.md
@@ -0,0 +1,66 @@
+### `tf.nn.embedding_lookup_sparse(params, sp_ids, sp_weights, partition_strategy='mod', name=None, combiner='mean')` {#embedding_lookup_sparse}
+
+Computes embeddings for the given ids and weights.
+
+This op assumes that there is at least one id for each row in the dense tensor
+represented by sp_ids (i.e. there are no rows with empty features), and that
+all the indices of sp_ids are in canonical row-major order.
+
+It also assumes that all id values lie in the range [0, p0), where p0
+is the sum of the size of params along dimension 0.
+
+##### Args:
+
+
+*  <b>`params`</b>: A single tensor representing the complete embedding tensor,
+    or a list of P tensors all of same shape except for the first dimension,
+    representing sharded embedding tensors.
+*  <b>`sp_ids`</b>: N x M SparseTensor of int64 ids (typically from FeatureValueToId),
+    where N is typically batch size and M is arbitrary.
+*  <b>`sp_weights`</b>: either a SparseTensor of float / double weights, or None to
+    indicate all weights should be taken to be 1. If specified, sp_weights
+    must have exactly the same shape and indices as sp_ids.
+*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy, relevant
+    if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default
+    is `"mod"`. See `tf.nn.embedding_lookup` for more details.
+*  <b>`name`</b>: Optional name for the op.
+*  <b>`combiner`</b>: A string specifying the reduction op. Currently "mean", "sqrtn"
+    and "sum" are supported.
+    "sum" computes the weighted sum of the embedding results for each row.
+    "mean" is the weighted sum divided by the total weight.
+    "sqrtn" is the weighted sum divided by the square root of the sum of the
+    squares of the weights.
+
+##### Returns:
+
+  A dense tensor representing the combined embeddings for the
+  sparse ids. For each row in the dense tensor represented by sp_ids, the op
+  looks up the embeddings for all ids in that row, multiplies them by the
+  corresponding weight, and combines these embeddings as specified.
+
+  In other words, if
+    shape(combined params) = [p0, p1, ..., pm]
+  and
+    shape(sp_ids) = shape(sp_weights) = [d0, d1, ..., dn]
+  then
+    shape(output) = [d0, d1, ..., dn-1, p1, ..., pm].
+
+  For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are
+
+    [0, 0]: id 1, weight 2.0
+    [0, 1]: id 3, weight 0.5
+    [1, 0]: id 0, weight 1.0
+    [2, 3]: id 1, weight 3.0
+
+  with combiner="mean", then the output will be a 3x20 matrix where
+    output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5)
+    output[1, :] = params[0, :] * 1.0
+    output[2, :] = params[1, :] * 3.0
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If sp_ids is not a SparseTensor, or if sp_weights is neither
+    None nor SparseTensor.
+*  <b>`ValueError`</b>: If combiner is not one of {"mean", "sqrtn", "sum"}.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.fixed_unigram_candidate_sampler.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.fixed_unigram_candidate_sampler.md
new file mode 100644
index 0000000000..ad9b059e42
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.fixed_unigram_candidate_sampler.md
@@ -0,0 +1,75 @@
+### `tf.nn.fixed_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, vocab_file='', distortion=1.0, num_reserved_ids=0, num_shards=1, shard=0, unigrams=(), seed=None, name=None)` {#fixed_unigram_candidate_sampler}
+
+Samples a set of classes using the provided (fixed) base distribution.
+
+This operation randomly samples a tensor of sampled classes
+(`sampled_candidates`) from the range of integers `[0, range_max)`.
+
+The elements of `sampled_candidates` are drawn without replacement
+(if `unique=True`) or with replacement (if `unique=False`) from
+the base distribution.
+
+The base distribution is read from a file or passed in as an
+in-memory array. There is also an option to skew the distribution by
+applying a distortion power to the weights.
+
+In addition, this operation returns tensors `true_expected_count`
+and `sampled_expected_count` representing the number of times each
+of the target classes (`true_classes`) and the sampled
+classes (`sampled_candidates`) is expected to occur in an average
+tensor of sampled classes.  These values correspond to `Q(y|x)`
+defined in [this
+document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
+If `unique=True`, then these are post-rejection probabilities and we
+compute them approximately.
+
+##### Args:
+
+
+*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
+    num_true]`. The target classes.
+*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
+*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
+*  <b>`unique`</b>: A `bool`. Determines whether all sampled classes in a batch are
+    unique.
+*  <b>`range_max`</b>: An `int`. The number of possible classes.
+*  <b>`vocab_file`</b>: Each valid line in this file (which should have a CSV-like
+    format) corresponds to a valid word ID. IDs are in sequential order,
+    starting from num_reserved_ids. The last entry in each line is expected
+    to be a value corresponding to the count or relative probability. Exactly
+    one of `vocab_file` and `unigrams` needs to be passed to this operation.
+*  <b>`distortion`</b>: The distortion is used to skew the unigram probability
+    distribution.  Each weight is first raised to the distortion's power
+    before adding to the internal unigram distribution. As a result,
+    `distortion = 1.0` gives regular unigram sampling (as defined by the vocab
+    file), and `distortion = 0.0` gives a uniform distribution.
+*  <b>`num_reserved_ids`</b>: Optionally some reserved IDs can be added in the range
+    `[0, num_reserved_ids]` by the users. One use case is that a special
+    unknown word token is used as ID 0. These IDs will have a sampling
+    probability of 0.
+*  <b>`num_shards`</b>: A sampler can be used to sample from a subset of the original
+    range in order to speed up the whole computation through parallelism. This
+    parameter (together with `shard`) indicates the number of partitions that
+    are being used in the overall computation.
+*  <b>`shard`</b>: A sampler can be used to sample from a subset of the original range
+    in order to speed up the whole computation through parallelism. This
+    parameter (together with `num_shards`) indicates the particular partition
+    number of the operation, when partitioning is being used.
+*  <b>`unigrams`</b>: A list of unigram counts or probabilities, one per ID in
+    sequential order. Exactly one of `vocab_file` and `unigrams` should be
+    passed to this operation.
+*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+
+*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
+    The sampled classes.
+*  <b>`true_expected_count`</b>: A tensor of type `float`.  Same shape as
+    `true_classes`. The expected counts under the sampling distribution
+    of each of `true_classes`.
+*  <b>`sampled_expected_count`</b>: A tensor of type `float`. Same shape as
+    `sampled_candidates`. The expected counts under the sampling distribution
+    of each of `sampled_candidates`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.l2_normalize.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.l2_normalize.md
new file mode 100644
index 0000000000..fdcdd71e20
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.l2_normalize.md
@@ -0,0 +1,24 @@
+### `tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None)` {#l2_normalize}
+
+Normalizes along dimension `dim` using an L2 norm.
+
+For a 1-D tensor with `dim = 0`, computes
+
+    output = x / sqrt(max(sum(x**2), epsilon))
+
+For `x` with more dimensions, independently normalizes each 1-D slice along
+dimension `dim`.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`.
+*  <b>`dim`</b>: Dimension along which to normalize.
+*  <b>`epsilon`</b>: A lower bound value for the norm. Will use `sqrt(epsilon)` as the
+    divisor if `norm < sqrt(epsilon)`.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  A `Tensor` with the same shape as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.local_response_normalization.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.local_response_normalization.md
deleted file mode 100644
index 349e34fa73..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.local_response_normalization.md
+++ /dev/null
@@ -1,34 +0,0 @@
-### `tf.nn.local_response_normalization(input, depth_radius=None, bias=None, alpha=None, beta=None, name=None)` {#local_response_normalization}
-
-Local Response Normalization.
-
-The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
-dimension), and each vector is normalized independently.  Within a given vector,
-each component is divided by the weighted, squared sum of inputs within
-`depth_radius`.  In detail,
-
-    sqr_sum[a, b, c, d] =
-        sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
-    output = input / (bias + alpha * sqr_sum) ** beta
-
-For details, see [Krizhevsky et al., ImageNet classification with deep
-convolutional neural networks (NIPS 2012)]
-(http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `float32`. 4-D.
-*  <b>`depth_radius`</b>: An optional `int`. Defaults to `5`.
-    0-D.  Half-width of the 1-D normalization window.
-*  <b>`bias`</b>: An optional `float`. Defaults to `1`.
-    An offset (usually positive to avoid dividing by 0).
-*  <b>`alpha`</b>: An optional `float`. Defaults to `1`.
-    A scale factor, usually positive.
-*  <b>`beta`</b>: An optional `float`. Defaults to `0.5`. An exponent.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `float32`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.log_uniform_candidate_sampler.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.log_uniform_candidate_sampler.md
deleted file mode 100644
index baf9f9d421..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.log_uniform_candidate_sampler.md
+++ /dev/null
@@ -1,56 +0,0 @@
-### `tf.nn.log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)` {#log_uniform_candidate_sampler}
-
-Samples a set of classes using a log-uniform (Zipfian) base distribution.
-
-This operation randomly samples a tensor of sampled classes
-(`sampled_candidates`) from the range of integers `[0, range_max)`.
-
-The elements of `sampled_candidates` are drawn without replacement
-(if `unique=True`) or with replacement (if `unique=False`) from
-the base distribution.
-
-The base distribution for this operation is an approximately log-uniform
-or Zipfian distribution:
-
-`P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)`
-
-This sampler is useful when the target classes approximately follow such
-a distribution - for example, if the classes represent words in a lexicon
-sorted in decreasing order of frequency. If your classes are not ordered by
-decreasing frequency, do not use this op.
-
-In addition, this operation returns tensors `true_expected_count`
-and `sampled_expected_count` representing the number of times each
-of the target classes (`true_classes`) and the sampled
-classes (`sampled_candidates`) is expected to occur in an average
-tensor of sampled classes.  These values correspond to `Q(y|x)`
-defined in [this
-document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
-If `unique=True`, then these are post-rejection probabilities and we
-compute them approximately.
-
-##### Args:
-
-
-*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
-    num_true]`. The target classes.
-*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
-*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
-*  <b>`unique`</b>: A `bool`. Determines whether all sampled classes in a batch are
-    unique.
-*  <b>`range_max`</b>: An `int`. The number of possible classes.
-*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-
-*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
-    The sampled classes.
-*  <b>`true_expected_count`</b>: A tensor of type `float`.  Same shape as
-    `true_classes`. The expected counts under the sampling distribution
-    of each of `true_classes`.
-*  <b>`sampled_expected_count`</b>: A tensor of type `float`. Same shape as
-    `sampled_candidates`. The expected counts under the sampling distribution
-    of each of `sampled_candidates`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.normalize_moments.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.normalize_moments.md
new file mode 100644
index 0000000000..d7a6b9cab4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.normalize_moments.md
@@ -0,0 +1,20 @@
+### `tf.nn.normalize_moments(counts, mean_ss, variance_ss, shift, name=None)` {#normalize_moments}
+
+Calculate the mean and variance of based on the sufficient statistics.
+
+##### Args:
+
+
+*  <b>`counts`</b>: A `Tensor` containing a the total count of the data (one value).
+*  <b>`mean_ss`</b>: A `Tensor` containing the mean sufficient statistics: the (possibly
+    shifted) sum of the elements to average over.
+*  <b>`variance_ss`</b>: A `Tensor` containing the variance sufficient statistics: the
+    (possibly shifted) squared sum of the data to compute the variance over.
+*  <b>`shift`</b>: A `Tensor` containing the value by which the data is shifted for
+    numerical stability, or `None` if no shift was performed.
+*  <b>`name`</b>: Name used to scope the operations that compute the moments.
+
+##### Returns:
+
+  Two `Tensor` objects: `mean` and `variance`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.sampled_softmax_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.sampled_softmax_loss.md
deleted file mode 100644
index 6d22f67352..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.sampled_softmax_loss.md
+++ /dev/null
@@ -1,49 +0,0 @@
-### `tf.nn.sampled_softmax_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=True, partition_strategy='mod', name='sampled_softmax_loss')` {#sampled_softmax_loss}
-
-Computes and returns the sampled softmax training loss.
-
-This is a faster way to train a softmax classifier over a huge number of
-classes.
-
-This operation is for training only.  It is generally an underestimate of
-the full softmax loss.
-
-At inference time, you can compute full softmax probabilities with the
-expression `tf.nn.softmax(tf.matmul(inputs, tf.transpose(weights)) + biases)`.
-
-See our [Candidate Sampling Algorithms Reference]
-(../../extras/candidate_sampling.pdf)
-
-Also see Section 3 of [Jean et al., 2014](http://arxiv.org/abs/1412.2007)
-([pdf](http://arxiv.org/pdf/1412.2007.pdf)) for the math.
-
-##### Args:
-
-
-*  <b>`weights`</b>: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
-      objects whose concatenation along dimension 0 has shape
-      [num_classes, dim].  The (possibly-sharded) class embeddings.
-*  <b>`biases`</b>: A `Tensor` of shape `[num_classes]`.  The class biases.
-*  <b>`inputs`</b>: A `Tensor` of shape `[batch_size, dim]`.  The forward
-      activations of the input network.
-*  <b>`labels`</b>: A `Tensor` of type `int64` and shape `[batch_size,
-      num_true]`. The target classes.  Note that this format differs from
-      the `labels` argument of `nn.softmax_cross_entropy_with_logits`.
-*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
-*  <b>`num_classes`</b>: An `int`. The number of possible classes.
-*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
-*  <b>`sampled_values`</b>: a tuple of (`sampled_candidates`, `true_expected_count`,
-      `sampled_expected_count`) returned by a `*_candidate_sampler` function.
-      (if None, we default to `log_uniform_candidate_sampler`)
-*  <b>`remove_accidental_hits`</b>: A `bool`.  whether to remove "accidental hits"
-      where a sampled class equals one of the target classes.  Default is
-      True.
-*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy, relevant
-      if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
-      Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `batch_size` 1-D tensor of per-example sampled softmax losses.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.separable_conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.separable_conv2d.md
new file mode 100644
index 0000000000..f4be03303f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.nn.separable_conv2d.md
@@ -0,0 +1,40 @@
+### `tf.nn.separable_conv2d(input, depthwise_filter, pointwise_filter, strides, padding, name=None)` {#separable_conv2d}
+
+2-D convolution with separable filters.
+
+Performs a depthwise convolution that acts separately on channels followed by
+a pointwise convolution that mixes channels.  Note that this is separability
+between dimensions `[1, 2]` and `3`, not spatial separability between
+dimensions `1` and `2`.
+
+In detail,
+
+    output[b, i, j, k] = sum_{di, dj, q, r]
+        input[b, strides[1] * i + di, strides[2] * j + dj, q] *
+        depthwise_filter[di, dj, q, r] *
+        pointwise_filter[0, 0, q * channel_multiplier + r, k]
+
+`strides` controls the strides for the depthwise convolution only, since
+the pointwise convolution has implicit strides of `[1, 1, 1, 1]`.  Must have
+`strides[0] = strides[3] = 1`.  For the most common case of the same
+horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
+
+##### Args:
+
+
+*  <b>`input`</b>: 4-D `Tensor` with shape `[batch, in_height, in_width, in_channels]`.
+*  <b>`depthwise_filter`</b>: 4-D `Tensor` with shape
+    `[filter_height, filter_width, in_channels, channel_multiplier]`.
+    Contains `in_channels` convolutional filters of depth 1.
+*  <b>`pointwise_filter`</b>: 4-D `Tensor` with shape
+    `[1, 1, channel_multiplier * in_channels, out_channels]`.  Pointwise
+    filter to mix channels after `depthwise_filter` has convolved spatially.
+*  <b>`strides`</b>: 1-D of size 4.  The strides for the depthwise convolution for
+    each dimension of `input`.
+*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`.  The padding algorithm.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  A 4-D `Tensor` of shape `[batch, out_height, out_width, out_channels]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.ones_like.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.ones_like.md
deleted file mode 100644
index 2c9b04ceca..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.ones_like.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.ones_like(tensor, dtype=None, name=None)` {#ones_like}
-
-Creates a tensor with all elements set to 1.
-
-Given a single tensor (`tensor`), this operation returns a tensor of the same
-type and shape as `tensor` with all elements set to 1. Optionally, you can
-specify a new type (`dtype`) for the returned tensor.
-
-For example:
-
-```python
-# 'tensor' is [[1, 2, 3], [4, 5, 6]]
-tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]
-```
-
-##### Args:
-
-
-*  <b>`tensor`</b>: A `Tensor`.
-*  <b>`dtype`</b>: A type for the returned `Tensor`. Must be `float32`, `float64`,
-  `int8`, `int16`, `int32`, `int64`, `uint8`, or `complex64`.
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` with all elements set to 1.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.pad.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.pad.md
deleted file mode 100644
index 7fbf7442c7..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.pad.md
+++ /dev/null
@@ -1,57 +0,0 @@
-### `tf.pad(tensor, paddings, mode='CONSTANT', name=None)` {#pad}
-
-Pads a tensor.
-
-This operation pads a `tensor` according to the `paddings` you specify.
-`paddings` is an integer tensor with shape `[n, 2]`, where n is the rank of
-`tensor`. For each dimension D of `input`, `paddings[D, 0]` indicates how
-many values to add before the contents of `tensor` in that dimension, and
-`paddings[D, 1]` indicates how many values to add after the contents of
-`tensor` in that dimension. If `mode` is "REFLECT" then both `paddings[D, 0]`
-and `paddings[D, 1]` must be no greater than `tensor.dim_size(D) - 1`. If
-`mode` is "SYMMETRIC" then both `paddings[D, 0]` and `paddings[D, 1]` must be
-no greater than `tensor.dim_size(D)`.
-
-The padded size of each dimension D of the output is:
-
-`paddings[D, 0] + tensor.dim_size(D) + paddings[D, 1]`
-
-For example:
-
-```python
-# 't' is [[1, 2, 3], [4, 5, 6]].
-# 'paddings' is [[1, 1,], [2, 2]].
-# rank of 't' is 2.
-pad(t, paddings, "CONSTANT") ==> [[0, 0, 0, 0, 0, 0, 0],
-                                  [0, 0, 1, 2, 3, 0, 0],
-                                  [0, 0, 4, 5, 6, 0, 0],
-                                  [0, 0, 0, 0, 0, 0, 0]]
-
-pad(t, paddings, "REFLECT") ==> [[6, 5, 4, 5, 6, 5, 4],
-                                 [3, 2, 1, 2, 3, 2, 1],
-                                 [6, 5, 4, 5, 6, 5, 4],
-                                 [3, 2, 1, 2, 3, 2, 1]]
-
-pad(t, paddings, "SYMMETRIC") ==> [[2, 1, 1, 2, 3, 3, 2],
-                                   [2, 1, 1, 2, 3, 3, 2],
-                                   [5, 4, 4, 5, 6, 6, 5],
-                                   [5, 4, 4, 5, 6, 6, 5]]
-```
-
-##### Args:
-
-
-*  <b>`tensor`</b>: A `Tensor`.
-*  <b>`paddings`</b>: A `Tensor` of type `int32`.
-*  <b>`mode`</b>: One of "CONSTANT", "REFLECT", or "SYMMETRIC".
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `tensor`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC".
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.parse_single_example.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.parse_single_example.md
new file mode 100644
index 0000000000..e0bce09137
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.parse_single_example.md
@@ -0,0 +1,35 @@
+### `tf.parse_single_example(serialized, features, name=None, example_names=None)` {#parse_single_example}
+
+Parses a single `Example` proto.
+
+Similar to `parse_example`, except:
+
+For dense tensors, the returned `Tensor` is identical to the output of
+`parse_example`, except there is no batch dimension, the output shape is the
+same as the shape given in `dense_shape`.
+
+For `SparseTensor`s, the first (batch) column of the indices matrix is removed
+(the indices matrix is a column vector), the values vector is unchanged, and
+the first (`batch_size`) entry of the shape vector is removed (it is now a
+single element vector).
+
+##### Args:
+
+
+*  <b>`serialized`</b>: A scalar string Tensor, a single serialized Example.
+    See `_parse_single_example_raw` documentation for more details.
+*  <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
+    `VarLenFeature` values.
+*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`example_names`</b>: (Optional) A scalar string Tensor, the associated name.
+    See `_parse_single_example_raw` documentation for more details.
+
+##### Returns:
+
+  A `dict` mapping feature keys to `Tensor` and `SparseTensor` values.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if any feature is invalid.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.placeholder_with_default.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.placeholder_with_default.md
deleted file mode 100644
index 2719b876f1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.placeholder_with_default.md
+++ /dev/null
@@ -1,17 +0,0 @@
-### `tf.placeholder_with_default(input, shape, name=None)` {#placeholder_with_default}
-
-A placeholder op that passes though `input` when its output is not fed.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. The default value to produce when `output` is not fed.
-*  <b>`shape`</b>: A `tf.TensorShape` or list of `ints`.
-    The (possibly partial) shape of the tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-  A placeholder tensor that defaults to `input` if it is not fed.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.read_file.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.read_file.md
deleted file mode 100644
index 3c0ad3652a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.read_file.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.read_file(filename, name=None)` {#read_file}
-
-Reads and outputs the entire contents of the input filename.
-
-##### Args:
-
-
-*  <b>`filename`</b>: A `Tensor` of type `string`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `string`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.reduce_mean.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.reduce_mean.md
new file mode 100644
index 0000000000..af446b6c53
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.reduce_mean.md
@@ -0,0 +1,35 @@
+### `tf.reduce_mean(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_mean}
+
+Computes the mean of elements across dimensions of a tensor.
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+For example:
+
+```python
+# 'x' is [[1., 1.]
+#         [2., 2.]]
+tf.reduce_mean(x) ==> 1.5
+tf.reduce_mean(x, 0) ==> [1.5, 1.5]
+tf.reduce_mean(x, 1) ==> [1.,  2.]
+```
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.scalar_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.scalar_summary.md
new file mode 100644
index 0000000000..1e8c3479e4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.scalar_summary.md
@@ -0,0 +1,21 @@
+### `tf.scalar_summary(tags, values, collections=None, name=None)` {#scalar_summary}
+
+Outputs a `Summary` protocol buffer with scalar values.
+
+The input `tags` and `values` must have the same shape.  The generated
+summary has a summary value for each tag-value pair in `tags` and `values`.
+
+##### Args:
+
+
+*  <b>`tags`</b>: A `string` `Tensor`.  Tags for the summaries.
+*  <b>`values`</b>: A real numeric Tensor.  Values for the summaries.
+*  <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
+    added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A scalar `Tensor` of type `string`. The serialized `Summary` protocol
+  buffer.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.scatter_add.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.scatter_add.md
new file mode 100644
index 0000000000..a8f8b7a9b0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.scatter_add.md
@@ -0,0 +1,46 @@
+### `tf.scatter_add(ref, indices, updates, use_locking=None, name=None)` {#scatter_add}
+
+Adds sparse updates to a variable reference.
+
+This operation computes
+
+    # Scalar indices
+    ref[indices, ...] += updates[...]
+
+    # Vector indices (for each i)
+    ref[indices[i], ...] += updates[i, ...]
+
+    # High rank indices (for each i, ..., j)
+    ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
+
+This operation outputs `ref` after the update is done.
+This makes it easier to chain operations that need to use the reset value.
+
+Duplicate entries are handled correctly: if multiple `indices` reference
+the same location, their contributions add.
+
+Requires `updates.shape = indices.shape + ref.shape[1:]`.
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/ScatterAdd.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`ref`</b>: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+    Should be from a `Variable` node.
+*  <b>`indices`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    A tensor of indices into the first dimension of `ref`.
+*  <b>`updates`</b>: A `Tensor`. Must have the same type as `ref`.
+    A tensor of updated values to add to `ref`.
+*  <b>`use_locking`</b>: An optional `bool`. Defaults to `False`.
+    If True, the addition will be protected by a lock;
+    otherwise the behavior is undefined, but may exhibit less contention.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  Same as `ref`.  Returned as a convenience for operations that want
+  to use the updated values after the update is done.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.scatter_sub.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.scatter_sub.md
deleted file mode 100644
index 8f1afc42f6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.scatter_sub.md
+++ /dev/null
@@ -1,44 +0,0 @@
-### `tf.scatter_sub(ref, indices, updates, use_locking=None, name=None)` {#scatter_sub}
-
-Subtracts sparse updates to a variable reference.
-
-    # Scalar indices
-    ref[indices, ...] -= updates[...]
-
-    # Vector indices (for each i)
-    ref[indices[i], ...] -= updates[i, ...]
-
-    # High rank indices (for each i, ..., j)
-    ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
-
-This operation outputs `ref` after the update is done.
-This makes it easier to chain operations that need to use the reset value.
-
-Duplicate entries are handled correctly: if multiple `indices` reference
-the same location, their (negated) contributions add.
-
-Requires `updates.shape = indices.shape + ref.shape[1:]`.
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/ScatterSub.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`ref`</b>: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-    Should be from a `Variable` node.
-*  <b>`indices`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    A tensor of indices into the first dimension of `ref`.
-*  <b>`updates`</b>: A `Tensor`. Must have the same type as `ref`.
-    A tensor of updated values to subtract from `ref`.
-*  <b>`use_locking`</b>: An optional `bool`. Defaults to `False`.
-    If True, the subtraction will be protected by a lock;
-    otherwise the behavior is undefined, but may exhibit less contention.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  Same as `ref`.  Returned as a convenience for operations that want
-  to use the updated values after the update is done.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.segment_max.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.segment_max.md
new file mode 100644
index 0000000000..c9d7a28900
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.segment_max.md
@@ -0,0 +1,30 @@
+### `tf.segment_max(data, segment_ids, name=None)` {#segment_max}
+
+Computes the maximum along segments of a tensor.
+
+Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation)
+for an explanation of segments.
+
+Computes a tensor such that
+\\(output_i = \max_j(data_j)\\) where `max` is over `j` such
+that `segment_ids[j] == i`.
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/SegmentMax.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    A 1-D tensor whose rank is equal to the rank of `data`'s
+    first dimension.  Values should be sorted and can be repeated.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `data`.
+  Has same shape as data, except for dimension 0 which
+  has size `k`, the number of segments.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.segment_mean.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.segment_mean.md
new file mode 100644
index 0000000000..5d901859a9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.segment_mean.md
@@ -0,0 +1,32 @@
+### `tf.segment_mean(data, segment_ids, name=None)` {#segment_mean}
+
+Computes the mean along segments of a tensor.
+
+Read [the section on
+Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
+of segments.
+
+Computes a tensor such that
+\\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is
+over `j` such that `segment_ids[j] == i` and `N` is the total number of
+values summed.
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/SegmentMean.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    A 1-D tensor whose rank is equal to the rank of `data`'s
+    first dimension.  Values should be sorted and can be repeated.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `data`.
+  Has same shape as data, except for dimension 0 which
+  has size `k`, the number of segments.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.segment_sum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.segment_sum.md
deleted file mode 100644
index eeffe1601a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.segment_sum.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.segment_sum(data, segment_ids, name=None)` {#segment_sum}
-
-Computes the sum along segments of a tensor.
-
-Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation)
-for an explanation of segments.
-
-Computes a tensor such that
-\\(output_i = \sum_j data_j\\) where sum is over `j` such
-that `segment_ids[j] == i`.
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/SegmentSum.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    A 1-D tensor whose rank is equal to the rank of `data`'s
-    first dimension.  Values should be sorted and can be repeated.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `data`.
-  Has same shape as data, except for dimension 0 which
-  has size `k`, the number of segments.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_add.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_add.md
deleted file mode 100644
index 4835ae70e5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_add.md
+++ /dev/null
@@ -1,55 +0,0 @@
-### `tf.sparse_add(a, b, thresh=0)` {#sparse_add}
-
-Adds two tensors, at least one of each is a `SparseTensor`.
-
-If one `SparseTensor` and one `Tensor` are passed in, returns a `Tensor`.  If
-both arguments are `SparseTensor`s, this returns a `SparseTensor`.  The order
-of arguments does not matter.  Use vanilla `tf.add()` for adding two dense
-`Tensor`s.
-
-The indices of any input `SparseTensor` are assumed ordered in standard
-lexicographic order.  If this is not the case, before this step run
-`SparseReorder` to restore index ordering.
-
-If both arguments are sparse, we perform "clipping" as follows.  By default,
-if two values sum to zero at some index, the output `SparseTensor` would still
-include that particular location in its index, storing a zero in the
-corresponding value slot.  To override this, callers can specify `thresh`,
-indicating that if the sum has a magnitude strictly smaller than `thresh`, its
-corresponding value and index would then not be included.  In particular,
-`thresh == 0.0` (default) means everything is kept and actual thresholding
-happens only for a positive value.
-
-For example, suppose the logical sum of two sparse operands is (densified):
-
-    [       2]
-    [.1     0]
-    [ 6   -.2]
-
-Then,
-
-    - thresh == 0 (the default): all 5 index/value pairs will be returned.
-    - thresh == 0.11: only .1 and 0  will vanish, and the remaining three
-        index/value pairs will be returned.
-    - thresh == 0.21: .1, 0, and -.2 will vanish.
-
-##### Args:
-
-
-*  <b>`a`</b>: The first operand; `SparseTensor` or `Tensor`.
-*  <b>`b`</b>: The second operand; `SparseTensor` or `Tensor`.  At least one operand
-    must be sparse.
-*  <b>`thresh`</b>: A 0-D `Tensor`.  The magnitude threshold that determines if an
-  output value/index pair takes space.  Its dtype should match that of the
-  values if they are real; if the latter are complex64/complex128, then the
-  dtype should be float32/float64, correspondingly.
-
-##### Returns:
-
-  A `SparseTensor` or a `Tensor`, representing the sum.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If both `a` and `b` are `Tensor`s.  Use `tf.add()` instead.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_concat.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_concat.md
deleted file mode 100644
index 8d05472e34..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_concat.md
+++ /dev/null
@@ -1,100 +0,0 @@
-### `tf.sparse_concat(concat_dim, sp_inputs, name=None, expand_nonconcat_dim=False)` {#sparse_concat}
-
-Concatenates a list of `SparseTensor` along the specified dimension.
-
-Concatenation is with respect to the dense versions of each sparse input.
-It is assumed that each inputs is a `SparseTensor` whose elements are ordered
-along increasing dimension number.
-
-If expand_nonconcat_dim is False, all inputs' shapes must match, except for
-the concat dimension. If expand_nonconcat_dim is True, then inputs' shapes are
-allowd to vary among all inputs.
-
-The `indices`, `values`, and `shapes` lists must have the same length.
-
-If expand_nonconcat_dim is False, then the output shape is identical to the
-inputs', except along the concat dimension, where it is the sum of the inputs'
-sizes along that dimension.
-
-If expand_nonconcat_dim is True, then the output shape along the non-concat
-dimensions will be expand to be the largest among all inputs, and it is the
-sum of the inputs sizes along the concat dimension.
-
-The output elements will be resorted to preserve the sort order along
-increasing dimension number.
-
-This op runs in `O(M log M)` time, where `M` is the total number of non-empty
-values across all inputs. This is due to the need for an internal sort in
-order to concatenate efficiently across an arbitrary dimension.
-
-For example, if `concat_dim = 1` and the inputs are
-
-    sp_inputs[0]: shape = [2, 3]
-    [0, 2]: "a"
-    [1, 0]: "b"
-    [1, 1]: "c"
-
-    sp_inputs[1]: shape = [2, 4]
-    [0, 1]: "d"
-    [0, 2]: "e"
-
-then the output will be
-
-    shape = [2, 7]
-    [0, 2]: "a"
-    [0, 4]: "d"
-    [0, 5]: "e"
-    [1, 0]: "b"
-    [1, 1]: "c"
-
-Graphically this is equivalent to doing
-
-    [    a] concat [  d e  ] = [    a   d e  ]
-    [b c  ]        [       ]   [b c          ]
-
-Another example, if 'concat_dim = 1' and the inputs are
-
-    sp_inputs[0]: shape = [3, 3]
-    [0, 2]: "a"
-    [1, 0]: "b"
-    [2, 1]: "c"
-
-    sp_inputs[1]: shape = [2, 4]
-    [0, 1]: "d"
-    [0, 2]: "e"
-
-if expand_nonconcat_dim = False, this will result in an error. But if
-expand_nonconcat_dim = True, this will result in:
-
-    shape = [3, 7]
-    [0, 2]: "a"
-    [0, 4]: "d"
-    [0, 5]: "e"
-    [1, 0]: "b"
-    [2, 1]: "c"
-
-Graphically this is equivalent to doing
-
-    [    a] concat [  d e  ] = [    a   d e  ]
-    [b    ]        [       ]   [b            ]
-    [  c  ]                    [  c          ]
-
-
-##### Args:
-
-
-*  <b>`concat_dim`</b>: Dimension to concatenate along.
-*  <b>`sp_inputs`</b>: List of `SparseTensor` to concatenate.
-*  <b>`name`</b>: A name prefix for the returned tensors (optional).
-*  <b>`expand_nonconcat_dim`</b>: Whether to allow the expansion in the non-concat
-    dimensions. Defaulted to False.
-
-##### Returns:
-
-  A `SparseTensor` with the concatenated output.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `sp_inputs` is not a list of `SparseTensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_fill_empty_rows.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_fill_empty_rows.md
deleted file mode 100644
index 3ea1697f3d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_fill_empty_rows.md
+++ /dev/null
@@ -1,54 +0,0 @@
-### `tf.sparse_fill_empty_rows(sp_input, default_value, name=None)` {#sparse_fill_empty_rows}
-
-Fills empty rows in the input 2-D `SparseTensor` with a default value.
-
-This op adds entries with the specified `default_value` at index
-`[row, 0]` for any row in the input that does not already have a value.
-
-For example, suppose `sp_input` has shape `[5, 6]` and non-empty values:
-
-    [0, 1]: a
-    [0, 3]: b
-    [2, 0]: c
-    [3, 1]: d
-
-Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values:
-
-    [0, 1]: a
-    [0, 3]: b
-    [1, 0]: default_value
-    [2, 0]: c
-    [3, 1]: d
-    [4, 0]: default_value
-
-Note that the input may have empty columns at the end, with no effect on
-this op.
-
-The output `SparseTensor` will be in row-major order and will have the
-same shape as the input.
-
-This op also returns an indicator vector such that
-
-    empty_row_indicator[i] = True iff row i was an empty row.
-
-##### Args:
-
-
-*  <b>`sp_input`</b>: A `SparseTensor` with shape `[N, M]`.
-*  <b>`default_value`</b>: The value to fill for empty rows, with the same type as
-    `sp_input.`
-*  <b>`name`</b>: A name prefix for the returned tensors (optional)
-
-##### Returns:
-
-
-*  <b>`sp_ordered_output`</b>: A `SparseTensor` with shape `[N, M]`, and with all empty
-    rows filled in with `default_value`.
-*  <b>`empty_row_indicator`</b>: A bool vector of length `N` indicating whether each
-    input row was empty.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_merge.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_merge.md
new file mode 100644
index 0000000000..38742123d6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_merge.md
@@ -0,0 +1,73 @@
+### `tf.sparse_merge(sp_ids, sp_values, vocab_size, name=None)` {#sparse_merge}
+
+Combines a batch of feature ids and values into a single `SparseTensor`.
+
+The most common use case for this function occurs when feature ids and
+their corresponding values are stored in `Example` protos on disk.
+`parse_example` will return a batch of ids and a batch of values, and this
+function joins them into a single logical `SparseTensor` for use in
+functions such as `sparse_tensor_dense_matmul`, `sparse_to_dense`, etc.
+
+The `SparseTensor` returned by this function has the following properties:
+
+  - `indices` is equivalent to `sp_ids.indices` with the last
+    dimension discarded and replaced with `sp_ids.values`.
+  - `values` is simply `sp_values.values`.
+  - If `sp_ids.shape = [D0, D1, ..., Dn, K]`, then
+    `output.shape = [D0, D1, ..., Dn, vocab_size]`.
+
+For example, consider the following feature vectors:
+
+  vector1 = [-3, 0, 0, 0, 0, 0]
+  vector2 = [ 0, 1, 0, 4, 1, 0]
+  vector3 = [ 5, 0, 0, 9, 0, 0]
+
+These might be stored sparsely in the following Example protos by storing
+only the feature ids (column number if the vectors are treated as a matrix)
+of the non-zero elements and the corresponding values:
+
+  examples = [Example(features={
+                  "ids": Feature(int64_list=Int64List(value=[0])),
+                  "values": Feature(float_list=FloatList(value=[-3]))}),
+              Example(features={
+                  "ids": Feature(int64_list=Int64List(value=[1, 4, 3])),
+                  "values": Feature(float_list=FloatList(value=[1, 1, 4]))}),
+              Example(features={
+                  "ids": Feature(int64_list=Int64List(value=[0, 3])),
+                  "values": Feature(float_list=FloatList(value=[5, 9]))})]
+
+The result of calling parse_example on these examples will produce a
+dictionary with entries for "ids" and "values". Passing those two objects
+to this function along with vocab_size=6, will produce a `SparseTensor` that
+sparsely represents all three instances. Namely, the `indices` property will
+contain the coordinates of the non-zero entries in the feature matrix (the
+first dimension is the row number in the matrix, i.e., the index within the
+batch, and the second dimension is the column number, i.e., the feature id);
+`values` will contain the actual values. `shape` will be the shape of the
+original matrix, i.e., (3, 6). For our example above, the output will be
+equal to:
+
+  SparseTensor(indices=[[0, 0], [1, 1], [1, 3], [1, 4], [2, 0], [2, 3]],
+               values=[-3, 1, 4, 1, 5, 9],
+               shape=[3, 6])
+
+##### Args:
+
+
+*  <b>`sp_ids`</b>: A `SparseTensor` with `values` property of type `int32`
+    or `int64`.
+*  <b>`sp_values`</b>: A`SparseTensor` of any type.
+*  <b>`vocab_size`</b>: A scalar `int64` Tensor (or Python int) containing the new size
+    of the last dimension, `all(0 <= sp_ids.values < vocab_size)`.
+*  <b>`name`</b>: A name prefix for the returned tensors (optional)
+
+##### Returns:
+
+  A `SparseTensor` compactly representing a batch of feature ids and values,
+  useful for passing to functions that expect such a `SparseTensor`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sp_ids` or `sp_values` are not a `SparseTensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_placeholder.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_placeholder.md
deleted file mode 100644
index def6c8329d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_placeholder.md
+++ /dev/null
@@ -1,43 +0,0 @@
-### `tf.sparse_placeholder(dtype, shape=None, name=None)` {#sparse_placeholder}
-
-Inserts a placeholder for a sparse tensor that will be always fed.
-
-**Important**: This sparse tensor will produce an error if evaluated.
-Its value must be fed using the `feed_dict` optional argument to
-`Session.run()`, `Tensor.eval()`, or `Operation.run()`.
-
-For example:
-
-```python
-x = tf.sparse_placeholder(tf.float32)
-y = tf.sparse_reduce_sum(x)
-
-with tf.Session() as sess:
-  print(sess.run(y))  # ERROR: will fail because x was not fed.
-
-  indices = np.array([[3, 2, 0], [4, 5, 1]], dtype=np.int64)
-  values = np.array([1.0, 2.0], dtype=np.float32)
-  shape = np.array([7, 9, 2], dtype=np.int64)
-  print(sess.run(y, feed_dict={
-    x: tf.SparseTensorValue(indices, values, shape)}))  # Will succeed.
-  print(sess.run(y, feed_dict={
-    x: (indices, values, shape)}))  # Will succeed.
-
-  sp = tf.SparseTensor(indices=indices, values=values, shape=shape)
-  sp_value = sp.eval(session)
-  print(sess.run(y, feed_dict={x: sp_value}))  # Will succeed.
-```
-
-##### Args:
-
-
-*  <b>`dtype`</b>: The type of `values` elements in the tensor to be fed.
-*  <b>`shape`</b>: The shape of the tensor to be fed (optional). If the shape is not
-    specified, you can feed a sparse tensor of any shape.
-*  <b>`name`</b>: A name for prefixing the operations (optional).
-
-##### Returns:
-
-  A `SparseTensor` that may be used as a handle for feeding a value, but not
-  evaluated directly.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_reset_shape.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_reset_shape.md
deleted file mode 100644
index d0606cdc5d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_reset_shape.md
+++ /dev/null
@@ -1,60 +0,0 @@
-### `tf.sparse_reset_shape(sp_input, new_shape=None)` {#sparse_reset_shape}
-
-Resets the shape of a `SparseTensor` with indices and values unchanged.
-
-If `new_shape` is None, returns a copy of `sp_input` with its shape reset
-to the tight bounding box of `sp_input`.
-
-If `new_shape` is provided, then it must be larger or equal in all dimensions
-compared to the shape of `sp_input`. When this condition is met, the returned
-SparseTensor will have its shape reset to `new_shape` and its indices and
-values unchanged from that of `sp_input.`
-
-For example:
-
-  Consider a `sp_input` with shape [2, 3, 5]:
-
-    [0, 0, 1]: a
-    [0, 1, 0]: b
-    [0, 2, 2]: c
-    [1, 0, 3]: d
-
-  - It is an error to set `new_shape` as [3, 7] since this represents a
-    rank-2 tensor while `sp_input` is rank-3. This is either a ValueError
-    during graph construction (if both shapes are known) or an OpError during
-    run time.
-
-  - Setting `new_shape` as [2, 3, 6] will be fine as this shape is larger or
-    eqaul in every dimension compared to the original shape [2, 3, 5].
-
-  - On the other hand, setting new_shape as [2, 3, 4] is also an error: The
-    third dimension is smaller than the original shape [2, 3, 5] (and an
-    `InvalidArgumentError` will be raised).
-
-  - If `new_shape` is None, the returned SparseTensor will have a shape
-    [2, 3, 4], which is the tight bounding box of `sp_input`.
-
-##### Args:
-
-
-*  <b>`sp_input`</b>: The input `SparseTensor`.
-*  <b>`new_shape`</b>: None or a vector representing the new shape for the returned
-    `SpraseTensor`.
-
-##### Returns:
-
-  A `SparseTensor` indices and values unchanged from `input_sp`. Its shape is
-    `new_shape` if that is set. Otherwise it is  the tight bounding box of
-     `input_sp`
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
-*  <b>`ValueError`</b>: If `new_shape` represents a tensor with a different rank from
-    that of `sp_input` (if shapes are known when graph is constructed).
-*  <b>`OpError`</b>: 
-    - If `new_shape` has dimension sizes that are too small.
-    - If shapes are not known during graph construction time, and during run
-      time it is found out that the ranks do not match.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_segment_mean.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_segment_mean.md
new file mode 100644
index 0000000000..d95830b8a9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_segment_mean.md
@@ -0,0 +1,27 @@
+### `tf.sparse_segment_mean(data, indices, segment_ids, name=None)` {#sparse_segment_mean}
+
+Computes the mean along sparse segments of a tensor.
+
+Read [the section on
+Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
+of segments.
+
+Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first
+dimension, selecting a subset of dimension 0, specified by `indices`.
+
+##### Args:
+
+
+*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`indices`</b>: A `Tensor` of type `int32`.
+    A 1-D tensor. Has same rank as `segment_ids`.
+*  <b>`segment_ids`</b>: A `Tensor` of type `int32`.
+    A 1-D tensor. Values should be sorted and can be repeated.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `data`.
+  Has same shape as data, except for dimension 0 which
+  has size `k`, the number of segments.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_softmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_softmax.md
new file mode 100644
index 0000000000..cb54fd9452
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.sparse_softmax.md
@@ -0,0 +1,51 @@
+### `tf.sparse_softmax(sp_input, name=None)` {#sparse_softmax}
+
+Applies softmax to a batched N-D `SparseTensor`.
+
+The inputs represent an N-D SparseTensor  with logical shape `[..., B, C]`
+(where `N >= 2`), and with indices sorted in the canonical lexicographic
+order.
+
+This op is equivalent to applying the normal `tf.nn.softmax()` to each
+innermost logical submatrix with shape `[B, C]`, but with the catch that *the
+implicitly zero elements do not participate*.  Specifically, the algorithm is
+equivalent to:
+
+  (1) Applies `tf.nn.softmax()` to a densified view of each innermost
+      submatrix with shape `[B, C]`, along the size-C dimension;
+  (2) Masks out the original implicitly-zero locations;
+  (3) Renormalizes the remaining elements.
+
+Hence, the `SparseTensor` result has exactly the same non-zero indices and
+shape.
+
+Example:
+```python
+# First batch:
+# [?   e.]
+# [1.  ? ]
+# Second batch:
+# [e   ? ]
+# [e   e ]
+shape = [2, 2, 2]  # 3-D SparseTensor
+values = np.asarray([[[0., np.e], [1., 0.]], [[np.e, 0.], [np.e, np.e]]])
+indices = np.vstack(np.where(values)).astype(np.int64).T
+
+result = tf.sparse_softmax(tf.SparseTensor(indices, values, shape))
+# ...returning a 3-D SparseTensor, equivalent to:
+# [?   1.]     [1    ?]
+# [1.  ? ] and [.5  .5]
+# where ? means implicitly zero.
+```
+
+##### Args:
+
+
+*  <b>`sp_input`</b>: N-D `SparseTensor`, where `N >= 2`.
+*  <b>`name`</b>: optional name of the operation.
+
+##### Returns:
+
+
+*  <b>`output`</b>: N-D `SparseTensor` representing the results.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.split.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.split.md
new file mode 100644
index 0000000000..f6cc936328
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.split.md
@@ -0,0 +1,29 @@
+### `tf.split(split_dim, num_split, value, name='split')` {#split}
+
+Splits a tensor into `num_split` tensors along one dimension.
+
+Splits `value` along dimension `split_dim` into `num_split` smaller tensors.
+Requires that `num_split` evenly divide `value.shape[split_dim]`.
+
+For example:
+
+```python
+# 'value' is a tensor with shape [5, 30]
+# Split 'value' into 3 tensors along dimension 1
+split0, split1, split2 = tf.split(1, 3, value)
+tf.shape(split0) ==> [5, 10]
+```
+
+##### Args:
+
+
+*  <b>`split_dim`</b>: A 0-D `int32` `Tensor`. The dimension along which to split.
+    Must be in the range `[0, rank(value))`.
+*  <b>`num_split`</b>: A Python integer. The number of ways to split.
+*  <b>`value`</b>: The `Tensor` to split.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  `num_split` `Tensor` objects resulting from splitting `value`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.square.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.square.md
new file mode 100644
index 0000000000..649c763015
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.square.md
@@ -0,0 +1,16 @@
+### `tf.square(x, name=None)` {#square}
+
+Computes square of x element-wise.
+
+I.e., \\(y = x * x = x^2\\).
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.test.assert_equal_graph_def.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.test.assert_equal_graph_def.md
new file mode 100644
index 0000000000..653236cf9f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.test.assert_equal_graph_def.md
@@ -0,0 +1,20 @@
+### `tf.test.assert_equal_graph_def(actual, expected)` {#assert_equal_graph_def}
+
+Asserts that two `GraphDef`s are (mostly) the same.
+
+Compares two `GraphDef` protos for equality, ignoring versions and ordering of
+nodes, attrs, and control inputs.  Node names are used to match up nodes
+between the graphs, so the naming of nodes must be consistent.
+
+##### Args:
+
+
+*  <b>`actual`</b>: The `GraphDef` we have.
+*  <b>`expected`</b>: The `GraphDef` we expected.
+
+##### Raises:
+
+
+*  <b>`AssertionError`</b>: If the `GraphDef`s do not match.
+*  <b>`TypeError`</b>: If either argument is not a `GraphDef`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.to_bfloat16.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.to_bfloat16.md
deleted file mode 100644
index 3d55da1110..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.to_bfloat16.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.to_bfloat16(x, name='ToBFloat16')` {#to_bfloat16}
-
-Casts a tensor to type `bfloat16`.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` or `SparseTensor` with same shape as `x` with type `bfloat16`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `x` cannot be cast to the `bfloat16`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.to_int32.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.to_int32.md
deleted file mode 100644
index fcc9db61cc..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.to_int32.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.to_int32(x, name='ToInt32')` {#to_int32}
-
-Casts a tensor to type `int32`.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` or `SparseTensor` with same shape as `x` with type `int32`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `x` cannot be cast to the `int32`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.Coordinator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.Coordinator.md
new file mode 100644
index 0000000000..f51c0721ff
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.Coordinator.md
@@ -0,0 +1,223 @@
+A coordinator for threads.
+
+This class implements a simple mechanism to coordinate the termination of a
+set of threads.
+
+#### Usage:
+
+```python
+# Create a coordinator.
+coord = Coordinator()
+# Start a number of threads, passing the coordinator to each of them.
+...start thread 1...(coord, ...)
+...start thread N...(coord, ...)
+# Wait for all the threads to terminate.
+coord.join(threads)
+```
+
+Any of the threads can call `coord.request_stop()` to ask for all the threads
+to stop.  To cooperate with the requests, each thread must check for
+`coord.should_stop()` on a regular basis.  `coord.should_stop()` returns
+`True` as soon as `coord.request_stop()` has been called.
+
+A typical thread running with a coordinator will do something like:
+
+```python
+while not coord.should_stop():
+  ...do some work...
+```
+
+#### Exception handling:
+
+A thread can report an exception to the coordinator as part of the
+`should_stop()` call.  The exception will be re-raised from the
+`coord.join()` call.
+
+Thread code:
+
+```python
+try:
+  while not coord.should_stop():
+    ...do some work...
+except Exception as e:
+  coord.request_stop(e)
+```
+
+Main code:
+
+```python
+try:
+  ...
+  coord = Coordinator()
+  # Start a number of threads, passing the coordinator to each of them.
+  ...start thread 1...(coord, ...)
+  ...start thread N...(coord, ...)
+  # Wait for all the threads to terminate.
+  coord.join(threads)
+except Exception as e:
+  ...exception that was passed to coord.request_stop()
+```
+
+To simplify the thread implementation, the Coordinator provides a
+context handler `stop_on_exception()` that automatically requests a stop if
+an exception is raised.  Using the context handler the thread code above
+can be written as:
+
+```python
+with coord.stop_on_exception():
+  while not coord.should_stop():
+    ...do some work...
+```
+
+#### Grace period for stopping:
+
+After a thread has called `coord.request_stop()` the other threads have a
+fixed time to stop, this is called the 'stop grace period' and defaults to 2
+minutes.  If any of the threads is still alive after the grace period expires
+`coord.join()` raises a RuntimeException reporting the laggards.
+
+```python
+try:
+  ...
+  coord = Coordinator()
+  # Start a number of threads, passing the coordinator to each of them.
+  ...start thread 1...(coord, ...)
+  ...start thread N...(coord, ...)
+  # Wait for all the threads to terminate, give them 10s grace period
+  coord.join(threads, stop_grace_period_secs=10)
+except RuntimeException:
+  ...one of the threads took more than 10s to stop after request_stop()
+  ...was called.
+except Exception:
+  ...exception that was passed to coord.request_stop()
+```
+- - -
+
+#### `tf.train.Coordinator.__init__()` {#Coordinator.__init__}
+
+Create a new Coordinator.
+
+
+- - -
+
+#### `tf.train.Coordinator.clear_stop()` {#Coordinator.clear_stop}
+
+Clears the stop flag.
+
+After this is called, calls to `should_stop()` will return `False`.
+
+
+- - -
+
+#### `tf.train.Coordinator.join(threads, stop_grace_period_secs=120)` {#Coordinator.join}
+
+Wait for threads to terminate.
+
+Blocks until all `threads` have terminated or `request_stop()` is called.
+
+After the threads stop, if an `exc_info` was passed to `request_stop`, that
+exception is re-raised.
+
+Grace period handling: When `request_stop()` is called, threads are given
+'stop_grace_period_secs' seconds to terminate.  If any of them is still
+alive after that period expires, a `RuntimeError` is raised.  Note that if
+an `exc_info` was passed to `request_stop()` then it is raised instead of
+that `RuntimeError`.
+
+##### Args:
+
+
+*  <b>`threads`</b>: List of `threading.Threads`. The started threads to join.
+*  <b>`stop_grace_period_secs`</b>: Number of seconds given to threads to stop after
+    `request_stop()` has been called.
+
+##### Raises:
+
+
+*  <b>`RuntimeError`</b>: If any thread is still alive after `request_stop()`
+    is called and the grace period expires.
+
+
+- - -
+
+#### `tf.train.Coordinator.request_stop(ex=None)` {#Coordinator.request_stop}
+
+Request that the threads stop.
+
+After this is called, calls to `should_stop()` will return `True`.
+
+Note: If an exception is being passed in, in must be in the context of
+handling the exception (i.e. `try: ... except Exception as ex: ...`) and not
+a newly created one.
+
+##### Args:
+
+
+*  <b>`ex`</b>: Optional `Exception`, or Python `exc_info` tuple as returned by
+    `sys.exc_info()`.  If this is the first call to `request_stop()` the
+    corresponding exception is recorded and re-raised from `join()`.
+
+
+- - -
+
+#### `tf.train.Coordinator.should_stop()` {#Coordinator.should_stop}
+
+Check if stop was requested.
+
+##### Returns:
+
+  True if a stop was requested.
+
+
+- - -
+
+#### `tf.train.Coordinator.stop_on_exception()` {#Coordinator.stop_on_exception}
+
+Context manager to request stop when an Exception is raised.
+
+Code that uses a coordinator must catch exceptions and pass
+them to the `request_stop()` method to stop the other threads
+managed by the coordinator.
+
+This context handler simplifies the exception handling.
+Use it as follows:
+
+```python
+with coord.stop_on_exception():
+  # Any exception raised in the body of the with
+  # clause is reported to the coordinator before terminating
+  # the execution of the body.
+  ...body...
+```
+
+This is completely equivalent to the slightly longer code:
+
+```python
+try:
+  ...body...
+exception Exception as ex:
+  coord.request_stop(ex)
+```
+
+##### Yields:
+
+  nothing.
+
+
+- - -
+
+#### `tf.train.Coordinator.wait_for_stop(timeout=None)` {#Coordinator.wait_for_stop}
+
+Wait till the Coordinator is told to stop.
+
+##### Args:
+
+
+*  <b>`timeout`</b>: Float.  Sleep for up to that many seconds waiting for
+    should_stop() to become True.
+
+##### Returns:
+
+  True if the Coordinator is told stop, False if the timeout expired.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.Saver.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.Saver.md
deleted file mode 100644
index 8bf255040e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.Saver.md
+++ /dev/null
@@ -1,315 +0,0 @@
-Saves and restores variables.
-
-See [Variables](../../how_tos/variables/index.md)
-for an overview of variables, saving and restoring.
-
-The `Saver` class adds ops to save and restore variables to and from
-*checkpoints*.  It also provides convenience methods to run these ops.
-
-Checkpoints are binary files in a proprietary format which map variable names
-to tensor values.  The best way to examine the contents of a checkpoint is to
-load it using a `Saver`.
-
-Savers can automatically number checkpoint filenames with a provided counter.
-This lets you keep multiple checkpoints at different steps while training a
-model.  For example you can number the checkpoint filenames with the training
-step number.  To avoid filling up disks, savers manage checkpoint files
-automatically. For example, they can keep only the N most recent files, or
-one checkpoint for every N hours of training.
-
-You number checkpoint filenames by passing a value to the optional
-`global_step` argument to `save()`:
-
-```python
-saver.save(sess, 'my-model', global_step=0) ==> filename: 'my-model-0'
-...
-saver.save(sess, 'my-model', global_step=1000) ==> filename: 'my-model-1000'
-```
-
-Additionally, optional arguments to the `Saver()` constructor let you control
-the proliferation of checkpoint files on disk:
-
-* `max_to_keep` indicates the maximum number of recent checkpoint files to
-  keep.  As new files are created, older files are deleted.  If None or 0,
-  all checkpoint files are kept.  Defaults to 5 (that is, the 5 most recent
-  checkpoint files are kept.)
-
-* `keep_checkpoint_every_n_hours`: In addition to keeping the most recent
-  `max_to_keep` checkpoint files, you might want to keep one checkpoint file
-  for every N hours of training.  This can be useful if you want to later
-  analyze how a model progressed during a long training session.  For
-  example, passing `keep_checkpoint_every_n_hours=2` ensures that you keep
-  one checkpoint file for every 2 hours of training.  The default value of
-  10,000 hours effectively disables the feature.
-
-Note that you still have to call the `save()` method to save the model.
-Passing these arguments to the constructor will not save variables
-automatically for you.
-
-A training program that saves regularly looks like:
-
-```python
-...
-# Create a saver.
-saver = tf.train.Saver(...variables...)
-# Launch the graph and train, saving the model every 1,000 steps.
-sess = tf.Session()
-for step in xrange(1000000):
-    sess.run(..training_op..)
-    if step % 1000 == 0:
-        # Append the step number to the checkpoint name:
-        saver.save(sess, 'my-model', global_step=step)
-```
-
-In addition to checkpoint files, savers keep a protocol buffer on disk with
-the list of recent checkpoints. This is used to manage numbered checkpoint
-files and by `latest_checkpoint()`, which makes it easy to discover the path
-to the most recent checkpoint. That protocol buffer is stored in a file named
-'checkpoint' next to the checkpoint files.
-
-If you create several savers, you can specify a different filename for the
-protocol buffer file in the call to `save()`.
-
-- - -
-
-#### `tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None)` {#Saver.__init__}
-
-Creates a `Saver`.
-
-The constructor adds ops to save and restore variables.
-
-`var_list` specifies the variables that will be saved and restored. It can
-be passed as a `dict` or a list:
-
-* A `dict` of names to variables: The keys are the names that will be
-  used to save or restore the variables in the checkpoint files.
-* A list of variables: The variables will be keyed with their op name in
-  the checkpoint files.
-
-For example:
-
-```python
-v1 = tf.Variable(..., name='v1')
-v2 = tf.Variable(..., name='v2')
-
-# Pass the variables as a dict:
-saver = tf.train.Saver({'v1': v1, 'v2': v2})
-
-# Or pass them as a list.
-saver = tf.train.Saver([v1, v2])
-# Passing a list is equivalent to passing a dict with the variable op names
-# as keys:
-saver = tf.train.Saver({v.op.name: v for v in [v1, v2]})
-```
-
-The optional `reshape` argument, if `True`, allows restoring a variable from
-a save file where the variable had a different shape, but the same number
-of elements and type.  This is useful if you have reshaped a variable and
-want to reload it from an older checkpoint.
-
-The optional `sharded` argument, if `True`, instructs the saver to shard
-checkpoints per device.
-
-##### Args:
-
-
-*  <b>`var_list`</b>: A list of `Variable` objects or a dictionary mapping names to
-    variables.  If `None`, defaults to the list of all variables.
-*  <b>`reshape`</b>: If `True`, allows restoring parameters from a checkpoint
-    where the variables have a different shape.
-*  <b>`sharded`</b>: If `True`, shard the checkpoints, one per device.
-*  <b>`max_to_keep`</b>: Maximum number of recent checkpoints to keep.
-    Defaults to 5.
-*  <b>`keep_checkpoint_every_n_hours`</b>: How often to keep checkpoints.
-    Defaults to 10,000 hours.
-*  <b>`name`</b>: String.  Optional name to use as a prefix when adding operations.
-*  <b>`restore_sequentially`</b>: A `Bool`, which if true, causes restore of different
-    variables to happen sequentially within each device.  This can lower
-    memory usage when restoring very large models.
-*  <b>`saver_def`</b>: Optional `SaverDef` proto to use instead of running the
-    builder. This is only useful for specialty code that wants to recreate
-    a `Saver` object for a previously built `Graph` that had a `Saver`.
-    The `saver_def` proto should be the one returned by the
-    `as_saver_def()` call of the `Saver` that was created for that `Graph`.
-*  <b>`builder`</b>: Optional `SaverBuilder` to use if a `saver_def` was not provided.
-    Defaults to `BaseSaverBuilder()`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `var_list` is invalid.
-*  <b>`ValueError`</b>: If any of the keys or values in `var_list` are not unique.
-
-
-- - -
-
-#### `tf.train.Saver.save(sess, save_path, global_step=None, latest_filename=None, meta_graph_suffix='meta', write_meta_graph=True)` {#Saver.save}
-
-Saves variables.
-
-This method runs the ops added by the constructor for saving variables.
-It requires a session in which the graph was launched.  The variables to
-save must also have been initialized.
-
-The method returns the path of the newly created checkpoint file.  This
-path can be passed directly to a call to `restore()`.
-
-##### Args:
-
-
-*  <b>`sess`</b>: A Session to use to save the variables.
-*  <b>`save_path`</b>: String.  Path to the checkpoint filename.  If the saver is
-    `sharded`, this is the prefix of the sharded checkpoint filename.
-*  <b>`global_step`</b>: If provided the global step number is appended to
-    `save_path` to create the checkpoint filename. The optional argument
-    can be a `Tensor`, a `Tensor` name or an integer.
-*  <b>`latest_filename`</b>: Optional name for the protocol buffer file that will
-    contains the list of most recent checkpoint filenames.  That file,
-    kept in the same directory as the checkpoint files, is automatically
-    managed by the saver to keep track of recent checkpoints.  Defaults to
-    'checkpoint'.
-*  <b>`meta_graph_suffix`</b>: Suffix for `MetaGraphDef` file. Defaults to 'meta'.
-*  <b>`write_meta_graph`</b>: `Boolean` indicating whether or not to write the meta
-    graph file.
-
-##### Returns:
-
-  A string: path at which the variables were saved.  If the saver is
-    sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn'
-    is the number of shards created.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `sess` is not a `Session`.
-*  <b>`ValueError`</b>: If `latest_filename` contains path components.
-
-
-- - -
-
-#### `tf.train.Saver.restore(sess, save_path)` {#Saver.restore}
-
-Restores previously saved variables.
-
-This method runs the ops added by the constructor for restoring variables.
-It requires a session in which the graph was launched.  The variables to
-restore do not have to have been initialized, as restoring is itself a way
-to initialize variables.
-
-The `save_path` argument is typically a value previously returned from a
-`save()` call, or a call to `latest_checkpoint()`.
-
-##### Args:
-
-
-*  <b>`sess`</b>: A `Session` to use to restore the parameters.
-*  <b>`save_path`</b>: Path where parameters were previously saved.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the given `save_path` does not point to a file.
-
-
-
-Other utility methods.
-
-- - -
-
-#### `tf.train.Saver.last_checkpoints` {#Saver.last_checkpoints}
-
-List of not-yet-deleted checkpoint filenames.
-
-You can pass any of the returned values to `restore()`.
-
-##### Returns:
-
-  A list of checkpoint filenames, sorted from oldest to newest.
-
-
-- - -
-
-#### `tf.train.Saver.set_last_checkpoints(last_checkpoints)` {#Saver.set_last_checkpoints}
-
-DEPRECATED: Use set_last_checkpoints_with_time.
-
-Sets the list of old checkpoint filenames.
-
-##### Args:
-
-
-*  <b>`last_checkpoints`</b>: A list of checkpoint filenames.
-
-##### Raises:
-
-
-*  <b>`AssertionError`</b>: If last_checkpoints is not a list.
-
-
-- - -
-
-#### `tf.train.Saver.as_saver_def()` {#Saver.as_saver_def}
-
-Generates a `SaverDef` representation of this saver.
-
-##### Returns:
-
-  A `SaverDef` proto.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.train.Saver.export_meta_graph(filename=None, collection_list=None, as_text=False)` {#Saver.export_meta_graph}
-
-Writes `MetaGraphDef` to save_path/filename.
-
-##### Args:
-
-
-*  <b>`filename`</b>: Optional meta_graph filename including the path.
-*  <b>`collection_list`</b>: List of string keys to collect.
-*  <b>`as_text`</b>: If `True`, writes the meta_graph as an ASCII proto.
-
-##### Returns:
-
-  A `MetaGraphDef` proto.
-
-
-- - -
-
-#### `tf.train.Saver.from_proto(saver_def)` {#Saver.from_proto}
-
-Returns a `Saver` object created from `saver_def`.
-
-
-- - -
-
-#### `tf.train.Saver.set_last_checkpoints_with_time(last_checkpoints_with_time)` {#Saver.set_last_checkpoints_with_time}
-
-Sets the list of old checkpoint filenames and timestamps.
-
-##### Args:
-
-
-*  <b>`last_checkpoints_with_time`</b>: A list of tuples of checkpoint filenames and
-    timestamps.
-
-##### Raises:
-
-
-*  <b>`AssertionError`</b>: If last_checkpoints_with_time is not a list.
-
-
-- - -
-
-#### `tf.train.Saver.to_proto()` {#Saver.to_proto}
-
-Converts this `Saver` to a `SaverDef` protocol buffer.
-
-##### Returns:
-
-  A `SaverDef` protocol buffer.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.Server.create_local_server.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.Server.create_local_server.md
new file mode 100644
index 0000000000..f349dc0748
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.Server.create_local_server.md
@@ -0,0 +1,19 @@
+#### `tf.train.Server.create_local_server(start=True)` {#Server.create_local_server}
+
+Creates a new single-process cluster running on the local host.
+
+This method is a convenience wrapper for creating a
+`tf.train.Server` with a `tf.train.ServerDef` that specifies a
+single-process cluster containing a single task in a job called
+`"local"`.
+
+##### Args:
+
+
+*  <b>`start`</b>: (Optional.) Boolean, indicating whether to start the server after
+    creating it. Defaults to `True`.
+
+##### Returns:
+
+  A local `tf.train.Server`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.exponential_decay.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.exponential_decay.md
new file mode 100644
index 0000000000..2b8e72a0a2
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.exponential_decay.md
@@ -0,0 +1,54 @@
+### `tf.train.exponential_decay(learning_rate, global_step, decay_steps, decay_rate, staircase=False, name=None)` {#exponential_decay}
+
+Applies exponential decay to the learning rate.
+
+When training a model, it is often recommended to lower the learning rate as
+the training progresses.  This function applies an exponential decay function
+to a provided initial learning rate.  It requires a `global_step` value to
+compute the decayed learning rate.  You can just pass a TensorFlow variable
+that you increment at each training step.
+
+The function returns the decayed learning rate.  It is computed as:
+
+```python
+decayed_learning_rate = learning_rate *
+                        decay_rate ^ (global_step / decay_steps)
+```
+
+If the argument `staircase` is `True`, then `global_step /decay_steps` is an
+integer division and the decayed learning rate follows a staircase function.
+
+Example: decay every 100000 steps with a base of 0.96:
+
+```python
+...
+global_step = tf.Variable(0, trainable=False)
+starter_learning_rate = 0.1
+learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step,
+                                           100000, 0.96, staircase=True)
+# Passing global_step to minimize() will increment it at each step.
+learning_step = (
+    tf.GradientDescentOptimizer(learning_rate)
+    .minimize(...my loss..., global_step=global_step)
+)
+```
+
+##### Args:
+
+
+*  <b>`learning_rate`</b>: A scalar `float32` or `float64` `Tensor` or a
+    Python number.  The initial learning rate.
+*  <b>`global_step`</b>: A scalar `int32` or `int64` `Tensor` or a Python number.
+    Global step to use for the decay computation.  Must not be negative.
+*  <b>`decay_steps`</b>: A scalar `int32` or `int64` `Tensor` or a Python number.
+    Must be positive.  See the decay computation above.
+*  <b>`decay_rate`</b>: A scalar `float32` or `float64` `Tensor` or a
+    Python number.  The decay rate.
+*  <b>`staircase`</b>: Boolean.  It `True` decay the learning rate at discrete intervals.
+*  <b>`name`</b>: String.  Optional name of the operation.  Defaults to 'ExponentialDecay'
+
+##### Returns:
+
+  A scalar `Tensor` of the same type as `learning_rate`.  The decayed
+  learning rate.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.get_checkpoint_state.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.get_checkpoint_state.md
new file mode 100644
index 0000000000..e2852b2314
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.get_checkpoint_state.md
@@ -0,0 +1,19 @@
+### `tf.train.get_checkpoint_state(checkpoint_dir, latest_filename=None)` {#get_checkpoint_state}
+
+Returns CheckpointState proto from the "checkpoint" file.
+
+If the "checkpoint" file contains a valid CheckpointState
+proto, returns it.
+
+##### Args:
+
+
+*  <b>`checkpoint_dir`</b>: The directory of checkpoints.
+*  <b>`latest_filename`</b>: Optional name of the checkpoint file.  Default to
+    'checkpoint'.
+
+##### Returns:
+
+  A CheckpointState if the state was available, None
+  otherwise.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.range_input_producer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.range_input_producer.md
new file mode 100644
index 0000000000..fa73440d88
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.range_input_producer.md
@@ -0,0 +1,25 @@
+### `tf.train.range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None)` {#range_input_producer}
+
+Produces the integers from 0 to limit-1 in a queue.
+
+##### Args:
+
+
+*  <b>`limit`</b>: An int32 scalar tensor.
+*  <b>`num_epochs`</b>: An integer (optional). If specified, `range_input_producer`
+    produces each integer `num_epochs` times before generating an
+    OutOfRange error. If not specified, `range_input_producer` can cycle
+    through the integers an unlimited number of times.
+*  <b>`shuffle`</b>: Boolean. If true, the integers are randomly shuffled within each
+    epoch.
+*  <b>`seed`</b>: An integer (optional). Seed used if shuffle == True.
+*  <b>`capacity`</b>: An integer. Sets the queue capacity.
+*  <b>`shared_name`</b>: (optional). If set, this queue will be shared under the given
+    name across multiple sessions.
+*  <b>`name`</b>: A name for the operations (optional).
+
+##### Returns:
+
+  A Queue with the output integers.  A `QueueRunner` for the Queue
+  is added to the current `Graph`'s `QUEUE_RUNNER` collection.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.write_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.write_graph.md
new file mode 100644
index 0000000000..eea9025321
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.train.write_graph.md
@@ -0,0 +1,21 @@
+### `tf.train.write_graph(graph_def, logdir, name, as_text=True)` {#write_graph}
+
+Writes a graph proto to a file.
+
+The graph is written as a binary proto unless `as_text` is `True`.
+
+```python
+v = tf.Variable(0, name='my_variable')
+sess = tf.Session()
+tf.train.write_graph(sess.graph_def, '/tmp/my-model', 'train.pbtxt')
+```
+
+##### Args:
+
+
+*  <b>`graph_def`</b>: A `GraphDef` protocol buffer.
+*  <b>`logdir`</b>: Directory where to write the graph. This can refer to remote
+    filesystems, such as Google Cloud Storage (GCS).
+*  <b>`name`</b>: Filename for the graph.
+*  <b>`as_text`</b>: If `True`, writes the graph as an ASCII proto.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.tuple.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.tuple.md
new file mode 100644
index 0000000000..503a98d625
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.tuple.md
@@ -0,0 +1,36 @@
+### `tf.tuple(tensors, name=None, control_inputs=None)` {#tuple}
+
+Group tensors together.
+
+This creates a tuple of tensors with the same values as the `tensors`
+argument, except that the value of each tensor is only returned after the
+values of all tensors have been computed.
+
+`control_inputs` contains additional ops that have to finish before this op
+finishes, but whose outputs are not returned.
+
+This can be used as a "join" mechanism for parallel computations: all the
+argument tensors can be computed in parallel, but the values of any tensor
+returned by `tuple` are only available after all the parallel computations
+are done.
+
+See also `group` and `with_dependencies`.
+
+##### Args:
+
+
+*  <b>`tensors`</b>: A list of `Tensor`s or `IndexedSlices`, some entries can be `None`.
+*  <b>`name`</b>: (optional) A name to use as a `name_scope` for the operation.
+*  <b>`control_inputs`</b>: List of additional ops to finish before returning.
+
+##### Returns:
+
+  Same as `tensors`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `tensors` does not contain any `Tensor` or `IndexedSlices`.
+*  <b>`TypeError`</b>: If `control_inputs` is not a list of `Operation` or `Tensor`
+    objects.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.unsorted_segment_sum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.unsorted_segment_sum.md
deleted file mode 100644
index 63255ce815..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.unsorted_segment_sum.md
+++ /dev/null
@@ -1,38 +0,0 @@
-### `tf.unsorted_segment_sum(data, segment_ids, num_segments, name=None)` {#unsorted_segment_sum}
-
-Computes the sum along segments of a tensor.
-
-Read [the section on
-Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
-of segments.
-
-Computes a tensor such that
-\\(output_i = \sum_j data_j\\) where sum is over `j` such
-that `segment_ids[j] == i`. Unlike `SegmentSum`, `segment_ids`
-need not be sorted and need not cover all values in the full
-  range of valid values.
-
-If the sum is empty for a given segment ID `i`, `output[i] = 0`.
-
-`num_segments` should equal the number of distinct segment IDs.
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/UnsortedSegmentSum.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    A 1-D tensor whose rank is equal to the rank of `data`'s
-    first dimension.
-*  <b>`num_segments`</b>: A `Tensor` of type `int32`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `data`.
-  Has same shape as data, except for dimension 0 which
-  has size `num_segments`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.while_loop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.while_loop.md
deleted file mode 100644
index 4baea56c63..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.while_loop.md
+++ /dev/null
@@ -1,60 +0,0 @@
-### `tf.while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#while_loop}
-
-Repeat `body` while the condition `cond` is true.
-
-`cond` is a callable returning a boolean scalar tensor. `body` is a callable
-returning a list of tensors of the same length and with the same types as
-`loop_vars`. `loop_vars` is a list of tensors that is passed to both `cond`
-and `body`. `cond` and `body` both take as many arguments as there are
-`loop_vars`.
-
-In addition to regular Tensors or IndexedSlices, the body may accept and
-return TensorArray objects.  The flows of the TensorArray objects will
-be appropriately forwarded between loops and during gradient calculations.
-
-While `cond` evaluates to true, `body` is executed.
-
-`while_loop` implements non-strict semantics, enabling multiple iterations
-to run in parallel. The maximum number of parallel iterations can be
-controlled by `parallel_iterations`, which gives users some control over
-memory consumption and execution order. For correct programs, `while_loop`
-should return the same result for any parallel_iterations > 0.
-
-For training, TensorFlow remembers the tensors that are produced in the
-forward inference but needed in back propagation. These tensors can be a
-main source of memory consumption and often cause OOM problems when training
-on GPUs.  When the flag swap_memory is true, we swap out these tensors from
-GPU to CPU.  This for example allows us to train RNN models with very long
-sequences and large batches.
-
-##### Args:
-
-
-*  <b>`cond`</b>: A callable that represents the termination condition of the loop.
-*  <b>`body`</b>: A callable that represents the loop body.
-*  <b>`loop_vars`</b>: The list of variable input tensors.
-*  <b>`parallel_iterations`</b>: The number of iterations allowed to run in parallel.
-*  <b>`back_prop`</b>: Whether backprop is enabled for this while loop.
-*  <b>`swap_memory`</b>: Whether GPU-CPU memory swap is enabled for this loop.
-*  <b>`name`</b>: Optional name prefix for the returned tensors.
-
-##### Returns:
-
-  The output tensors for the loop variables after the loop.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `cond` or `body` is not callable.
-*  <b>`ValueError`</b>: if `loop_var` is empty.
-
-
-*  <b>`Example`</b>: 
-
-  ```python
-  i = tf.constant(0)
-  c = lambda i: tf.less(i, 10)
-  b = lambda i: tf.add(i, 1)
-  r = tf.while_loop(c, b, [i])
-  ```
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.zeros_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.zeros_initializer.md
new file mode 100644
index 0000000000..707393f8be
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.zeros_initializer.md
@@ -0,0 +1,4 @@
+### `tf.zeros_initializer(shape, dtype=tf.float32)` {#zeros_initializer}
+
+An adaptor for zeros() to match the Initializer spec.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.DType.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.DType.md
deleted file mode 100644
index 4c77a143e0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.DType.md
+++ /dev/null
@@ -1,206 +0,0 @@
-Represents the type of the elements in a `Tensor`.
-
-The following `DType` objects are defined:
-
-* `tf.float16`: 16-bit half-precision floating-point.
-* `tf.float32`: 32-bit single-precision floating-point.
-* `tf.float64`: 64-bit double-precision floating-point.
-* `tf.bfloat16`: 16-bit truncated floating-point.
-* `tf.complex64`: 64-bit single-precision complex.
-* `tf.complex128`: 128-bit double-precision complex.
-
-* `tf.int8`: 8-bit signed integer.
-* `tf.uint8`: 8-bit unsigned integer.
-* `tf.uint16`: 16-bit unsigned integer.
-* `tf.int16`: 16-bit signed integer.
-* `tf.int32`: 32-bit signed integer.
-* `tf.int64`: 64-bit signed integer.
-
-* `tf.bool`: Boolean.
-
-* `tf.string`: String.
-
-* `tf.qint8`: Quantized 8-bit signed integer.
-* `tf.quint8`: Quantized 8-bit unsigned integer.
-* `tf.qint16`: Quantized 16-bit signed integer.
-* `tf.quint16`: Quantized 16-bit unsigned integer.
-* `tf.qint32`: Quantized 32-bit signed integer.
-
-In addition, variants of these types with the `_ref` suffix are
-defined for reference-typed tensors.
-
-The `tf.as_dtype()` function converts numpy types and string type
-names to a `DType` object.
-
-- - -
-
-#### `tf.DType.is_compatible_with(other)` {#DType.is_compatible_with}
-
-Returns True if the `other` DType will be converted to this DType.
-
-The conversion rules are as follows:
-
-```
-DType(T)       .is_compatible_with(DType(T))        == True
-DType(T)       .is_compatible_with(DType(T).as_ref) == True
-DType(T).as_ref.is_compatible_with(DType(T))        == False
-DType(T).as_ref.is_compatible_with(DType(T).as_ref) == True
-```
-
-##### Args:
-
-
-*  <b>`other`</b>: A `DType` (or object that may be converted to a `DType`).
-
-##### Returns:
-
-  True if a Tensor of the `other` `DType` will be implicitly converted to
-  this `DType`.
-
-
-- - -
-
-#### `tf.DType.name` {#DType.name}
-
-Returns the string name for this `DType`.
-
-
-- - -
-
-#### `tf.DType.base_dtype` {#DType.base_dtype}
-
-Returns a non-reference `DType` based on this `DType`.
-
-
-- - -
-
-#### `tf.DType.real_dtype` {#DType.real_dtype}
-
-Returns the dtype correspond to this dtype's real part.
-
-
-- - -
-
-#### `tf.DType.is_ref_dtype` {#DType.is_ref_dtype}
-
-Returns `True` if this `DType` represents a reference type.
-
-
-- - -
-
-#### `tf.DType.as_ref` {#DType.as_ref}
-
-Returns a reference `DType` based on this `DType`.
-
-
-- - -
-
-#### `tf.DType.is_floating` {#DType.is_floating}
-
-Returns whether this is a (real) floating point type.
-
-
-- - -
-
-#### `tf.DType.is_complex` {#DType.is_complex}
-
-Returns whether this is a complex floating point type.
-
-
-- - -
-
-#### `tf.DType.is_integer` {#DType.is_integer}
-
-Returns whether this is a (non-quantized) integer type.
-
-
-- - -
-
-#### `tf.DType.is_quantized` {#DType.is_quantized}
-
-Returns whether this is a quantized data type.
-
-
-- - -
-
-#### `tf.DType.is_unsigned` {#DType.is_unsigned}
-
-Returns whether this type is unsigned.
-
-Non-numeric, unordered, and quantized types are not considered unsigned, and
-this function returns `False`.
-
-##### Returns:
-
-  Whether a `DType` is unsigned.
-
-
-
-- - -
-
-#### `tf.DType.as_numpy_dtype` {#DType.as_numpy_dtype}
-
-Returns a `numpy.dtype` based on this `DType`.
-
-
-- - -
-
-#### `tf.DType.as_datatype_enum` {#DType.as_datatype_enum}
-
-Returns a `types_pb2.DataType` enum value based on this `DType`.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.DType.__init__(type_enum)` {#DType.__init__}
-
-Creates a new `DataType`.
-
-NOTE(mrry): In normal circumstances, you should not need to
-construct a `DataType` object directly. Instead, use the
-`tf.as_dtype()` function.
-
-##### Args:
-
-
-*  <b>`type_enum`</b>: A `types_pb2.DataType` enum value.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `type_enum` is not a value `types_pb2.DataType`.
-
-
-- - -
-
-#### `tf.DType.max` {#DType.max}
-
-Returns the maximum representable value in this data type.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if this is a non-numeric, unordered, or quantized type.
-
-
-- - -
-
-#### `tf.DType.min` {#DType.min}
-
-Returns the minimum representable value in this data type.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if this is a non-numeric, unordered, or quantized type.
-
-
-- - -
-
-#### `tf.DType.size` {#DType.size}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.IndexedSlices.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.IndexedSlices.md
deleted file mode 100644
index 435a178205..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.IndexedSlices.md
+++ /dev/null
@@ -1,93 +0,0 @@
-A sparse representation of a set of tensor slices at given indices.
-
-This class is a simple wrapper for a pair of `Tensor` objects:
-
-* `values`: A `Tensor` of any dtype with shape `[D0, D1, ..., Dn]`.
-* `indices`: A 1-D integer `Tensor` with shape `[D0]`.
-
-An `IndexedSlices` is typically used to represent a subset of a larger
-tensor `dense` of shape `[LARGE0, D1, .. , DN]` where `LARGE0 >> D0`.
-The values in `indices` are the indices in the first dimension of
-the slices that have been extracted from the larger tensor.
-
-The dense tensor `dense` represented by an `IndexedSlices` `slices` has
-
-```python
-dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]
-```
-
-The `IndexedSlices` class is used principally in the definition of
-gradients for operations that have sparse gradients
-(e.g. [`tf.gather`](../../api_docs/python/array_ops.md#gather)).
-
-Contrast this representation with
-[`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
-which uses multi-dimensional indices and scalar values.
-
-- - -
-
-#### `tf.IndexedSlices.__init__(values, indices, dense_shape=None)` {#IndexedSlices.__init__}
-
-Creates an `IndexedSlices`.
-
-
-
-- - -
-
-#### `tf.IndexedSlices.values` {#IndexedSlices.values}
-
-A `Tensor` containing the values of the slices.
-
-
-- - -
-
-#### `tf.IndexedSlices.indices` {#IndexedSlices.indices}
-
-A 1-D `Tensor` containing the indices of the slices.
-
-
-- - -
-
-#### `tf.IndexedSlices.dense_shape` {#IndexedSlices.dense_shape}
-
-A 1-D `Tensor` containing the shape of the corresponding dense tensor.
-
-
-
-- - -
-
-#### `tf.IndexedSlices.name` {#IndexedSlices.name}
-
-The name of this `IndexedSlices`.
-
-
-- - -
-
-#### `tf.IndexedSlices.dtype` {#IndexedSlices.dtype}
-
-The `DType` of elements in this tensor.
-
-
-- - -
-
-#### `tf.IndexedSlices.device` {#IndexedSlices.device}
-
-The name of the device on which `values` will be produced, or `None`.
-
-
-- - -
-
-#### `tf.IndexedSlices.op` {#IndexedSlices.op}
-
-The `Operation` that produces `values` as an output.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.IndexedSlices.graph` {#IndexedSlices.graph}
-
-The `Graph` that contains the values, indices, and shape tensors.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.VariableScope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.VariableScope.md
deleted file mode 100644
index 04fdca9bdf..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.VariableScope.md
+++ /dev/null
@@ -1,105 +0,0 @@
-Variable scope object to carry defaults to provide to get_variable.
-
-Many of the arguments we need for get_variable in a variable store are most
-easily handled with a context. This object is used for the defaults.
-
-Attributes:
-  name: name of the current scope, used as prefix in get_variable.
-  initializer: default initializer passed to get_variable.
-  regularizer: default regularizer passed to get_variable.
-  reuse: Boolean or None, setting the reuse in get_variable.
-  caching_device: string, callable, or None: the caching device passed to
-    get_variable.
-  partitioner: callable or `None`: the partitioner passed to `get_variable`.
-  name_scope: The name passed to `tf.name_scope`.
-- - -
-
-#### `tf.VariableScope.__init__(reuse, name='', initializer=None, regularizer=None, caching_device=None, partitioner=None, name_scope='')` {#VariableScope.__init__}
-
-Creates a new VariableScope with the given properties.
-
-
-- - -
-
-#### `tf.VariableScope.caching_device` {#VariableScope.caching_device}
-
-
-
-
-- - -
-
-#### `tf.VariableScope.get_variable(var_store, name, shape=None, dtype=tf.float32, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True)` {#VariableScope.get_variable}
-
-Gets an existing variable with this name or create a new one.
-
-
-- - -
-
-#### `tf.VariableScope.initializer` {#VariableScope.initializer}
-
-
-
-
-- - -
-
-#### `tf.VariableScope.name` {#VariableScope.name}
-
-
-
-
-- - -
-
-#### `tf.VariableScope.partitioner` {#VariableScope.partitioner}
-
-
-
-
-- - -
-
-#### `tf.VariableScope.regularizer` {#VariableScope.regularizer}
-
-
-
-
-- - -
-
-#### `tf.VariableScope.reuse` {#VariableScope.reuse}
-
-
-
-
-- - -
-
-#### `tf.VariableScope.reuse_variables()` {#VariableScope.reuse_variables}
-
-Reuse variables in this scope.
-
-
-- - -
-
-#### `tf.VariableScope.set_caching_device(caching_device)` {#VariableScope.set_caching_device}
-
-Set caching_device for this scope.
-
-
-- - -
-
-#### `tf.VariableScope.set_initializer(initializer)` {#VariableScope.set_initializer}
-
-Set initializer for this scope.
-
-
-- - -
-
-#### `tf.VariableScope.set_partitioner(partitioner)` {#VariableScope.set_partitioner}
-
-Set partitioner for this scope.
-
-
-- - -
-
-#### `tf.VariableScope.set_regularizer(regularizer)` {#VariableScope.set_regularizer}
-
-Set regularizer for this scope.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.add_check_numerics_ops.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.add_check_numerics_ops.md
new file mode 100644
index 0000000000..9e72af79db
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.add_check_numerics_ops.md
@@ -0,0 +1,13 @@
+### `tf.add_check_numerics_ops()` {#add_check_numerics_ops}
+
+Connect a `check_numerics` to every floating point tensor.
+
+`check_numerics` operations themselves are added for each `float` or `double`
+tensor in the graph. For all ops in the graph, the `check_numerics` op for
+all of its (`float` or `double`) inputs is guaranteed to run before the
+`check_numerics` op on any of its outputs.
+
+##### Returns:
+
+  A `group` op depending on all `check_numerics` ops added.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.assert_variables_initialized.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.assert_variables_initialized.md
deleted file mode 100644
index ef61848aa8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.assert_variables_initialized.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.assert_variables_initialized(var_list=None)` {#assert_variables_initialized}
-
-Returns an Op to check if variables are initialized.
-
-NOTE: This function is obsolete and will be removed in 6 months.  Please
-change your implementation to use `report_uninitialized_variables()`.
-
-When run, the returned Op will raise the exception `FailedPreconditionError`
-if any of the variables has not yet been initialized.
-
-Note: This function is implemented by trying to fetch the values of the
-variables. If one of the variables is not initialized a message may be
-logged by the C++ runtime. This is expected.
-
-##### Args:
-
-
-*  <b>`var_list`</b>: List of `Variable` objects to check. Defaults to the
-    value of `all_variables().`
-
-##### Returns:
-
-  An Op, or None if there are no variables.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.batch_fft3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.batch_fft3d.md
deleted file mode 100644
index 10c2ea3bf6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.batch_fft3d.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.batch_fft3d(input, name=None)` {#batch_fft3d}
-
-Compute the 3-dimensional discrete Fourier Transform over the inner-most 3
-
-dimensions of `input`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`.
-  A complex64 tensor of the same shape as `input`. The inner-most 3
-  dimensions of `input` are replaced with their 3D Fourier Transform.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.cast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.cast.md
deleted file mode 100644
index 9571f87afe..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.cast.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.cast(x, dtype, name=None)` {#cast}
-
-Casts a tensor to a new type.
-
-The operation casts `x` (in case of `Tensor`) or `x.values`
-(in case of `SparseTensor`) to `dtype`.
-
-For example:
-
-```python
-# tensor `a` is [1.8, 2.2], dtype=tf.float
-tf.cast(a, tf.int32) ==> [1, 2]  # dtype=tf.int32
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
-*  <b>`dtype`</b>: The destination type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` or `SparseTensor` with same shape as `x`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `x` cannot be cast to the `dtype`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.cholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.cholesky.md
new file mode 100644
index 0000000000..4032b80d8e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.cholesky.md
@@ -0,0 +1,22 @@
+### `tf.cholesky(input, name=None)` {#cholesky}
+
+Calculates the Cholesky decomposition of a square matrix.
+
+The input has to be symmetric and positive definite. Only the lower-triangular
+part of the input will be used for this operation. The upper-triangular part
+will not be read.
+
+The result is the lower-triangular matrix of the Cholesky decomposition of the
+input, `L`, so that `input = L L^*`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
+    Shape is `[M, M]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`. Shape is `[M, M]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.Uniform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.Uniform.md
deleted file mode 100644
index ad6008c9f6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.Uniform.md
+++ /dev/null
@@ -1,216 +0,0 @@
-Uniform distribution with `a` and `b` parameters.
-
-The PDF of this distribution is constant between [`a`, `b`], and 0 elsewhere.
-- - -
-
-#### `tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, name='Uniform')` {#Uniform.__init__}
-
-Construct Uniform distributions with `a` and `b`.
-
-The parameters `a` and `b` must be shaped in a way that supports
-broadcasting (e.g. `b - a` is a valid operation).
-
-Here are examples without broadcasting:
-
-```python
-# Without broadcasting
-u1 = Uniform(3.0, 4.0)  # a single uniform distribution [3, 4]
-u2 = Uniform([1.0, 2.0], [3.0, 4.0])  # 2 distributions [1, 3], [2, 4]
-u3 = Uniform([[1.0, 2.0],
-              [3.0, 4.0]],
-             [[1.5, 2.5],
-              [3.5, 4.5]])  # 4 distributions
-```
-
-And with broadcasting:
-
-```python
-u1 = Uniform(3.0, [5.0, 6.0, 7.0])  # 3 distributions
-```
-
-##### Args:
-
-
-*  <b>`a`</b>: `float` or `double` tensor, the minimum endpoint.
-*  <b>`b`</b>: `float` or `double` tensor, the maximum endpoint. Must be > `a`.
-*  <b>`name`</b>: The name to prefix Ops created by this distribution class.
-
-##### Raises:
-
-
-*  <b>`InvalidArgumentError`</b>: if `a >= b`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.a` {#Uniform.a}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.b` {#Uniform.b}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.batch_shape(name='batch_shape')` {#Uniform.batch_shape}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.cdf(x, name='cdf')` {#Uniform.cdf}
-
-CDF of observations in `x` under these Uniform distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`. If `x` is `nan`, will
-      return `nan`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.dtype` {#Uniform.dtype}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.entropy(name='entropy')` {#Uniform.entropy}
-
-The entropy of Uniform distribution(s).
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.event_shape(name='event_shape')` {#Uniform.event_shape}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.get_batch_shape()` {#Uniform.get_batch_shape}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.get_event_shape()` {#Uniform.get_event_shape}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.is_reparameterized` {#Uniform.is_reparameterized}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.log_cdf(x, name='log_cdf')` {#Uniform.log_cdf}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.log_pdf(x, name='log_pdf')` {#Uniform.log_pdf}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.mean` {#Uniform.mean}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.name` {#Uniform.name}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.pdf(x, name='pdf')` {#Uniform.pdf}
-
-The PDF of observations in `x` under these Uniform distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`. If `x` is `nan`, will
-      return `nan`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.range` {#Uniform.range}
-
-`b - a`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.sample(n, seed=None, name='sample')` {#Uniform.sample}
-
-Sample `n` observations from the Uniform Distributions.
-
-##### Args:
-
-
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Uniform.variance` {#Uniform.variance}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.normal_congugates_known_sigma_predictive.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.normal_congugates_known_sigma_predictive.md
new file mode 100644
index 0000000000..89e4e5ca3c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.distributions.normal_congugates_known_sigma_predictive.md
@@ -0,0 +1,55 @@
+### `tf.contrib.distributions.normal_congugates_known_sigma_predictive(prior, sigma, s, n)` {#normal_congugates_known_sigma_predictive}
+
+Posterior predictive Normal distribution w. conjugate prior on the mean.
+
+This model assumes that `n` observations (with sum `s`) come from a
+Normal with unknown mean `mu` (described by the Normal `prior`)
+and known variance `sigma^2`.  The "known sigma predictive"
+is the distribution of new observations, conditioned on the existing
+observations and our prior.
+
+Accepts a prior Normal distribution object, having parameters
+`mu0` and `sigma0`, as well as known `sigma` values of the predictive
+distribution(s) (also assumed Normal),
+and statistical estimates `s` (the sum(s) of the observations) and
+`n` (the number(s) of observations).
+
+Calculates the Normal distribution(s) `p(x | sigma^2)`:
+
+```
+  p(x | sigma^2) = int N(x | mu, sigma^2) N(mu | prior.mu, prior.sigma^2) dmu
+                 = N(x | prior.mu, 1/(sigma^2 + prior.sigma^2))
+```
+
+Returns the predictive posterior distribution object, with parameters
+`(mu', sigma'^2)`, where:
+
+```
+sigma_n^2 = 1/(1/sigma0^2 + n/sigma^2),
+mu' = (mu0/sigma0^2 + s/sigma^2) * sigma_n^2.
+sigma'^2 = sigma_n^2 + sigma^2,
+```
+
+Distribution parameters from `prior`, as well as `sigma`, `s`, and `n`.
+will broadcast in the case of multidimensional sets of parameters.
+
+##### Args:
+
+
+*  <b>`prior`</b>: `Normal` object of type `dtype`:
+    the prior distribution having parameters `(mu0, sigma0)`.
+*  <b>`sigma`</b>: tensor of type `dtype`, taking values `sigma > 0`.
+    The known stddev parameter(s).
+*  <b>`s`</b>: Tensor of type `dtype`.  The sum(s) of observations.
+*  <b>`n`</b>: Tensor of type `int`.  The number(s) of observations.
+
+##### Returns:
+
+  A new Normal predictive distribution object.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if dtype of `s` does not match `dtype`, or `prior` is not a
+    Normal object.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.apply_regularization.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.apply_regularization.md
deleted file mode 100644
index 8216a4fa25..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.apply_regularization.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.contrib.layers.apply_regularization(regularizer, weights_list=None)` {#apply_regularization}
-
-Returns the summed penalty by applying `regularizer` to the `weights_list`.
-
-Adding a regularization penalty over the layer weights and embedding weights
-can help prevent overfitting the training data. Regularization over layer
-biases is less common/useful, but assuming proper data preprocessing/mean
-subtraction, it usually shouldn't hurt much either.
-
-##### Args:
-
-
-*  <b>`regularizer`</b>: A function that takes a single `Tensor` argument and returns
-    a scalar `Tensor` output.
-*  <b>`weights_list`</b>: List of weights `Tensors` or `Variables` to apply
-    `regularizer` over. Defaults to the `GraphKeys.WEIGHTS` collection if
-    `None`.
-
-##### Returns:
-
-  A scalar representing the overall regularization penalty.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `regularizer` does not return a scalar output.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.l1_regularizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.l1_regularizer.md
new file mode 100644
index 0000000000..1aa8074980
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.l1_regularizer.md
@@ -0,0 +1,22 @@
+### `tf.contrib.layers.l1_regularizer(scale)` {#l1_regularizer}
+
+Returns a function that can be used to apply L1 regularization to weights.
+
+L1 regularization encourages sparsity.
+
+##### Args:
+
+
+*  <b>`scale`</b>: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
+
+##### Returns:
+
+  A function with signature `l1(weights, name=None)` that apply L1
+  regularization.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If scale is outside of the range [0.0, 1.0] or if scale is not a
+  float.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.sum_regularizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.sum_regularizer.md
deleted file mode 100644
index ee05583b04..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.sum_regularizer.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.contrib.layers.sum_regularizer(regularizer_list)` {#sum_regularizer}
-
-Returns a function that applies the sum of multiple regularizers.
-
-##### Args:
-
-
-*  <b>`regularizer_list`</b>: A list of regularizers to apply.
-
-##### Returns:
-
-  A function with signature `sum_reg(weights, name=None)` that applies the
-  sum of all the input regularizers.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.summarize_activation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.summarize_activation.md
new file mode 100644
index 0000000000..3aed0ff43c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.summarize_activation.md
@@ -0,0 +1,16 @@
+### `tf.contrib.layers.summarize_activation(op)` {#summarize_activation}
+
+Summarize an activation.
+
+This applies the given activation and adds useful summaries specific to the
+activation.
+
+##### Args:
+
+
+*  <b>`op`</b>: The tensor to summarize (assumed to be a layer activation).
+
+##### Returns:
+
+  The summary op created to summarize `op`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.summarize_collection.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.summarize_collection.md
new file mode 100644
index 0000000000..b1b5f56056
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.summarize_collection.md
@@ -0,0 +1,4 @@
+### `tf.contrib.layers.summarize_collection(collection, name_filter=None, summarizer=summarize_tensor)` {#summarize_collection}
+
+Summarize a graph collection of tensors, possibly filtered by name.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.summarize_tensors.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.summarize_tensors.md
deleted file mode 100644
index 608999b437..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.summarize_tensors.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.contrib.layers.summarize_tensors(tensors, summarizer=summarize_tensor)` {#summarize_tensors}
-
-Summarize a set of tensors.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md
deleted file mode 100644
index c3270290b9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md
+++ /dev/null
@@ -1,295 +0,0 @@
-Base class for all TensorFlow estimators.
-
-Parameters:
-  model_fn: Model function, that takes input X, y tensors and outputs
-    prediction and loss tensors.
-  n_classes: Number of classes in the target.
-  batch_size: Mini batch size.
-  steps: Number of steps to run over data.
-  optimizer: Optimizer name (or class), for example "SGD", "Adam",
-    "Adagrad".
-  learning_rate: If this is constant float value, no decay function is used.
-    Instead, a customized decay function can be passed that accepts
-    global_step as parameter and returns a Tensor.
-    e.g. exponential decay function:
-    def exp_decay(global_step):
-        return tf.train.exponential_decay(
-            learning_rate=0.1, global_step,
-            decay_steps=2, decay_rate=0.001)
-  clip_gradients: Clip norm of the gradients to this value to stop
-    gradient explosion.
-  class_weight: None or list of n_classes floats. Weight associated with
-    classes for loss computation. If not given, all classes are supposed to
-    have weight one.
-  continue_training: when continue_training is True, once initialized
-    model will be continuely trained on every call of fit.
-  config: RunConfig object that controls the configurations of the
-    session, e.g. num_cores, gpu_memory_fraction, etc.
-  verbose: Controls the verbosity, possible values:
-    0: the algorithm and debug information is muted.
-    1: trainer prints the progress.
-    2: log device placement is printed.
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.__init__(model_fn, n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, class_weight=None, continue_training=False, config=None, verbose=1)` {#TensorFlowEstimator.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowEstimator.evaluate}
-
-See base class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowEstimator.fit}
-
-Builds a neural network model given provided `model_fn` and training
-data X and y.
-
-Note: called first time constructs the graph and initializers
-variables. Consecutives times it will continue training the same model.
-This logic follows partial_fit() interface in scikit-learn.
-
-To restart learning, create new estimator.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class labels in classification, real numbers in regression).
-
-*  <b>`steps`</b>: int, number of steps to train.
-         If None or 0, train for `self.steps`.
-*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
-    invoke early stopping.
-*  <b>`logdir`</b>: the directory to save the log file that can be used for
-  optional visualization.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.get_params(deep=True)` {#TensorFlowEstimator.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.get_tensor(name)` {#TensorFlowEstimator.get_tensor}
-
-Returns tensor by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.get_tensor_value(name)` {#TensorFlowEstimator.get_tensor_value}
-
-Returns value of the tensor give by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.get_variable_names()` {#TensorFlowEstimator.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.model_dir` {#TensorFlowEstimator.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.partial_fit(x, y)` {#TensorFlowEstimator.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class label in classification, real numbers in regression).
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.predict(x, axis=1, batch_size=None)` {#TensorFlowEstimator.predict}
-
-Predict class or regression for X.
-
-For a classification model, the predicted class for each sample in X is
-returned. For a regression model, the predicted value based on X is
-returned.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`axis`</b>: Which axis to argmax for classification.
-    By default axis 1 (next after batch) is used.
-    Use 2 for sequence predictions.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member
-    variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
-  value.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.predict_proba(x, batch_size=None)` {#TensorFlowEstimator.predict_proba}
-
-Predict class probability of the input samples X.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
-  probabilities for each class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.restore(cls, path, config=None)` {#TensorFlowEstimator.restore}
-
-Restores model from give path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Path to the checkpoints and other model information.
-*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
-      reconfigured.
-
-##### Returns:
-
-  Estimator, object of the subclass of TensorFlowEstimator.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.save(path)` {#TensorFlowEstimator.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.set_params(**params)` {#TensorFlowEstimator.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowEstimator.train(input_fn, steps, monitors=None)` {#TensorFlowEstimator.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.extract_dask_labels.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.extract_dask_labels.md
deleted file mode 100644
index 15831ce758..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.extract_dask_labels.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.contrib.learn.extract_dask_labels(labels)` {#extract_dask_labels}
-
-Extract data from dask.Series for labels
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.confusion_matrix.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.confusion_matrix.md
new file mode 100644
index 0000000000..a57fa44318
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.confusion_matrix.md
@@ -0,0 +1,45 @@
+### `tf.contrib.metrics.confusion_matrix(predictions, labels, num_classes=None, name=None)` {#confusion_matrix}
+
+Computes the confusion matrix from predictions and labels
+
+Calculate the Confusion Matrix for a pair of prediction and
+label 1-D int arrays.
+
+Considering a prediction array such as: `[1, 2, 3]`
+And a label array such as: `[2, 2, 3]`
+
+##### The confusion matrix returned would be the following one:
+
+    [[0, 0, 0]
+     [0, 1, 0]
+     [0, 1, 0]
+     [0, 0, 1]]
+
+Where the matrix rows represent the prediction labels and the columns
+represents the real labels. The confusion matrix is always a 2-D array
+of shape [n, n], where n is the number of valid labels for a given
+classification task. Both prediction and labels must be 1-D arrays of
+the same shape in order for this function to work.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: A 1-D array represeting the predictions for a given
+               classification.
+*  <b>`labels`</b>: A 1-D represeting the real labels for the classification task.
+*  <b>`num_classes`</b>: The possible number of labels the classification task can
+               have. If this value is not provided, it will be calculated
+               using both predictions and labels array.
+*  <b>`name`</b>: Scope name.
+
+##### Returns:
+
+  A l X l matrix represeting the confusion matrix, where l in the number of
+  possible labels in the classification task.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If both predictions and labels are not 1-D vectors and do not
+              have the same size.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.set_union.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.set_union.md
deleted file mode 100644
index bb378fe2a2..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.set_union.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.contrib.metrics.set_union(a, b, validate_indices=True)` {#set_union}
-
-Compute set union of elements in last dimension of `a` and `b`.
-
-All but the last dimension of `a` and `b` must match.
-
-##### Args:
-
-
-*  <b>`a`</b>: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
-      must be sorted in row-major order.
-*  <b>`b`</b>: `Tensor` or `SparseTensor` of the same type as `a`. Must be
-      `SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
-      sorted in row-major order.
-*  <b>`validate_indices`</b>: Whether to validate the order and range of sparse indices
-     in `a` and `b`.
-
-##### Returns:
-
-  A `SparseTensor` with the same rank as `a` and `b`, and all but the last
-  dimension the same. Elements along the last dimension contain the
-  unions.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.streaming_mean_cosine_distance.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.streaming_mean_cosine_distance.md
new file mode 100644
index 0000000000..1900cd1a97
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.streaming_mean_cosine_distance.md
@@ -0,0 +1,48 @@
+### `tf.contrib.metrics.streaming_mean_cosine_distance(predictions, labels, dim, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean_cosine_distance}
+
+Computes the cosine distance between the labels and predictions.
+
+The `streaming_mean_cosine_distance` function creates two local variables,
+`total` and `count` that are used to compute the average cosine distance
+between `predictions` and `labels`. This average is ultimately returned as
+`mean_distance` which is an idempotent operation that simply divides `total`
+by `count. To facilitate the estimation of a mean over multiple batches
+of data, the function creates an `update_op` operation whose behavior is
+dependent on the value of `weights`. If `weights` is None, then `update_op`
+increments `total` with the reduced sum of `values and increments `count` with
+the number of elements in `values`. If `weights` is not `None`, then
+`update_op` increments `total` with the reduced sum of the product of `values`
+and `weights` and increments `count` with the reduced sum of weights.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: A tensor of the same size as labels.
+*  <b>`labels`</b>: A tensor of arbitrary size.
+*  <b>`dim`</b>: The dimension along which the cosine distance is computed.
+*  <b>`weights`</b>: An optional set of weights which indicates which predictions to
+    ignore during metric computation. Its size matches that of labels except
+    for the value of 'dim' which should be 1. For example if labels has
+    dimensions [32, 100, 200, 3], then `weights` should have dimensions
+    [32, 100, 200, 1].
+*  <b>`metrics_collections`</b>: An optional list of collections that the metric
+    value variable should be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that the metric update
+    ops should be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`mean_distance`</b>: A tensor representing the current mean, the value of `total`
+    divided by `count`.
+*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
+    appropriately.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If labels and predictions are of different sizes or if the
+    ignore_mask is of the wrong size or if either `metrics_collections` or
+    `updates_collections` are not a list or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.streaming_mean_squared_error.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.streaming_mean_squared_error.md
deleted file mode 100644
index 6d682d0427..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.metrics.streaming_mean_squared_error.md
+++ /dev/null
@@ -1,48 +0,0 @@
-### `tf.contrib.metrics.streaming_mean_squared_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean_squared_error}
-
-Computes the mean squared error between the labels and predictions.
-
-The `streaming_mean_squared_error` function creates two local variables,
-`total` and `count` that are used to compute the mean squared error.
-This average is ultimately returned as `mean_squared_error`: an idempotent
-operation that simply divides `total` by `count`. To facilitate the estimation
-of the mean squared error over a stream of data, the function utilizes two
-operations. First, a `squared_error` operation computes the element-wise
-square of the difference between `predictions` and `labels`. Second, an
-`update_op` operation whose behavior is dependent on the value of `weights`.
-If `weights` is None, then `update_op` increments `total` with the
-reduced sum of `squared_error` and increments `count` with the number of
-elements in `squared_error`. If `weights` is not `None`, then `update_op`
-increments `total` with the reduced sum of the product of `weights` and
-`squared_error` and increments `count` with the reduced sum of `weights`. In
-addition to performing the updates, `update_op` also returns the
-`mean_squared_error` value.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: A `Tensor` of arbitrary shape.
-*  <b>`labels`</b>: A `Tensor` of the same shape as `predictions`.
-*  <b>`weights`</b>: An optional set of weights of the same shape as `predictions`. If
-    `weights` is not None, the function computes a weighted mean.
-*  <b>`metrics_collections`</b>: An optional list of collections that
-    `mean_squared_error` should be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
-    be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`mean_squared_error`</b>: A tensor representing the current mean, the value of
-    `total` divided by `count`.
-*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
-    appropriately and whose value matches `mean_squared_error`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `weights` is not `None` and its shape doesn't match
-    `predictions` or if either `metrics_collections` or `updates_collections`
-    are not a list or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.util.constant_value.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.util.constant_value.md
new file mode 100644
index 0000000000..58ba7b0abb
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.util.constant_value.md
@@ -0,0 +1,31 @@
+### `tf.contrib.util.constant_value(tensor)` {#constant_value}
+
+Returns the constant value of the given tensor, if efficiently calculable.
+
+This function attempts to partially evaluate the given tensor, and
+returns its value as a numpy ndarray if this succeeds.
+
+TODO(mrry): Consider whether this function should use a registration
+mechanism like gradients and ShapeFunctions, so that it is easily
+extensible.
+
+NOTE: If `constant_value(tensor)` returns a non-`None` result, it will no
+longer be possible to feed a different value for `tensor`. This allows the
+result of this function to influence the graph that is constructed, and
+permits static shape optimizations.
+
+##### Args:
+
+
+*  <b>`tensor`</b>: The Tensor to be evaluated.
+
+##### Returns:
+
+  A numpy ndarray containing the constant value of the given `tensor`,
+  or None if it cannot be calculated.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if tensor is not an ops.Tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.util.ops_used_by_graph_def.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.util.ops_used_by_graph_def.md
new file mode 100644
index 0000000000..38a9cc4f43
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.util.ops_used_by_graph_def.md
@@ -0,0 +1,15 @@
+### `tf.contrib.util.ops_used_by_graph_def(graph_def)` {#ops_used_by_graph_def}
+
+Collect the list of ops used by a graph.
+
+Does not validate that the ops are all registered.
+
+##### Args:
+
+
+*  <b>`graph_def`</b>: A `GraphDef` proto, as from `graph.as_graph_def()`.
+
+##### Returns:
+
+  A list of strings, each naming an op used by the graph.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.convert_to_tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.convert_to_tensor.md
new file mode 100644
index 0000000000..29902ed467
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.convert_to_tensor.md
@@ -0,0 +1,47 @@
+### `tf.convert_to_tensor(value, dtype=None, name=None, as_ref=False)` {#convert_to_tensor}
+
+Converts the given `value` to a `Tensor`.
+
+This function converts Python objects of various types to `Tensor`
+objects. It accepts `Tensor` objects, numpy arrays, Python lists,
+and Python scalars. For example:
+
+```python
+import numpy as np
+
+def my_func(arg):
+  arg = tf.convert_to_tensor(arg, dtype=tf.float32)
+  return tf.matmul(arg, arg) + arg
+
+# The following calls are equivalent.
+value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))
+value_2 = my_func([[1.0, 2.0], [3.0, 4.0]])
+value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32))
+```
+
+This function can be useful when composing a new operation in Python
+(such as `my_func` in the example above). All standard Python op
+constructors apply this function to each of their Tensor-valued
+inputs, which allows those ops to accept numpy arrays, Python lists,
+and scalars in addition to `Tensor` objects.
+
+##### Args:
+
+
+*  <b>`value`</b>: An object whose type has a registered `Tensor` conversion function.
+*  <b>`dtype`</b>: Optional element type for the returned tensor. If missing, the
+    type is inferred from the type of `value`.
+*  <b>`name`</b>: Optional name to use if a new `Tensor` is created.
+*  <b>`as_ref`</b>: True if we want the result as a ref tensor. Only used if a new
+    `Tensor` is created.
+
+##### Returns:
+
+  A `Tensor` based on `value`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If no conversion function is registered for `value`.
+*  <b>`RuntimeError`</b>: If a registered conversion function returns an invalid value.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.device.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.device.md
new file mode 100644
index 0000000000..2a5e33203d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.device.md
@@ -0,0 +1,19 @@
+### `tf.device(device_name_or_function)` {#device}
+
+Wrapper for `Graph.device()` using the default graph.
+
+See
+[`Graph.device()`](../../api_docs/python/framework.md#Graph.device)
+for more details.
+
+##### Args:
+
+
+*  <b>`device_name_or_function`</b>: The device name or function to use in
+    the context.
+
+##### Returns:
+
+  A context manager that specifies the default device to use for newly
+  created ops.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.dynamic_partition.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.dynamic_partition.md
deleted file mode 100644
index 3fbb885055..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.dynamic_partition.md
+++ /dev/null
@@ -1,50 +0,0 @@
-### `tf.dynamic_partition(data, partitions, num_partitions, name=None)` {#dynamic_partition}
-
-Partitions `data` into `num_partitions` tensors using indices from `partitions`.
-
-For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
-becomes part of `outputs[partitions[js]]`.  The slices with `partitions[js] = i`
-are placed in `outputs[i]` in lexicographic order of `js`, and the first
-dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
-In detail,
-
-    outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
-
-    outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
-
-`data.shape` must start with `partitions.shape`.
-
-For example:
-
-    # Scalar partitions
-    partitions = 1
-    num_partitions = 2
-    data = [10, 20]
-    outputs[0] = []  # Empty with shape [0, 2]
-    outputs[1] = [[10, 20]]
-
-    # Vector partitions
-    partitions = [0, 0, 1, 1, 0]
-    num_partitions = 2
-    data = [10, 20, 30, 40, 50]
-    outputs[0] = [10, 20, 50]
-    outputs[1] = [30, 40]
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/DynamicPartition.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`data`</b>: A `Tensor`.
-*  <b>`partitions`</b>: A `Tensor` of type `int32`.
-    Any shape.  Indices in the range `[0, num_partitions)`.
-*  <b>`num_partitions`</b>: An `int` that is `>= 1`.
-    The number of partitions to output.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A list of `num_partitions` `Tensor` objects of the same type as data.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.CancelledError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.CancelledError.md
deleted file mode 100644
index cf20c0e2e3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.CancelledError.md
+++ /dev/null
@@ -1,17 +0,0 @@
-Raised when an operation or step is cancelled.
-
-For example, a long-running operation (e.g.
-[`queue.enqueue()`](../../api_docs/python/io_ops.md#QueueBase.enqueue) may be
-cancelled by running another operation (e.g.
-[`queue.close(cancel_pending_enqueues=True)`](../../api_docs/python/io_ops.md#QueueBase.close),
-or by [closing the session](../../api_docs/python/client.md#Session.close).
-A step that is running such a long-running operation will fail by raising
-`CancelledError`.
-
-- - -
-
-#### `tf.errors.CancelledError.__init__(node_def, op, message)` {#CancelledError.__init__}
-
-Creates a `CancelledError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.DeadlineExceededError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.DeadlineExceededError.md
new file mode 100644
index 0000000000..e8ef3be06e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.DeadlineExceededError.md
@@ -0,0 +1,11 @@
+Raised when a deadline expires before an operation could complete.
+
+This exception is not currently used.
+
+- - -
+
+#### `tf.errors.DeadlineExceededError.__init__(node_def, op, message)` {#DeadlineExceededError.__init__}
+
+Creates a `DeadlineExceededError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.OutOfRangeError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.OutOfRangeError.md
deleted file mode 100644
index ef996b0a88..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.OutOfRangeError.md
+++ /dev/null
@@ -1,15 +0,0 @@
-Raised when an operation iterates past the valid input range.
-
-This exception is raised in "end-of-file" conditions, such as when a
-[`queue.dequeue()`](../../api_docs/python/io_ops.md#QueueBase.dequeue)
-operation is blocked on an empty queue, and a
-[`queue.close()`](../../api_docs/python/io_ops.md#QueueBase.close)
-operation executes.
-
-- - -
-
-#### `tf.errors.OutOfRangeError.__init__(node_def, op, message)` {#OutOfRangeError.__init__}
-
-Creates an `OutOfRangeError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.PermissionDeniedError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.PermissionDeniedError.md
deleted file mode 100644
index a8a81494c8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.PermissionDeniedError.md
+++ /dev/null
@@ -1,14 +0,0 @@
-Raised when the caller does not have permission to run an operation.
-
-For example, running the
-[`tf.WholeFileReader.read()`](../../api_docs/python/io_ops.md#WholeFileReader)
-operation could raise `PermissionDeniedError` if it receives the name of a
-file for which the user does not have the read file permission.
-
-- - -
-
-#### `tf.errors.PermissionDeniedError.__init__(node_def, op, message)` {#PermissionDeniedError.__init__}
-
-Creates a `PermissionDeniedError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.UnimplementedError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.UnimplementedError.md
deleted file mode 100644
index 945daa1a22..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.errors.UnimplementedError.md
+++ /dev/null
@@ -1,15 +0,0 @@
-Raised when an operation has not been implemented.
-
-Some operations may raise this error when passed otherwise-valid
-arguments that it does not currently support. For example, running
-the [`tf.nn.max_pool()`](../../api_docs/python/nn.md#max_pool) operation
-would raise this error if pooling was requested on the batch dimension,
-because this is not yet supported.
-
-- - -
-
-#### `tf.errors.UnimplementedError.__init__(node_def, op, message)` {#UnimplementedError.__init__}
-
-Creates an `UnimplementedError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.fft3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.fft3d.md
new file mode 100644
index 0000000000..7214e3ae20
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.fft3d.md
@@ -0,0 +1,14 @@
+### `tf.fft3d(input, name=None)` {#fft3d}
+
+Compute the 3-dimensional discrete Fourier Transform.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 3-D tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`. The 3D Fourier Transform of `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.gather.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.gather.md
deleted file mode 100644
index f3ae59bbb6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.gather.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.gather(params, indices, validate_indices=None, name=None)` {#gather}
-
-Gather slices from `params` according to `indices`.
-
-`indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
-Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
-
-    # Scalar indices
-    output[:, ..., :] = params[indices, :, ... :]
-
-    # Vector indices
-    output[i, :, ..., :] = params[indices[i], :, ... :]
-
-    # Higher rank indices
-    output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
-
-If `indices` is a permutation and `len(indices) == params.shape[0]` then
-this operation will permute `params` accordingly.
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/Gather.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`params`</b>: A `Tensor`.
-*  <b>`indices`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-*  <b>`validate_indices`</b>: An optional `bool`. Defaults to `True`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `params`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_collection_ref.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_collection_ref.md
deleted file mode 100644
index c393da2233..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_collection_ref.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.get_collection_ref(key)` {#get_collection_ref}
-
-Wrapper for `Graph.get_collection_ref()` using the default graph.
-
-See [`Graph.get_collection_ref()`](../../api_docs/python/framework.md#Graph.get_collection_ref)
-for more details.
-
-##### Args:
-
-
-*  <b>`key`</b>: The key for the collection. For example, the `GraphKeys` class
-    contains many standard names for collections.
-
-##### Returns:
-
-  The list of values in the collection with the given `name`, or an empty
-  list if no value has been added to that collection.  Note that this returns
-  the collection list itself, which can be modified in place to change the
-  collection.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_seed.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_seed.md
new file mode 100644
index 0000000000..ccf6712418
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_seed.md
@@ -0,0 +1,22 @@
+### `tf.get_seed(op_seed)` {#get_seed}
+
+Returns the local seeds an operation should use given an op-specific seed.
+
+Given operation-specific seed, `op_seed`, this helper function returns two
+seeds derived from graph-level and op-level seeds. Many random operations
+internally use the two seeds to allow user to change the seed globally for a
+graph, or for only specific operations.
+
+For details on how the graph-level seed interacts with op seeds, see
+[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed).
+
+##### Args:
+
+
+*  <b>`op_seed`</b>: integer.
+
+##### Returns:
+
+  A tuple of two integers that should be used for the local seed of this
+  operation.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_session_handle.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_session_handle.md
new file mode 100644
index 0000000000..3fdd2b0ae9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_session_handle.md
@@ -0,0 +1,38 @@
+### `tf.get_session_handle(data, name=None)` {#get_session_handle}
+
+Return the handle of `data`.
+
+This is EXPERIMENTAL and subject to change.
+
+Keep `data` "in-place" in the runtime and create a handle that can be
+used to retrieve `data` in a subsequent run().
+
+Combined with `get_session_tensor`, we can keep a tensor produced in
+one run call in place, and use it as the input in a future run call.
+Below is a simple example:
+
+```python
+c = tf.mul(a, b)
+h = tf.get_session_handle(c)
+h = sess.run(h)
+
+p, a = tf.get_session_tensor(tf.float32)
+b = tf.mul(a, 10)
+c = sess.run(b, feed_dict={p: h.handle})
+```
+
+##### Args:
+
+
+*  <b>`data`</b>: A tensor to be stored in the session.
+*  <b>`name`</b>: Optional name prefix for the return tensor.
+
+##### Returns:
+
+  A scalar string tensor representing a unique handle for `data`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `data` is not a Tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_session_tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_session_tensor.md
deleted file mode 100644
index 215647a989..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.get_session_tensor.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.get_session_tensor(dtype, name=None)` {#get_session_tensor}
-
-Get the tensor of type `dtype` by feeding a tensor handle.
-
-This is EXPERIMENTAL and subject to change.
-
-Get the value of the tensor from a tensor handle. The tensor
-is produced in a previous run() and stored in the state of the
-session.
-
-##### Args:
-
-
-*  <b>`dtype`</b>: The type of the output tensor.
-*  <b>`name`</b>: Optional name prefix for the return tensor.
-
-##### Returns:
-
-  A pair of tensors. The first is a placeholder for feeding a
-  tensor handle and the second is the tensor in the session state
-  keyed by the tensor handle.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.global_norm.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.global_norm.md
deleted file mode 100644
index d37d4228b2..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.global_norm.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.global_norm(t_list, name=None)` {#global_norm}
-
-Computes the global norm of multiple tensors.
-
-Given a tuple or list of tensors `t_list`, this operation returns the
-global norm of the elements in all tensors in `t_list`. The global norm is
-computed as:
-
-`global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))`
-
-Any entries in `t_list` that are of type None are ignored.
-
-##### Args:
-
-
-*  <b>`t_list`</b>: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A 0-D (scalar) `Tensor` of type `float`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `t_list` is not a sequence.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.identity.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.identity.md
new file mode 100644
index 0000000000..13f1318601
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.identity.md
@@ -0,0 +1,14 @@
+### `tf.identity(input, name=None)` {#identity}
+
+Return a tensor with the same shape and contents as the input tensor or value.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.ifft2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.ifft2d.md
deleted file mode 100644
index 0ca8eb8dc1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.ifft2d.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.ifft2d(input, name=None)` {#ifft2d}
-
-Compute the inverse 2-dimensional discrete Fourier Transform.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 matrix.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`.
-  The inverse 2D Fourier Transform of `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.ifft3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.ifft3d.md
new file mode 100644
index 0000000000..35d58888ac
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.ifft3d.md
@@ -0,0 +1,15 @@
+### `tf.ifft3d(input, name=None)` {#ifft3d}
+
+Compute the inverse 3-dimensional discrete Fourier Transform.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 3-D tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`.
+  The inverse 3D Fourier Transform of `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.igammac.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.igammac.md
new file mode 100644
index 0000000000..1b739bcfca
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.igammac.md
@@ -0,0 +1,29 @@
+### `tf.igammac(a, x, name=None)` {#igammac}
+
+Compute the upper regularized incomplete Gamma function `Q(a, x)`.
+
+The upper regularized incomplete Gamma function is defined as:
+
+```
+Q(a, x) = Gamma(a, x) / Gamma(x) = 1 - P(a, x)
+```
+where
+```
+Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt
+```
+is the upper incomplete Gama function.
+
+Note, above `P(a, x)` (`Igamma`) is the lower regularized complete
+Gamma function.
+
+##### Args:
+
+
+*  <b>`a`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`x`</b>: A `Tensor`. Must have the same type as `a`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `a`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.imag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.imag.md
new file mode 100644
index 0000000000..1dfcadbb95
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.imag.md
@@ -0,0 +1,27 @@
+### `tf.imag(input, name=None)` {#imag}
+
+Returns the imaginary part of a complex number.
+
+Given a tensor `input` of complex numbers, this operation returns a tensor of
+type `float` or `double` that is the imaginary part of each element in
+`input`. All elements in `input` must be complex numbers of the form \(a +
+bj\), where *a* is the real part and *b* is the imaginary part returned by
+this operation.
+
+For example:
+
+```
+# tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+tf.imag(input) ==> [4.75, 5.75]
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `float` or `double`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.adjust_saturation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.adjust_saturation.md
new file mode 100644
index 0000000000..1829271ff6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.adjust_saturation.md
@@ -0,0 +1,25 @@
+### `tf.image.adjust_saturation(image, saturation_factor, name=None)` {#adjust_saturation}
+
+Adjust saturation of an RGB image.
+
+This is a convenience method that converts an RGB image to float
+representation, converts it to HSV, add an offset to the saturation channel,
+converts back to RGB and then back to the original data type. If several
+adjustments are chained it is advisable to minimize the number of redundant
+conversions.
+
+`image` is an RGB image.  The image saturation is adjusted by converting the
+image to HSV and multiplying the saturation (S) channel by
+`saturation_factor` and clipping. The image is then converted back to RGB.
+
+##### Args:
+
+
+*  <b>`image`</b>: RGB image or images. Size of the last dimension must be 3.
+*  <b>`saturation_factor`</b>: float. Factor to multiply the saturation by.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  Adjusted image(s), same shape and DType as `image`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.convert_image_dtype.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.convert_image_dtype.md
new file mode 100644
index 0000000000..63db6f36a9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.convert_image_dtype.md
@@ -0,0 +1,32 @@
+### `tf.image.convert_image_dtype(image, dtype, saturate=False, name=None)` {#convert_image_dtype}
+
+Convert `image` to `dtype`, scaling its values if needed.
+
+Images that are represented using floating point values are expected to have
+values in the range [0,1). Image data stored in integer data types are
+expected to have values in the range `[0,MAX]`, where `MAX` is the largest
+positive representable number for the data type.
+
+This op converts between data types, scaling the values appropriately before
+casting.
+
+Note that converting from floating point inputs to integer types may lead to
+over/underflow problems. Set saturate to `True` to avoid such problem in
+problematic conversions. If enabled, saturation will clip the output into the
+allowed range before performing a potentially dangerous cast (and only before
+performing such a cast, i.e., when casting from a floating point to an integer
+type, and when casting from a signed to an unsigned type; `saturate` has no
+effect on casts between floats, or on casts that increase the type's range).
+
+##### Args:
+
+
+*  <b>`image`</b>: An image.
+*  <b>`dtype`</b>: A `DType` to convert `image` to.
+*  <b>`saturate`</b>: If `True`, clip the input before casting (if necessary).
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  `image`, converted to `dtype`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.flip_left_right.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.flip_left_right.md
new file mode 100644
index 0000000000..ac8c99806e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.flip_left_right.md
@@ -0,0 +1,23 @@
+### `tf.image.flip_left_right(image)` {#flip_left_right}
+
+Flip an image horizontally (left to right).
+
+Outputs the contents of `image` flipped along the second dimension, which is
+`width`.
+
+See also `reverse()`.
+
+##### Args:
+
+
+*  <b>`image`</b>: A 3-D tensor of shape `[height, width, channels].`
+
+##### Returns:
+
+  A 3-D tensor of the same type and shape as `image`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the shape of `image` not supported.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.flip_up_down.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.flip_up_down.md
deleted file mode 100644
index ed92277f8a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.flip_up_down.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.image.flip_up_down(image)` {#flip_up_down}
-
-Flip an image horizontally (upside down).
-
-Outputs the contents of `image` flipped along the first dimension, which is
-`height`.
-
-See also `reverse()`.
-
-##### Args:
-
-
-*  <b>`image`</b>: A 3-D tensor of shape `[height, width, channels].`
-
-##### Returns:
-
-  A 3-D tensor of the same type and shape as `image`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if the shape of `image` not supported.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.pad_to_bounding_box.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.pad_to_bounding_box.md
new file mode 100644
index 0000000000..04c155c03c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.pad_to_bounding_box.md
@@ -0,0 +1,30 @@
+### `tf.image.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width)` {#pad_to_bounding_box}
+
+Pad `image` with zeros to the specified `height` and `width`.
+
+Adds `offset_height` rows of zeros on top, `offset_width` columns of
+zeros on the left, and then pads the image on the bottom and right
+with zeros until it has dimensions `target_height`, `target_width`.
+
+This op does nothing if `offset_*` is zero and the image already has size
+`target_height` by `target_width`.
+
+##### Args:
+
+
+*  <b>`image`</b>: 3-D tensor with shape `[height, width, channels]`
+*  <b>`offset_height`</b>: Number of rows of zeros to add on top.
+*  <b>`offset_width`</b>: Number of columns of zeros to add on the left.
+*  <b>`target_height`</b>: Height of output image.
+*  <b>`target_width`</b>: Width of output image.
+
+##### Returns:
+
+  3-D tensor of shape `[target_height, target_width, channels]`
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the shape of `image` is incompatible with the `offset_*` or
+    `target_*` arguments
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.resize_image_with_crop_or_pad.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.resize_image_with_crop_or_pad.md
new file mode 100644
index 0000000000..c93111bd99
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.resize_image_with_crop_or_pad.md
@@ -0,0 +1,30 @@
+### `tf.image.resize_image_with_crop_or_pad(image, target_height, target_width)` {#resize_image_with_crop_or_pad}
+
+Crops and/or pads an image to a target width and height.
+
+Resizes an image to a target width and height by either centrally
+cropping the image or padding it evenly with zeros.
+
+If `width` or `height` is greater than the specified `target_width` or
+`target_height` respectively, this op centrally crops along that dimension.
+If `width` or `height` is smaller than the specified `target_width` or
+`target_height` respectively, this op centrally pads with 0 along that
+dimension.
+
+##### Args:
+
+
+*  <b>`image`</b>: 3-D tensor of shape [height, width, channels]
+*  <b>`target_height`</b>: Target height.
+*  <b>`target_width`</b>: Target width.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `target_height` or `target_width` are zero or negative.
+
+##### Returns:
+
+  Cropped and/or padded image of shape
+  `[target_height, target_width, channels]`
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.resize_nearest_neighbor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.resize_nearest_neighbor.md
new file mode 100644
index 0000000000..ba72e73ebd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.resize_nearest_neighbor.md
@@ -0,0 +1,22 @@
+### `tf.image.resize_nearest_neighbor(images, size, align_corners=None, name=None)` {#resize_nearest_neighbor}
+
+Resize `images` to `size` using nearest neighbor interpolation.
+
+##### Args:
+
+
+*  <b>`images`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
+    4-D with shape `[batch, height, width, channels]`.
+*  <b>`size`</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
+    new size for the images.
+*  <b>`align_corners`</b>: An optional `bool`. Defaults to `False`.
+    If true, rescale input by (new_height - 1) / (height - 1), which
+    exactly aligns the 4 corners of images and resized images. If false, rescale
+    by new_height / height. Treat similarly the width dimension.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `images`. 4-D with shape
+  `[batch, new_height, new_width, channels]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.rgb_to_grayscale.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.rgb_to_grayscale.md
new file mode 100644
index 0000000000..bf9b6846e0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.rgb_to_grayscale.md
@@ -0,0 +1,19 @@
+### `tf.image.rgb_to_grayscale(images, name=None)` {#rgb_to_grayscale}
+
+Converts one or more images from RGB to Grayscale.
+
+Outputs a tensor of the same `DType` and rank as `images`.  The size of the
+last dimension of the output is 1, containing the Grayscale value of the
+pixels.
+
+##### Args:
+
+
+*  <b>`images`</b>: The RGB tensor to convert. Last dimension must have size 3 and
+    should contain RGB values.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The converted grayscale image(s).
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.rgb_to_hsv.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.rgb_to_hsv.md
new file mode 100644
index 0000000000..7c5d05f515
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.image.rgb_to_hsv.md
@@ -0,0 +1,23 @@
+### `tf.image.rgb_to_hsv(images, name=None)` {#rgb_to_hsv}
+
+Converts one or more images from RGB to HSV.
+
+Outputs a tensor of the same shape as the `images` tensor, containing the HSV
+value of the pixels. The output is only well defined if the value in `images`
+are in `[0,1]`.
+
+`output[..., 0]` contains hue, `output[..., 1]` contains saturation, and
+`output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0
+corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue.
+
+##### Args:
+
+
+*  <b>`images`</b>: A `Tensor` of type `float32`.
+    1-D or higher rank. RGB data to convert. Last dimension must be size 3.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `float32`. `images` converted to HSV.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.is_non_decreasing.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.is_non_decreasing.md
deleted file mode 100644
index f10ff932c0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.is_non_decreasing.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.is_non_decreasing(x, name=None)` {#is_non_decreasing}
-
-Returns `True` if `x` is non-decreasing.
-
-Elements of `x` are compared in row-major order.  The tensor `[x[0],...]`
-is non-decreasing if for every adjacent pair we have `x[i] <= x[i+1]`.
-If `x` has less than two elements, it is trivially non-decreasing.
-
-See also:  `is_strictly_increasing`
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`name`</b>: A name for this operation (optional).  Defaults to "is_non_decreasing"
-
-##### Returns:
-
-  Boolean `Tensor`, equal to `True` iff `x` is non-decreasing.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `x` is not a numeric tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.lgamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.lgamma.md
deleted file mode 100644
index 2b8fda7dee..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.lgamma.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.lgamma(x, name=None)` {#lgamma}
-
-Computes the log of the absolute value of `Gamma(x)` element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.linspace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.linspace.md
deleted file mode 100644
index 570845f502..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.linspace.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.linspace(start, stop, num, name=None)` {#linspace}
-
-Generates values in an interval.
-
-A sequence of `num` evenly-spaced values are generated beginning at `start`.
-If `num > 1`, the values in the sequence increase by `stop - start / num - 1`,
-so that the last one is exactly `stop`.
-
-For example:
-
-```
-tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0  11.0  12.0]
-```
-
-##### Args:
-
-
-*  <b>`start`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-    First entry in the range.
-*  <b>`stop`</b>: A `Tensor`. Must have the same type as `start`.
-    Last entry in the range.
-*  <b>`num`</b>: A `Tensor` of type `int32`. Number of values to generate.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `start`. 1-D. The generated values.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.logical_and.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.logical_and.md
new file mode 100644
index 0000000000..dd5b563c8b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.logical_and.md
@@ -0,0 +1,15 @@
+### `tf.logical_and(x, y, name=None)` {#logical_and}
+
+Returns the truth value of x AND y element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` of type `bool`.
+*  <b>`y`</b>: A `Tensor` of type `bool`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.logical_or.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.logical_or.md
deleted file mode 100644
index be18e65e92..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.logical_or.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.logical_or(x, y, name=None)` {#logical_or}
-
-Returns the truth value of x OR y element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` of type `bool`.
-*  <b>`y`</b>: A `Tensor` of type `bool`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.map_fn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.map_fn.md
deleted file mode 100644
index 1892d7b03c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.map_fn.md
+++ /dev/null
@@ -1,42 +0,0 @@
-### `tf.map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#map_fn}
-
-map on the list of tensors unpacked from `elems` on dimension 0.
-
-This map operator repeatedly applies the callable `fn` to a sequence of
-elements from first to last. The elements are made of the tensors unpacked
-from `elems`. `dtype` is the data type of the return value of `fn`. Users
-must provide `dtype` if it is different from the data type of `elems`.
-
-Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
-of the result tensor is `[len(values)] + fn(values[0]).shape`.
-
-##### Args:
-
-
-*  <b>`fn`</b>: The callable to be performed.
-*  <b>`elems`</b>: A tensor to be unpacked to apply `fn`.
-*  <b>`dtype`</b>: (optional) The output type of `fn`.
-*  <b>`parallel_iterations`</b>: (optional) The number of iterations allowed to run
-                       in parallel.
-*  <b>`back_prop`</b>: (optional) True enables back propagation.
-*  <b>`swap_memory`</b>: (optional) True enables GPU-CPU memory swapping.
-*  <b>`name`</b>: (optional) Name prefix for the returned tensors.
-
-##### Returns:
-
-  A tensor that packs the results of applying `fn` to the list of tensors
-  unpacked from `elems`, from first to last.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `fn` is not callable.
-
-##### Example:
-
-  ```python
-  elems = [1, 2, 3, 4, 5, 6]
-  squares = map_fn(lambda x: x * x, elems)
-  # squares == [1, 4, 9, 16, 25, 36]
-  ```
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.maximum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.maximum.md
deleted file mode 100644
index 309946f435..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.maximum.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.maximum(x, y, name=None)` {#maximum}
-
-Returns the max of x and y (i.e. x > y ? x : y) element-wise, broadcasts.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.merge_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.merge_summary.md
new file mode 100644
index 0000000000..b61a501c2d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.merge_summary.md
@@ -0,0 +1,26 @@
+### `tf.merge_summary(inputs, collections=None, name=None)` {#merge_summary}
+
+Merges summaries.
+
+This op creates a
+[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
+protocol buffer that contains the union of all the values in the input
+summaries.
+
+When the Op is run, it reports an `InvalidArgument` error if multiple values
+in the summaries to merge use the same tag.
+
+##### Args:
+
+
+*  <b>`inputs`</b>: A list of `string` `Tensor` objects containing serialized `Summary`
+    protocol buffers.
+*  <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
+    added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A scalar `Tensor` of type `string`. The serialized `Summary` protocol
+  buffer resulting from the merging.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.mul.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.mul.md
deleted file mode 100644
index 3d6fa56864..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.mul.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.mul(x, y, name=None)` {#mul}
-
-Returns x * y element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.neg.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.neg.md
deleted file mode 100644
index 519fd9a875..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.neg.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.neg(x, name=None)` {#neg}
-
-Computes numerical negative value element-wise.
-
-I.e., \\(y = -x\\).
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.atrous_conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.atrous_conv2d.md
deleted file mode 100644
index cf4c473689..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.atrous_conv2d.md
+++ /dev/null
@@ -1,107 +0,0 @@
-### `tf.nn.atrous_conv2d(value, filters, rate, padding, name=None)` {#atrous_conv2d}
-
-Atrous convolution (a.k.a. convolution with holes or dilated convolution).
-
-Computes a 2-D atrous convolution, also known as convolution with holes or
-dilated convolution, given 4-D `value` and `filters` tensors. If the `rate`
-parameter is equal to one, it performs regular 2-D convolution. If the `rate`
-parameter is greater than one, it performs convolution with holes, sampling
-the input values every `rate` pixels in the `height` and `width` dimensions.
-This is equivalent to convolving the input with a set of upsampled filters,
-produced by inserting `rate - 1` zeros between two consecutive values of the
-filters along the `height` and `width` dimensions, hence the name atrous
-convolution or convolution with holes (the French word trous means holes in
-English).
-
-More specifically:
-
-    output[b, i, j, k] = sum_{di, dj, q} filters[di, dj, q, k] *
-          value[b, i + rate * di, j + rate * dj, q]
-
-Atrous convolution allows us to explicitly control how densely to compute
-feature responses in fully convolutional networks. Used in conjunction with
-bilinear interpolation, it offers an alternative to `conv2d_transpose` in
-dense prediction tasks such as semantic image segmentation, optical flow
-computation, or depth estimation. It also allows us to effectively enlarge
-the field of view of filters without increasing the number of parameters or
-the amount of computation.
-
-For a description of atrous convolution and how it can be used for dense
-feature extraction, please see: [Semantic Image Segmentation with Deep
-Convolutional Nets and Fully Connected CRFs](http://arxiv.org/abs/1412.7062).
-The same operation is investigated further in [Multi-Scale Context Aggregation
-by Dilated Convolutions](http://arxiv.org/abs/1511.07122). Previous works
-that effectively use atrous convolution in different ways are, among others,
-[OverFeat: Integrated Recognition, Localization and Detection using
-Convolutional Networks](http://arxiv.org/abs/1312.6229) and [Fast Image
-Scanning with Deep Max-Pooling Convolutional Neural Networks]
-(http://arxiv.org/abs/1302.1700). Atrous convolution is also closely related
-to the so-called noble identities in multi-rate signal processing.
-
-There are many different ways to implement atrous convolution (see the refs
-above). The implementation here reduces
-
-    atrous_conv2d(value, filters, rate, padding=padding)
-
-to the following three operations:
-
-    paddings = ...
-    net = space_to_batch(value, paddings, block_size=rate)
-    net = conv2d(net, filters, strides=[1, 1, 1, 1], padding="VALID")
-    crops = ...
-    net = batch_to_space(net, crops, block_size=rate)
-
-Advanced usage. Note the following optimization: A sequence of `atrous_conv2d`
-operations with identical `rate` parameters, 'SAME' `padding`, and filters
-with odd heights/ widths:
-
-    net = atrous_conv2d(net, filters1, rate, padding="SAME")
-    net = atrous_conv2d(net, filters2, rate, padding="SAME")
-    ...
-    net = atrous_conv2d(net, filtersK, rate, padding="SAME")
-
-can be equivalently performed cheaper in terms of computation and memory as:
-
-    pad = ...  # padding so that the input dims are multiples of rate
-    net = space_to_batch(net, paddings=pad, block_size=rate)
-    net = conv2d(net, filters1, strides=[1, 1, 1, 1], padding="SAME")
-    net = conv2d(net, filters2, strides=[1, 1, 1, 1], padding="SAME")
-    ...
-    net = conv2d(net, filtersK, strides=[1, 1, 1, 1], padding="SAME")
-    net = batch_to_space(net, crops=pad, block_size=rate)
-
-because a pair of consecutive `space_to_batch` and `batch_to_space` ops with
-the same `block_size` cancel out when their respective `paddings` and `crops`
-inputs are identical.
-
-##### Args:
-
-
-*  <b>`value`</b>: A 4-D `Tensor` of type `float`. It needs to be in the default "NHWC"
-    format. Its shape is `[batch, in_height, in_width, in_channels]`.
-*  <b>`filters`</b>: A 4-D `Tensor` with the same type as `value` and shape
-    `[filter_height, filter_width, in_channels, out_channels]`. `filters`'
-    `in_channels` dimension must match that of `value`. Atrous convolution is
-    equivalent to standard convolution with upsampled filters with effective
-    height `filter_height + (filter_height - 1) * (rate - 1)` and effective
-    width `filter_width + (filter_width - 1) * (rate - 1)`, produced by
-    inserting `rate - 1` zeros along consecutive elements across the
-    `filters`' spatial dimensions.
-*  <b>`rate`</b>: A positive int32. The stride with which we sample input values across
-    the `height` and `width` dimensions. Equivalently, the rate by which we
-    upsample the filter values by inserting zeros across the `height` and
-    `width` dimensions. In the literature, the same parameter is sometimes
-    called `input stride` or `dilation`.
-*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
-*  <b>`name`</b>: Optional name for the returned tensor.
-
-##### Returns:
-
-  A `Tensor` with the same type as `value`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If input/output depth does not match `filters`' shape, or if
-    padding is other than `'VALID'` or `'SAME'`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.conv3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.conv3d.md
new file mode 100644
index 0000000000..886744c595
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.conv3d.md
@@ -0,0 +1,29 @@
+### `tf.nn.conv3d(input, filter, strides, padding, name=None)` {#conv3d}
+
+Computes a 3-D convolution given 5-D `input` and `filter` tensors.
+
+In signal processing, cross-correlation is a measure of similarity of
+two waveforms as a function of a time-lag applied to one of them. This
+is also known as a sliding dot product or sliding inner-product.
+
+Our Conv3D implements a form of cross-correlation.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+    Shape `[batch, in_depth, in_height, in_width, in_channels]`.
+*  <b>`filter`</b>: A `Tensor`. Must have the same type as `input`.
+    Shape `[filter_depth, filter_height, filter_width, in_channels, out_channels]`.
+    `in_channels` must match between `input` and `filter`.
+*  <b>`strides`</b>: A list of `ints` that has length `>= 5`.
+    1-D tensor of length 5. The stride of the sliding window for each
+    dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
+    The type of padding algorithm to use.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.depthwise_conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.depthwise_conv2d.md
deleted file mode 100644
index 7bacc20da8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.depthwise_conv2d.md
+++ /dev/null
@@ -1,37 +0,0 @@
-### `tf.nn.depthwise_conv2d(input, filter, strides, padding, name=None)` {#depthwise_conv2d}
-
-Depthwise 2-D convolution.
-
-Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
-and a filter tensor of shape
-`[filter_height, filter_width, in_channels, channel_multiplier]`
-containing `in_channels` convolutional filters of depth 1, `depthwise_conv2d`
-applies a different filter to each input channel (expanding from 1 channel
-to `channel_multiplier` channels for each), then concatenates the results
-together.  The output has `in_channels * channel_multiplier` channels.
-
-In detail,
-
-    output[b, i, j, k * channel_multiplier + q] =
-        sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
-                     filter[di, dj, k, q]
-
-Must have `strides[0] = strides[3] = 1`.  For the most common case of the
-same horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
-
-##### Args:
-
-
-*  <b>`input`</b>: 4-D with shape `[batch, in_height, in_width, in_channels]`.
-*  <b>`filter`</b>: 4-D with shape
-    `[filter_height, filter_width, in_channels, channel_multiplier]`.
-*  <b>`strides`</b>: 1-D of size 4.  The stride of the sliding window for each
-    dimension of `input`.
-*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`.  The padding algorithm.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  A 4-D `Tensor` of shape
-  `[batch, out_height, out_width, in_channels * channel_multiplier].`
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.max_pool.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.max_pool.md
new file mode 100644
index 0000000000..f17efa01de
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.max_pool.md
@@ -0,0 +1,21 @@
+### `tf.nn.max_pool(value, ksize, strides, padding, data_format='NHWC', name=None)` {#max_pool}
+
+Performs the max pooling on the input.
+
+##### Args:
+
+
+*  <b>`value`</b>: A 4-D `Tensor` with shape `[batch, height, width, channels]` and
+    type `tf.float32`.
+*  <b>`ksize`</b>: A list of ints that has length >= 4.  The size of the window for
+    each dimension of the input tensor.
+*  <b>`strides`</b>: A list of ints that has length >= 4.  The stride of the sliding
+    window for each dimension of the input tensor.
+*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
+*  <b>`data_format`</b>: A string. 'NHWC' and 'NCHW' are supported.
+*  <b>`name`</b>: Optional name for the operation.
+
+##### Returns:
+
+  A `Tensor` with type `tf.float32`.  The max pooled output tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.max_pool_with_argmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.max_pool_with_argmax.md
new file mode 100644
index 0000000000..0bf84c16d0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.max_pool_with_argmax.md
@@ -0,0 +1,30 @@
+### `tf.nn.max_pool_with_argmax(input, ksize, strides, padding, Targmax=None, name=None)` {#max_pool_with_argmax}
+
+Performs max pooling on the input and outputs both max values and indices.
+
+The indices in `argmax` are flattened, so that a maximum value at position
+`[b, y, x, c]` becomes flattened index
+`((b * height + y) * width + x) * channels + c`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `float32`.
+    4-D with shape `[batch, height, width, channels]`.  Input to pool over.
+*  <b>`ksize`</b>: A list of `ints` that has length `>= 4`.
+    The size of the window for each dimension of the input tensor.
+*  <b>`strides`</b>: A list of `ints` that has length `>= 4`.
+    The stride of the sliding window for each dimension of the
+    input tensor.
+*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
+    The type of padding algorithm to use.
+*  <b>`Targmax`</b>: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int64`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of `Tensor` objects (output, argmax).
+
+*  <b>`output`</b>: A `Tensor` of type `float32`. The max pooled output tensor.
+*  <b>`argmax`</b>: A `Tensor` of type `Targmax`. 4-D.  The flattened indices of the max values chosen for each output.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.moments.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.moments.md
deleted file mode 100644
index 704bb5ba49..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.moments.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.nn.moments(x, axes, shift=None, name=None, keep_dims=False)` {#moments}
-
-Calculate the mean and variance of `x`.
-
-The mean and variance are calculated by aggregating the contents of `x`
-across `axes`.  If `x` is 1-D and `axes = [0]` this is just the mean
-and variance of a vector.
-
-When using these moments for batch normalization (see
-`tf.nn.batch_normalization`):
-  * for so-called "global normalization", used with convolutional filters with
-    shape `[batch, height, width, depth]`, pass `axes=[0, 1, 2]`.
-  * for simple batch normalization pass `axes=[0]` (batch only).
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`.
-*  <b>`axes`</b>: array of ints.  Axes along which to compute mean and
-    variance.
-*  <b>`shift`</b>: A `Tensor` containing the value by which to shift the data for
-    numerical stability, or `None` if no shift is to be performed. A shift
-    close to the true mean provides the most numerically stable results.
-*  <b>`keep_dims`</b>: produce moments with the same dimensionality as the input.
-*  <b>`name`</b>: Name used to scope the operations that compute the moments.
-
-##### Returns:
-
-  Two `Tensor` objects: `mean` and `variance`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.sampled_softmax_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.sampled_softmax_loss.md
new file mode 100644
index 0000000000..6d22f67352
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.sampled_softmax_loss.md
@@ -0,0 +1,49 @@
+### `tf.nn.sampled_softmax_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=True, partition_strategy='mod', name='sampled_softmax_loss')` {#sampled_softmax_loss}
+
+Computes and returns the sampled softmax training loss.
+
+This is a faster way to train a softmax classifier over a huge number of
+classes.
+
+This operation is for training only.  It is generally an underestimate of
+the full softmax loss.
+
+At inference time, you can compute full softmax probabilities with the
+expression `tf.nn.softmax(tf.matmul(inputs, tf.transpose(weights)) + biases)`.
+
+See our [Candidate Sampling Algorithms Reference]
+(../../extras/candidate_sampling.pdf)
+
+Also see Section 3 of [Jean et al., 2014](http://arxiv.org/abs/1412.2007)
+([pdf](http://arxiv.org/pdf/1412.2007.pdf)) for the math.
+
+##### Args:
+
+
+*  <b>`weights`</b>: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
+      objects whose concatenation along dimension 0 has shape
+      [num_classes, dim].  The (possibly-sharded) class embeddings.
+*  <b>`biases`</b>: A `Tensor` of shape `[num_classes]`.  The class biases.
+*  <b>`inputs`</b>: A `Tensor` of shape `[batch_size, dim]`.  The forward
+      activations of the input network.
+*  <b>`labels`</b>: A `Tensor` of type `int64` and shape `[batch_size,
+      num_true]`. The target classes.  Note that this format differs from
+      the `labels` argument of `nn.softmax_cross_entropy_with_logits`.
+*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
+*  <b>`num_classes`</b>: An `int`. The number of possible classes.
+*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
+*  <b>`sampled_values`</b>: a tuple of (`sampled_candidates`, `true_expected_count`,
+      `sampled_expected_count`) returned by a `*_candidate_sampler` function.
+      (if None, we default to `log_uniform_candidate_sampler`)
+*  <b>`remove_accidental_hits`</b>: A `bool`.  whether to remove "accidental hits"
+      where a sampled class equals one of the target classes.  Default is
+      True.
+*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy, relevant
+      if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
+      Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `batch_size` 1-D tensor of per-example sampled softmax losses.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.softmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.softmax.md
new file mode 100644
index 0000000000..be31bb2093
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.softmax.md
@@ -0,0 +1,19 @@
+### `tf.nn.softmax(logits, name=None)` {#softmax}
+
+Computes softmax activations.
+
+For each batch `i` and class `j` we have
+
+    softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))
+
+##### Args:
+
+
+*  <b>`logits`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+    2-D with shape `[batch_size, num_classes]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `logits`. Same shape as `logits`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.sufficient_statistics.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.sufficient_statistics.md
new file mode 100644
index 0000000000..92cb5596e6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.sufficient_statistics.md
@@ -0,0 +1,27 @@
+### `tf.nn.sufficient_statistics(x, axes, shift=None, keep_dims=False, name=None)` {#sufficient_statistics}
+
+Calculate the sufficient statistics for the mean and variance of `x`.
+
+These sufficient statistics are computed using the one pass algorithm on
+an input that's optionally shifted. See:
+https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`.
+*  <b>`axes`</b>: Array of ints. Axes along which to compute mean and variance.
+*  <b>`shift`</b>: A `Tensor` containing the value by which to shift the data for
+    numerical stability, or `None` if no shift is to be performed. A shift
+    close to the true mean provides the most numerically stable results.
+*  <b>`keep_dims`</b>: produce statistics with the same dimensionality as the input.
+*  <b>`name`</b>: Name used to scope the operations that compute the sufficient stats.
+
+##### Returns:
+
+  Four `Tensor` objects of the same type as `x`:
+  * the count (number of elements to average over).
+  * the (possibly shifted) sum of the elements in the array.
+  * the (possibly shifted) sum of squares of the elements in the array.
+  * the shift by which the mean must be corrected or None if `shift` is None.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.weighted_cross_entropy_with_logits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.weighted_cross_entropy_with_logits.md
deleted file mode 100644
index 697de67936..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.weighted_cross_entropy_with_logits.md
+++ /dev/null
@@ -1,52 +0,0 @@
-### `tf.nn.weighted_cross_entropy_with_logits(logits, targets, pos_weight, name=None)` {#weighted_cross_entropy_with_logits}
-
-Computes a weighted cross entropy.
-
-This is like `sigmoid_cross_entropy_with_logits()` except that `pos_weight`,
-allows one to trade off recall and precision by up- or down-weighting the
-cost of a positive error relative to a negative error.
-
-The usual cross-entropy cost is defined as:
-
-  targets * -log(sigmoid(logits)) + (1 - targets) * -log(1 - sigmoid(logits))
-
-The argument `pos_weight` is used as a multiplier for the positive targets:
-
-  targets * -log(sigmoid(logits)) * pos_weight +
-      (1 - targets) * -log(1 - sigmoid(logits))
-
-For brevity, let `x = logits`, `z = targets`, `q = pos_weight`.
-The loss is:
-
-      qz * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
-    = qz * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
-    = qz * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
-    = qz * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
-    = (1 - z) * x + (qz +  1 - z) * log(1 + exp(-x))
-    = (1 - z) * x + (1 + (q - 1) * z) * log(1 + exp(-x))
-
-Setting `l = (1 + (q - 1) * z)`, to ensure stability and avoid overflow,
-the implementation uses
-
-    (1 - z) * x + l * (log(1 + exp(-abs(x))) + max(-x, 0))
-
-`logits` and `targets` must have the same type and shape.
-
-##### Args:
-
-
-*  <b>`logits`</b>: A `Tensor` of type `float32` or `float64`.
-*  <b>`targets`</b>: A `Tensor` of the same type and shape as `logits`.
-*  <b>`pos_weight`</b>: A coefficient to use on the positive examples.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of the same shape as `logits` with the componentwise
-  weightedlogistic losses.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `logits` and `targets` do not have the same shape.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.not_equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.not_equal.md
deleted file mode 100644
index 9c18792223..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.not_equal.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.not_equal(x, y, name=None)` {#not_equal}
-
-Returns the truth value of (x != y) element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `quint8`, `qint8`, `qint32`, `string`, `bool`, `complex128`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.pad.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.pad.md
new file mode 100644
index 0000000000..7fbf7442c7
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.pad.md
@@ -0,0 +1,57 @@
+### `tf.pad(tensor, paddings, mode='CONSTANT', name=None)` {#pad}
+
+Pads a tensor.
+
+This operation pads a `tensor` according to the `paddings` you specify.
+`paddings` is an integer tensor with shape `[n, 2]`, where n is the rank of
+`tensor`. For each dimension D of `input`, `paddings[D, 0]` indicates how
+many values to add before the contents of `tensor` in that dimension, and
+`paddings[D, 1]` indicates how many values to add after the contents of
+`tensor` in that dimension. If `mode` is "REFLECT" then both `paddings[D, 0]`
+and `paddings[D, 1]` must be no greater than `tensor.dim_size(D) - 1`. If
+`mode` is "SYMMETRIC" then both `paddings[D, 0]` and `paddings[D, 1]` must be
+no greater than `tensor.dim_size(D)`.
+
+The padded size of each dimension D of the output is:
+
+`paddings[D, 0] + tensor.dim_size(D) + paddings[D, 1]`
+
+For example:
+
+```python
+# 't' is [[1, 2, 3], [4, 5, 6]].
+# 'paddings' is [[1, 1,], [2, 2]].
+# rank of 't' is 2.
+pad(t, paddings, "CONSTANT") ==> [[0, 0, 0, 0, 0, 0, 0],
+                                  [0, 0, 1, 2, 3, 0, 0],
+                                  [0, 0, 4, 5, 6, 0, 0],
+                                  [0, 0, 0, 0, 0, 0, 0]]
+
+pad(t, paddings, "REFLECT") ==> [[6, 5, 4, 5, 6, 5, 4],
+                                 [3, 2, 1, 2, 3, 2, 1],
+                                 [6, 5, 4, 5, 6, 5, 4],
+                                 [3, 2, 1, 2, 3, 2, 1]]
+
+pad(t, paddings, "SYMMETRIC") ==> [[2, 1, 1, 2, 3, 3, 2],
+                                   [2, 1, 1, 2, 3, 3, 2],
+                                   [5, 4, 4, 5, 6, 6, 5],
+                                   [5, 4, 4, 5, 6, 6, 5]]
+```
+
+##### Args:
+
+
+*  <b>`tensor`</b>: A `Tensor`.
+*  <b>`paddings`</b>: A `Tensor` of type `int32`.
+*  <b>`mode`</b>: One of "CONSTANT", "REFLECT", or "SYMMETRIC".
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `tensor`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC".
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.parse_example.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.parse_example.md
deleted file mode 100644
index 2f2f511196..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.parse_example.md
+++ /dev/null
@@ -1,153 +0,0 @@
-### `tf.parse_example(serialized, features, name=None, example_names=None)` {#parse_example}
-
-Parses `Example` protos into a `dict` of tensors.
-
-Parses a number of serialized [`Example`]
-(https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
-protos given in `serialized`.
-
-`example_names` may contain descriptive names for the corresponding serialized
-protos. These may be useful for debugging purposes, but they have no effect on
-the output. If not `None`, `example_names` must be the same length as `serialized`.
-
-This op parses serialized examples into a dictionary mapping keys to `Tensor`
-and `SparseTensor` objects. `features` is a dict from keys to `VarLenFeature`
-and `FixedLenFeature` objects. Each `VarLenFeature` is mapped to a
-`SparseTensor`, and each `FixedLenFeature` is mapped to a `Tensor`.
-
-Each `VarLenFeature` maps to a `SparseTensor` of the specified type
-representing a ragged matrix. Its indices are `[batch, index]` where `batch`
-is the batch entry the value is from in `serialized`, and `index` is the
-value's index in the list of values associated with that feature and example.
-
-Each `FixedLenFeature` `df` maps to a `Tensor` of the specified type (or
-`tf.float32` if not specified) and shape `(serialized.size(),) + df.shape`.
-
-`FixedLenFeature` entries with a `default_value` are optional. With no default
-value, we will fail if that `Feature` is missing from any example in
-`serialized`.
-
-Examples:
-
-For example, if one expects a `tf.float32` sparse feature `ft` and three
-serialized `Example`s are provided:
-
-```
-serialized = [
-  features
-    { feature { key: "ft" value { float_list { value: [1.0, 2.0] } } } },
-  features
-    { feature []},
-  features
-    { feature { key: "ft" value { float_list { value: [3.0] } } }
-]
-```
-
-then the output will look like:
-
-```
-{"ft": SparseTensor(indices=[[0, 0], [0, 1], [2, 0]],
-                    values=[1.0, 2.0, 3.0],
-                    shape=(3, 2)) }
-```
-
-Given two `Example` input protos in `serialized`:
-
-```
-[
-  features {
-    feature { key: "kw" value { bytes_list { value: [ "knit", "big" ] } } }
-    feature { key: "gps" value { float_list { value: [] } } }
-  },
-  features {
-    feature { key: "kw" value { bytes_list { value: [ "emmy" ] } } }
-    feature { key: "dank" value { int64_list { value: [ 42 ] } } }
-    feature { key: "gps" value { } }
-  }
-]
-```
-
-And arguments
-
-```
-example_names: ["input0", "input1"],
-features: {
-    "kw": VarLenFeature(tf.string),
-    "dank": VarLenFeature(tf.int64),
-    "gps": VarLenFeature(tf.float32),
-}
-```
-
-Then the output is a dictionary:
-
-```python
-{
-  "kw": SparseTensor(
-      indices=[[0, 0], [0, 1], [1, 0]],
-      values=["knit", "big", "emmy"]
-      shape=[2, 2]),
-  "dank": SparseTensor(
-      indices=[[1, 0]],
-      values=[42],
-      shape=[2, 1]),
-  "gps": SparseTensor(
-      indices=[],
-      values=[],
-      shape=[2, 0]),
-}
-```
-
-For dense results in two serialized `Example`s:
-
-```
-[
-  features {
-    feature { key: "age" value { int64_list { value: [ 0 ] } } }
-    feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
-   },
-   features {
-    feature { key: "age" value { int64_list { value: [] } } }
-    feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
-  }
-]
-```
-
-We can use arguments:
-
-```
-example_names: ["input0", "input1"],
-features: {
-    "age": FixedLenFeature([], dtype=tf.int64, default_value=-1),
-    "gender": FixedLenFeature([], dtype=tf.string),
-}
-```
-
-And the expected output is:
-
-```python
-{
-  "age": [[0], [-1]],
-  "gender": [["f"], ["f"]],
-}
-```
-
-##### Args:
-
-
-*  <b>`serialized`</b>: A vector (1-D Tensor) of strings, a batch of binary
-    serialized `Example` protos.
-*  <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
-    `VarLenFeature` values.
-*  <b>`name`</b>: A name for this operation (optional).
-*  <b>`example_names`</b>: A vector (1-D Tensor) of strings (optional), the names of
-    the serialized protos in the batch.
-
-##### Returns:
-
-  A `dict` mapping feature keys to `Tensor` and `SparseTensor` values.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if any feature is invalid.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.placeholder.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.placeholder.md
new file mode 100644
index 0000000000..28cdc11cce
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.placeholder.md
@@ -0,0 +1,34 @@
+### `tf.placeholder(dtype, shape=None, name=None)` {#placeholder}
+
+Inserts a placeholder for a tensor that will be always fed.
+
+**Important**: This tensor will produce an error if evaluated. Its value must
+be fed using the `feed_dict` optional argument to `Session.run()`,
+`Tensor.eval()`, or `Operation.run()`.
+
+For example:
+
+```python
+x = tf.placeholder(tf.float32, shape=(1024, 1024))
+y = tf.matmul(x, x)
+
+with tf.Session() as sess:
+  print(sess.run(y))  # ERROR: will fail because x was not fed.
+
+  rand_array = np.random.rand(1024, 1024)
+  print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.
+```
+
+##### Args:
+
+
+*  <b>`dtype`</b>: The type of elements in the tensor to be fed.
+*  <b>`shape`</b>: The shape of the tensor to be fed (optional). If the shape is not
+    specified, you can feed a tensor of any shape.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` that may be used as a handle for feeding a value, but not
+  evaluated directly.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.python_io.tf_record_iterator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.python_io.tf_record_iterator.md
new file mode 100644
index 0000000000..f5e90ea422
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.python_io.tf_record_iterator.md
@@ -0,0 +1,18 @@
+### `tf.python_io.tf_record_iterator(path)` {#tf_record_iterator}
+
+An iterator that read the records from a TFRecords file.
+
+##### Args:
+
+
+*  <b>`path`</b>: The path to the TFRecords file.
+
+##### Yields:
+
+  Strings.
+
+##### Raises:
+
+
+*  <b>`IOError`</b>: If `path` cannot be opened for reading.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.read_file.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.read_file.md
new file mode 100644
index 0000000000..3c0ad3652a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.read_file.md
@@ -0,0 +1,14 @@
+### `tf.read_file(filename, name=None)` {#read_file}
+
+Reads and outputs the entire contents of the input filename.
+
+##### Args:
+
+
+*  <b>`filename`</b>: A `Tensor` of type `string`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `string`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.real.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.real.md
new file mode 100644
index 0000000000..3be066f588
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.real.md
@@ -0,0 +1,28 @@
+### `tf.real(input, name=None)` {#real}
+
+Returns the real part of a complex number.
+
+Given a tensor `input` of complex numbers, this operation returns a tensor of
+type `float` or `double` that is the real part of each element in `input`.
+All elements in `input` must be complex numbers of the form \(a + bj\),
+where *a* is the real part returned by this operation and *b* is the
+imaginary part.
+
+For example:
+
+```
+# tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
+tf.real(input) ==> [-2.25, 3.25]
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `complex64`,
+       `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `float` or `double`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.reshape.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.reshape.md
deleted file mode 100644
index 057b29e91f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.reshape.md
+++ /dev/null
@@ -1,72 +0,0 @@
-### `tf.reshape(tensor, shape, name=None)` {#reshape}
-
-Reshapes a tensor.
-
-Given `tensor`, this operation returns a tensor that has the same values
-as `tensor` with shape `shape`.
-
-If one component of `shape` is the special value -1, the size of that dimension
-is computed so that the total size remains constant.  In particular, a `shape`
-of `[-1]` flattens into 1-D.  At most one component of `shape` can be -1.
-
-If `shape` is 1-D or higher, then the operation returns a tensor with shape
-`shape` filled with the values of `tensor`. In this case, the number of elements
-implied by `shape` must be the same as the number of elements in `tensor`.
-
-For example:
-
-```prettyprint
-# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
-# tensor 't' has shape [9]
-reshape(t, [3, 3]) ==> [[1, 2, 3],
-                        [4, 5, 6],
-                        [7, 8, 9]]
-
-# tensor 't' is [[[1, 1], [2, 2]],
-#                [[3, 3], [4, 4]]]
-# tensor 't' has shape [2, 2, 2]
-reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
-                        [3, 3, 4, 4]]
-
-# tensor 't' is [[[1, 1, 1],
-#                 [2, 2, 2]],
-#                [[3, 3, 3],
-#                 [4, 4, 4]],
-#                [[5, 5, 5],
-#                 [6, 6, 6]]]
-# tensor 't' has shape [3, 2, 3]
-# pass '[-1]' to flatten 't'
-reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
-
-# -1 can also be used to infer the shape
-
-# -1 is inferred to be 9:
-reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
-                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
-# -1 is inferred to be 2:
-reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
-                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
-# -1 is inferred to be 3:
-reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
-                              [2, 2, 2],
-                              [3, 3, 3]],
-                             [[4, 4, 4],
-                              [5, 5, 5],
-                              [6, 6, 6]]]
-
-# tensor 't' is [7]
-# shape `[]` reshapes to a scalar
-reshape(t, []) ==> 7
-```
-
-##### Args:
-
-
-*  <b>`tensor`</b>: A `Tensor`.
-*  <b>`shape`</b>: A `Tensor` of type `int32`. Defines the shape of the output tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.scatter_update.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.scatter_update.md
new file mode 100644
index 0000000000..f865b8e9e8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.scatter_update.md
@@ -0,0 +1,46 @@
+### `tf.scatter_update(ref, indices, updates, use_locking=None, name=None)` {#scatter_update}
+
+Applies sparse updates to a variable reference.
+
+This operation computes
+
+    # Scalar indices
+    ref[indices, ...] = updates[...]
+
+    # Vector indices (for each i)
+    ref[indices[i], ...] = updates[i, ...]
+
+    # High rank indices (for each i, ..., j)
+    ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
+
+This operation outputs `ref` after the update is done.
+This makes it easier to chain operations that need to use the reset value.
+
+If values in `ref` is to be updated more than once, because there are
+duplicate entires in `indices`, the order at which the updates happen
+for each value is undefined.
+
+Requires `updates.shape = indices.shape + ref.shape[1:]`.
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/ScatterUpdate.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`ref`</b>: A mutable `Tensor`. Should be from a `Variable` node.
+*  <b>`indices`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    A tensor of indices into the first dimension of `ref`.
+*  <b>`updates`</b>: A `Tensor`. Must have the same type as `ref`.
+    A tensor of updated values to store in `ref`.
+*  <b>`use_locking`</b>: An optional `bool`. Defaults to `True`.
+    If True, the assignment will be protected by a lock;
+    otherwise the behavior is undefined, but may exhibit less contention.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  Same as `ref`.  Returned as a convenience for operations that want
+  to use the updated values after the update is done.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.segment_prod.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.segment_prod.md
deleted file mode 100644
index c9ed2759cf..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.segment_prod.md
+++ /dev/null
@@ -1,31 +0,0 @@
-### `tf.segment_prod(data, segment_ids, name=None)` {#segment_prod}
-
-Computes the product along segments of a tensor.
-
-Read [the section on
-Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
-of segments.
-
-Computes a tensor such that
-\\(output_i = \prod_j data_j\\) where the product is over `j` such
-that `segment_ids[j] == i`.
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/SegmentProd.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    A 1-D tensor whose rank is equal to the rank of `data`'s
-    first dimension.  Values should be sorted and can be repeated.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `data`.
-  Has same shape as data, except for dimension 0 which
-  has size `k`, the number of segments.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.select.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.select.md
new file mode 100644
index 0000000000..b77c9612e8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.select.md
@@ -0,0 +1,56 @@
+### `tf.select(condition, t, e, name=None)` {#select}
+
+Selects elements from `t` or `e`, depending on `condition`.
+
+The `t`, and `e` tensors must all have the same shape,
+and the output will also have that shape.  The `condition` tensor
+must be a scalar if `t` and `e` are scalars.  If `t` and `e` are vectors
+or higher rank, then `condition` must be either a vector with size
+matching the first dimension of `t`, or must have the same shape as `t`.
+
+The `condition` tensor acts as a mask that chooses, based on the value at each
+element, whether the corresponding element / row in the output should be
+taken from `t` (if true) or `e` (if false).
+
+If `condition` is a vector and `t` and `e` are higher rank matrices, then
+it chooses which row (outer dimension) to copy from `t` and `e`.
+If `condition` has the same shape as `t` and `e`, then it chooses which
+element to copy from `t` and `e`.
+
+For example:
+
+```prettyprint
+# 'condition' tensor is [[True,  False]
+#                        [False, True]]
+# 't' is [[1, 2],
+#         [3, 4]]
+# 'e' is [[5, 6],
+#         [7, 8]]
+select(condition, t, e) ==> [[1, 6],
+                             [7, 4]]
+
+
+# 'condition' tensor is [True, False]
+# 't' is [[1, 2],
+#         [3, 4]]
+# 'e' is [[5, 6],
+#         [7, 8]]
+select(condition, t, e) ==> [[1, 2],
+                             [7, 8]]
+
+```
+
+##### Args:
+
+
+*  <b>`condition`</b>: A `Tensor` of type `bool`.
+*  <b>`t`</b>: A `Tensor` which may have the same shape as `condition`.
+    If `condition` is rank 1, `t` may have higher rank,
+    but its first dimension must match the size of `condition`.
+*  <b>`e`</b>: A `Tensor` with the same type and shape as `t`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` with the same type and shape as `t` and `e`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sin.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sin.md
deleted file mode 100644
index aeeaf0c7e6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sin.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.sin(x, name=None)` {#sin}
-
-Computes sin of x element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.slice.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.slice.md
deleted file mode 100644
index 6da47df0b0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.slice.md
+++ /dev/null
@@ -1,47 +0,0 @@
-### `tf.slice(input_, begin, size, name=None)` {#slice}
-
-Extracts a slice from a tensor.
-
-This operation extracts a slice of size `size` from a tensor `input` starting
-at the location specified by `begin`. The slice `size` is represented as a
-tensor shape, where `size[i]` is the number of elements of the 'i'th dimension
-of `input` that you want to slice. The starting location (`begin`) for the
-slice is represented as an offset in each dimension of `input`. In other
-words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
-want to slice from.
-
-`begin` is zero-based; `size` is one-based. If `size[i]` is -1,
-all remaining elements in dimension i are included in the
-slice. In other words, this is equivalent to setting:
-
-`size[i] = input.dim_size(i) - begin[i]`
-
-This operation requires that:
-
-`0 <= begin[i] <= begin[i] + size[i] <= Di  for i in [0, n]`
-
-For example:
-
-```
-# 'input' is [[[1, 1, 1], [2, 2, 2]],
-#             [[3, 3, 3], [4, 4, 4]],
-#             [[5, 5, 5], [6, 6, 6]]]
-tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]]
-tf.slice(input, [1, 0, 0], [1, 2, 3]) ==> [[[3, 3, 3],
-                                            [4, 4, 4]]]
-tf.slice(input, [1, 0, 0], [2, 1, 3]) ==> [[[3, 3, 3]],
-                                           [[5, 5, 5]]]
-```
-
-##### Args:
-
-
-*  <b>`input_`</b>: A `Tensor`.
-*  <b>`begin`</b>: An `int32` or `int64` `Tensor`.
-*  <b>`size`</b>: An `int32` or `int64` `Tensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sparse_concat.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sparse_concat.md
new file mode 100644
index 0000000000..8d05472e34
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sparse_concat.md
@@ -0,0 +1,100 @@
+### `tf.sparse_concat(concat_dim, sp_inputs, name=None, expand_nonconcat_dim=False)` {#sparse_concat}
+
+Concatenates a list of `SparseTensor` along the specified dimension.
+
+Concatenation is with respect to the dense versions of each sparse input.
+It is assumed that each inputs is a `SparseTensor` whose elements are ordered
+along increasing dimension number.
+
+If expand_nonconcat_dim is False, all inputs' shapes must match, except for
+the concat dimension. If expand_nonconcat_dim is True, then inputs' shapes are
+allowd to vary among all inputs.
+
+The `indices`, `values`, and `shapes` lists must have the same length.
+
+If expand_nonconcat_dim is False, then the output shape is identical to the
+inputs', except along the concat dimension, where it is the sum of the inputs'
+sizes along that dimension.
+
+If expand_nonconcat_dim is True, then the output shape along the non-concat
+dimensions will be expand to be the largest among all inputs, and it is the
+sum of the inputs sizes along the concat dimension.
+
+The output elements will be resorted to preserve the sort order along
+increasing dimension number.
+
+This op runs in `O(M log M)` time, where `M` is the total number of non-empty
+values across all inputs. This is due to the need for an internal sort in
+order to concatenate efficiently across an arbitrary dimension.
+
+For example, if `concat_dim = 1` and the inputs are
+
+    sp_inputs[0]: shape = [2, 3]
+    [0, 2]: "a"
+    [1, 0]: "b"
+    [1, 1]: "c"
+
+    sp_inputs[1]: shape = [2, 4]
+    [0, 1]: "d"
+    [0, 2]: "e"
+
+then the output will be
+
+    shape = [2, 7]
+    [0, 2]: "a"
+    [0, 4]: "d"
+    [0, 5]: "e"
+    [1, 0]: "b"
+    [1, 1]: "c"
+
+Graphically this is equivalent to doing
+
+    [    a] concat [  d e  ] = [    a   d e  ]
+    [b c  ]        [       ]   [b c          ]
+
+Another example, if 'concat_dim = 1' and the inputs are
+
+    sp_inputs[0]: shape = [3, 3]
+    [0, 2]: "a"
+    [1, 0]: "b"
+    [2, 1]: "c"
+
+    sp_inputs[1]: shape = [2, 4]
+    [0, 1]: "d"
+    [0, 2]: "e"
+
+if expand_nonconcat_dim = False, this will result in an error. But if
+expand_nonconcat_dim = True, this will result in:
+
+    shape = [3, 7]
+    [0, 2]: "a"
+    [0, 4]: "d"
+    [0, 5]: "e"
+    [1, 0]: "b"
+    [2, 1]: "c"
+
+Graphically this is equivalent to doing
+
+    [    a] concat [  d e  ] = [    a   d e  ]
+    [b    ]        [       ]   [b            ]
+    [  c  ]                    [  c          ]
+
+
+##### Args:
+
+
+*  <b>`concat_dim`</b>: Dimension to concatenate along.
+*  <b>`sp_inputs`</b>: List of `SparseTensor` to concatenate.
+*  <b>`name`</b>: A name prefix for the returned tensors (optional).
+*  <b>`expand_nonconcat_dim`</b>: Whether to allow the expansion in the non-concat
+    dimensions. Defaulted to False.
+
+##### Returns:
+
+  A `SparseTensor` with the concatenated output.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sp_inputs` is not a list of `SparseTensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sparse_mask.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sparse_mask.md
new file mode 100644
index 0000000000..d2fa38733b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sparse_mask.md
@@ -0,0 +1,39 @@
+### `tf.sparse_mask(a, mask_indices, name=None)` {#sparse_mask}
+
+Masks elements of `IndexedSlices`.
+
+Given an `IndexedSlices` instance `a`, returns another `IndexedSlices` that
+contains a subset of the slices of `a`. Only the slices at indices specified
+in `mask_indices` are returned.
+
+This is useful when you need to extract a subset of slices in an
+`IndexedSlices` object.
+
+For example:
+
+```python
+# `a` contains slices at indices [12, 26, 37, 45] from a large tensor
+# with shape [1000, 10]
+a.indices => [12, 26, 37, 45]
+tf.shape(a.values) => [4, 10]
+
+# `b` will be the subset of `a` slices at its second and third indices, so
+# we want to mask of its first and last indices (which are at absolute
+# indices 12, 45)
+b = tf.sparse_mask(a, [12, 45])
+
+b.indices => [26, 37]
+tf.shape(b.values) => [2, 10]
+
+```
+
+##### Args:
+
+  * `a`: An `IndexedSlices` instance.
+  * `mask_indices`: Indices of elements to mask.
+  * `name`: A name for the operation (optional).
+
+##### Returns:
+
+  The masked `IndexedSlices` instance.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sparse_to_indicator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sparse_to_indicator.md
deleted file mode 100644
index 8ee455be32..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sparse_to_indicator.md
+++ /dev/null
@@ -1,52 +0,0 @@
-### `tf.sparse_to_indicator(sp_input, vocab_size, name=None)` {#sparse_to_indicator}
-
-Converts a `SparseTensor` of ids into a dense bool indicator tensor.
-
-The last dimension of `sp_input.indices` is discarded and replaced with
-the values of `sp_input`.  If `sp_input.shape = [D0, D1, ..., Dn, K]`, then
-`output.shape = [D0, D1, ..., Dn, vocab_size]`, where
-
-    output[d_0, d_1, ..., d_n, sp_input[d_0, d_1, ..., d_n, k]] = True
-
-and False elsewhere in `output`.
-
-For example, if `sp_input.shape = [2, 3, 4]` with non-empty values:
-
-    [0, 0, 0]: 0
-    [0, 1, 0]: 10
-    [1, 0, 3]: 103
-    [1, 1, 2]: 150
-    [1, 1, 3]: 149
-    [1, 1, 4]: 150
-    [1, 2, 1]: 121
-
-and `vocab_size = 200`, then the output will be a `[2, 3, 200]` dense bool
-tensor with False everywhere except at positions
-
-    (0, 0, 0), (0, 1, 10), (1, 0, 103), (1, 1, 149), (1, 1, 150),
-    (1, 2, 121).
-
-Note that repeats are allowed in the input SparseTensor.
-This op is useful for converting `SparseTensor`s into dense formats for
-compatibility with ops that expect dense tensors.
-
-The input `SparseTensor` must be in row-major order.
-
-##### Args:
-
-
-*  <b>`sp_input`</b>: A `SparseTensor` with `values` property of type `int32` or
-    `int64`.
-*  <b>`vocab_size`</b>: A scalar int64 Tensor (or Python int) containing the new size
-    of the last dimension, `all(0 <= sp_input.values < vocab_size)`.
-*  <b>`name`</b>: A name prefix for the returned tensors (optional)
-
-##### Returns:
-
-  A dense bool indicator tensor representing the indices with specified value.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.string_to_hash_bucket_strong.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.string_to_hash_bucket_strong.md
deleted file mode 100644
index 67cf3b6fd9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.string_to_hash_bucket_strong.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.string_to_hash_bucket_strong(input, num_buckets, key, name=None)` {#string_to_hash_bucket_strong}
-
-Converts each string in the input Tensor to its hash mod by a number of buckets.
-
-The hash function is deterministic on the content of the string within the
-process. The hash function is a keyed hash function, where attribute `key`
-defines the key of the hash function. `key` is an array of 2 elements.
-
-A strong hash is important when inputs may be malicious, e.g. URLs with
-additional components. Adversaries could try to make their inputs hash to the
-same bucket for a denial-of-service attack or to skew the results. A strong
-hash prevents this by making it dificult, if not infeasible, to compute inputs
-that hash to the same bucket. This comes at a cost of roughly 4x higher compute
-time than tf.string_to_hash_bucket_fast.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `string`. The strings to assign a hash bucket.
-*  <b>`num_buckets`</b>: An `int` that is `>= 1`. The number of buckets.
-*  <b>`key`</b>: A list of `ints`.
-    The key for the keyed hash function passed as a list of two uint64
-    elements.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `int64`.
-  A Tensor of the same shape as the input `string_tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sub.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sub.md
deleted file mode 100644
index 2d1da0f0b9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.sub.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.sub(x, y, name=None)` {#sub}
-
-Returns x - y element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.test.compute_gradient.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.test.compute_gradient.md
deleted file mode 100644
index 19b302d466..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.test.compute_gradient.md
+++ /dev/null
@@ -1,40 +0,0 @@
-### `tf.test.compute_gradient(x, x_shape, y, y_shape, x_init_value=None, delta=0.001, init_targets=None)` {#compute_gradient}
-
-Computes and returns the theoretical and numerical Jacobian.
-
-If `x` or `y` is complex, the Jacobian will still be real but the
-corresponding Jacobian dimension(s) will be twice as large.  This is required
-even if both input and output is complex since TensorFlow graphs are not
-necessarily holomorphic, and may have gradients not expressible as complex
-numbers.  For example, if `x` is complex with shape `[m]` and `y` is complex
-with shape `[n]`, each Jacobian `J` will have shape `[m * 2, n * 2]` with
-
-    J[:m, :n] = d(Re y)/d(Re x)
-    J[:m, n:] = d(Im y)/d(Re x)
-    J[m:, :n] = d(Re y)/d(Im x)
-    J[m:, n:] = d(Im y)/d(Im x)
-
-##### Args:
-
-
-*  <b>`x`</b>: a tensor or list of tensors
-*  <b>`x_shape`</b>: the dimensions of x as a tuple or an array of ints. If x is a list,
-  then this is the list of shapes.
-
-*  <b>`y`</b>: a tensor
-*  <b>`y_shape`</b>: the dimensions of y as a tuple or an array of ints.
-*  <b>`x_init_value`</b>: (optional) a numpy array of the same shape as "x"
-    representing the initial value of x. If x is a list, this should be a list
-    of numpy arrays.  If this is none, the function will pick a random tensor
-    as the initial value.
-*  <b>`delta`</b>: (optional) the amount of perturbation.
-*  <b>`init_targets`</b>: list of targets to run to initialize model params.
-    TODO(mrry): remove this argument.
-
-##### Returns:
-
-  Two 2-d numpy arrays representing the theoretical and numerical
-  Jacobian for dy/dx. Each has "x_size" rows and "y_size" columns
-  where "x_size" is the number of elements in x and "y_size" is the
-  number of elements in y. If x is a list, returns a list of two numpy arrays.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.to_int32.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.to_int32.md
new file mode 100644
index 0000000000..fcc9db61cc
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.to_int32.md
@@ -0,0 +1,19 @@
+### `tf.to_int32(x, name='ToInt32')` {#to_int32}
+
+Casts a tensor to type `int32`.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` or `SparseTensor` with same shape as `x` with type `int32`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `x` cannot be cast to the `int32`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.trace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.trace.md
new file mode 100644
index 0000000000..3b1e71fda1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.trace.md
@@ -0,0 +1,29 @@
+### `tf.trace(x, name=None)` {#trace}
+
+Compute the trace of a tensor `x`.
+
+`trace(x)` returns the sum of along the diagonal.
+
+For example:
+
+```python
+# 'x' is [[1, 1],
+#         [1, 1]]
+tf.trace(x) ==> 2
+
+# 'x' is [[1,2,3],
+#         [4,5,6],
+#         [7,8,9]]
+tf.trace(x) ==> 15
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: 2-D tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The trace of input tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.ClusterSpec.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.ClusterSpec.md
new file mode 100644
index 0000000000..c695781a86
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.ClusterSpec.md
@@ -0,0 +1,86 @@
+Represents a cluster as a set of "tasks", organized into "jobs".
+
+A `tf.train.ClusterSpec` represents the set of processes that
+participate in a distributed TensorFlow computation. Every
+[`tf.train.Server`](#Server) is constructed in a particular cluster.
+
+To create a cluster with two jobs and five tasks, you specify the
+mapping from job names to lists of network addresses (typically
+hostname-port pairs).
+
+```
+cluster = tf.train.ClusterSpec({"worker": ["worker0.example.com:2222",
+                                           "worker1.example.com:2222",
+                                           "worker2.example.com:2222"],
+                                "ps": ["ps0.example.com:2222",
+                                       "ps1.example.com:2222"]})
+```
+
+- - -
+
+#### `tf.train.ClusterSpec.as_cluster_def()` {#ClusterSpec.as_cluster_def}
+
+Returns a `tf.train.ClusterDef` protocol buffer based on this cluster.
+
+
+- - -
+
+#### `tf.train.ClusterSpec.as_dict()` {#ClusterSpec.as_dict}
+
+Returns a dictionary from job names to lists of network addresses.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.train.ClusterSpec.__init__(cluster)` {#ClusterSpec.__init__}
+
+Creates a `ClusterSpec`.
+
+##### Args:
+
+
+*  <b>`cluster`</b>: A dictionary mapping one or more job names to lists of network
+    addresses, or a `tf.train.ClusterDef` protocol buffer.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `cluster` is not a dictionary mapping strings to lists
+    of strings, and not a `tf.train.ClusterDef` protobuf.
+
+
+- - -
+
+#### `tf.train.ClusterSpec.job_tasks(job_name)` {#ClusterSpec.job_tasks}
+
+Returns a list of tasks in the given job.
+
+##### Args:
+
+
+*  <b>`job_name`</b>: The string name of a job in this cluster.
+
+##### Returns:
+
+  A list of strings, corresponding to the network addresses of tasks in
+  the given job, ordered by task index.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `job_name` does not name a job in this cluster.
+
+
+- - -
+
+#### `tf.train.ClusterSpec.jobs` {#ClusterSpec.jobs}
+
+Returns a list of job names in this cluster.
+
+##### Returns:
+
+  A list of strings, corresponding to the names of jobs in this cluster.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.SessionManager.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.SessionManager.md
deleted file mode 100644
index 8bebb8bd29..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.SessionManager.md
+++ /dev/null
@@ -1,187 +0,0 @@
-Training helper that restores from checkpoint and creates session.
-
-This class is a small wrapper that takes care of session creation and
-checkpoint recovery. It also provides functions that to facilitate
-coordination among multiple training threads or processes.
-
-* Checkpointing trained variables as the training progresses.
-* Initializing variables on startup, restoring them from the most recent
-  checkpoint after a crash, or wait for checkpoints to become available.
-
-### Usage:
-
-```python
-with tf.Graph().as_default():
-   ...add operations to the graph...
-  # Create a SessionManager that will checkpoint the model in '/tmp/mydir'.
-  sm = SessionManager()
-  sess = sm.prepare_session(master, init_op, saver, checkpoint_dir)
-  # Use the session to train the graph.
-  while True:
-    sess.run(<my_train_op>)
-```
-
-`prepare_session()` initializes or restores a model. It requires `init_op`
-and `saver` as an argument.
-
-A second process could wait for the model to be ready by doing the following:
-
-```python
-with tf.Graph().as_default():
-   ...add operations to the graph...
-  # Create a SessionManager that will wait for the model to become ready.
-  sm = SessionManager()
-  sess = sm.wait_for_session(master)
-  # Use the session to train the graph.
-  while True:
-    sess.run(<my_train_op>)
-```
-
-`wait_for_session()` waits for a model to be initialized by other processes.
-- - -
-
-#### `tf.train.SessionManager.__init__(local_init_op=None, ready_op=None, graph=None, recovery_wait_secs=30)` {#SessionManager.__init__}
-
-Creates a SessionManager.
-
-The `local_init_op` is an `Operation` that is run always after a new session
-was created. If `None`, this step is skipped.
-
-The `ready_op` is an `Operation` used to check if the model is ready.  The
-model is considered ready if that operation returns an empty string tensor.
-If the operation returns non empty string tensor, the elements are
-concatenated and used to indicate to the user why the model is not ready.
-
-If `ready_op` is `None`, the model is not checked for readiness.
-
-`recovery_wait_secs` is the number of seconds between checks that
-the model is ready.  It is used by processes to wait for a model to
-be initialized or restored.  Defaults to 30 seconds.
-
-##### Args:
-
-
-*  <b>`local_init_op`</b>: An `Operation` run immediately after session creation.
-     Usually used to initialize tables and local variables.
-*  <b>`ready_op`</b>: An `Operation` to check if the model is initialized.
-*  <b>`graph`</b>: The `Graph` that the model will use.
-*  <b>`recovery_wait_secs`</b>: Seconds between checks for the model to be ready.
-
-
-- - -
-
-#### `tf.train.SessionManager.prepare_session(master, init_op=None, saver=None, checkpoint_dir=None, wait_for_checkpoint=False, max_wait_secs=7200, config=None, init_feed_dict=None, init_fn=None)` {#SessionManager.prepare_session}
-
-Creates a `Session`. Makes sure the model is ready to be used.
-
-Creates a `Session` on 'master'. If a `saver` object is passed in, and
-`checkpoint_dir` points to a directory containing valid checkpoint
-files, then it will try to recover the model from checkpoint. If
-no checkpoint files are available, and `wait_for_checkpoint` is
-`True`, then the process would check every `recovery_wait_secs`,
-up to `max_wait_secs`, for recovery to succeed.
-
-If the model cannot be recovered successfully then it is initialized by
-either running the provided `init_op`, or calling the provided `init_fn`.
-It is an error if the model cannot be recovered and neither an `init_op`
-or an `init_fn` are passed.
-
-This is a convenient function for the following, with a few error checks
-added:
-
-```python
-sess, initialized = self.recover_session(master)
-if not initialized:
-  if init_op:
-    sess.run(init_op, feed_dict=init_feed_dict)
-  if init_fn;
-    init_fn(sess)
-return sess
-```
-
-##### Args:
-
-
-*  <b>`master`</b>: `String` representation of the TensorFlow master to use.
-*  <b>`init_op`</b>: Optional `Operation` used to initialize the model.
-*  <b>`saver`</b>: A `Saver` object used to restore a model.
-*  <b>`checkpoint_dir`</b>: Path to the checkpoint files.
-*  <b>`wait_for_checkpoint`</b>: Whether to wait for checkpoint to become available.
-*  <b>`max_wait_secs`</b>: Maximum time to wait for checkpoints to become available.
-*  <b>`config`</b>: Optional `ConfigProto` proto used to configure the session.
-*  <b>`init_feed_dict`</b>: Optional dictionary that maps `Tensor` objects to feed
-    values.  This feed dictionary is passed to the session `run()` call when
-    running the init op.
-*  <b>`init_fn`</b>: Optional callable used to initialize the model. Called after the
-    optional `init_op` is called.  The callable must accept one argument,
-    the session being initialized.
-
-##### Returns:
-
-  A `Session` object that can be used to drive the model.
-
-##### Raises:
-
-
-*  <b>`RuntimeError`</b>: If the model cannot be initialized or recovered.
-
-
-- - -
-
-#### `tf.train.SessionManager.recover_session(master, saver=None, checkpoint_dir=None, wait_for_checkpoint=False, max_wait_secs=7200, config=None)` {#SessionManager.recover_session}
-
-Creates a `Session`, recovering if possible.
-
-Creates a new session on 'master'.  If the session is not initialized
-and can be recovered from a checkpoint, recover it.
-
-##### Args:
-
-
-*  <b>`master`</b>: `String` representation of the TensorFlow master to use.
-*  <b>`saver`</b>: A `Saver` object used to restore a model.
-*  <b>`checkpoint_dir`</b>: Path to the checkpoint files.
-*  <b>`wait_for_checkpoint`</b>: Whether to wait for checkpoint to become available.
-*  <b>`max_wait_secs`</b>: Maximum time to wait for checkpoints to become available.
-*  <b>`config`</b>: Optional `ConfigProto` proto used to configure the session.
-
-##### Returns:
-
-  A pair (sess, initialized) where 'initialized' is `True` if
-  the session could be recovered, `False` otherwise.
-
-
-- - -
-
-#### `tf.train.SessionManager.wait_for_session(master, config=None, max_wait_secs=inf)` {#SessionManager.wait_for_session}
-
-Creates a new `Session` and waits for model to be ready.
-
-Creates a new `Session` on 'master'.  Waits for the model to be
-initialized or recovered from a checkpoint.  It's expected that
-another thread or process will make the model ready, and that this
-is intended to be used by threads/processes that participate in a
-distributed training configuration where a different thread/process
-is responsible for initializing or recovering the model being trained.
-
-NB: The amount of time this method waits for the session is bounded
-by max_wait_secs. By default, this function will wait indefinitely.
-
-##### Args:
-
-
-*  <b>`master`</b>: `String` representation of the TensorFlow master to use.
-*  <b>`config`</b>: Optional ConfigProto proto used to configure the session.
-*  <b>`max_wait_secs`</b>: Maximum time to wait for the session to become available.
-
-##### Returns:
-
-  A `Session`. May be None if the operation exceeds the timeout
-  specified by config.operation_timeout_in_ms.
-
-##### Raises:
-
-  tf.DeadlineExceededError: if the session is not available after
-    max_wait_secs.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.Supervisor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.Supervisor.md
new file mode 100644
index 0000000000..b3d17eac2d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.Supervisor.md
@@ -0,0 +1,845 @@
+A training helper that checkpoints models and computes summaries.
+
+The Supervisor is a small wrapper around a `Coordinator`, a `Saver`,
+and a `SessionManager` that takes care of common needs of Tensorflow
+training programs.
+
+#### Use for a single program
+
+```python
+with tf.Graph().as_default():
+  ...add operations to the graph...
+  # Create a Supervisor that will checkpoint the model in '/tmp/mydir'.
+  sv = Supervisor(logdir='/tmp/mydir')
+  # Get a Tensorflow session managed by the supervisor.
+  with sv.managed_session(FLAGS.master) as sess:
+    # Use the session to train the graph.
+    while not sv.should_stop():
+      sess.run(<my_train_op>)
+```
+
+Within the `with sv.managed_session()` block all variables in the graph have
+been initialized.  In addition, a few services have been started to
+checkpoint the model and add summaries to the event log.
+
+If the program crashes and is restarted, the managed session automatically
+reinitialize variables from the most recent checkpoint.
+
+The supervisor is notified of any exception raised by one of the services.
+After an exception is raised, `should_stop()` returns `True`.  In that case
+the training loop should also stop.  This is why the training loop has to
+check for `sv.should_stop()`.
+
+Exceptions that indicate that the training inputs have been exhausted,
+`tf.errors.OutOfRangeError`, also cause `sv.should_stop()` to return `True`
+but are not re-raised from the `with` block: they indicate a normal
+termination.
+
+#### Use for multiple replicas
+
+To train with replicas you deploy the same program in a `Cluster`.
+One of the tasks must be identified as the *chief*: the task that handles
+initialization, checkpoints, summaries, and recovery.  The other tasks
+depend on the *chief* for these services.
+
+The only change you have to do to the single program code is to indicate
+if the program is running as the *chief*.
+
+```python
+# Choose a task as the chief. This could be based on server_def.task_index,
+# or job_def.name, or job_def.tasks. It's entirely up to the end user.
+# But there can be only one *chief*.
+is_chief = (server_def.task_index == 0)
+server = tf.train.Server(server_def)
+
+with tf.Graph().as_default():
+  ...add operations to the graph...
+  # Create a Supervisor that uses log directory on a shared file system.
+  # Indicate if you are the 'chief'
+  sv = Supervisor(logdir='/shared_directory/...', is_chief=is_chief)
+  # Get a Session in a TensorFlow server on the cluster.
+  with sv.managed_session(server.target) as sess:
+    # Use the session to train the graph.
+    while not sv.should_stop():
+      sess.run(<my_train_op>)
+```
+
+In the *chief* task, the `Supervisor` works exactly as in the first example
+above.  In the other tasks `sv.managed_session()` waits for the Model to have
+been intialized before returning a session to the training code.  The
+non-chief tasks depend on the chief taks for initializing the model.
+
+If one of the tasks crashes and restarts, `managed_session()`
+checks if the Model is initialized.  If yes, it just creates a session and
+returns it to the training code that proceeds normally.  If the model needs
+to be initialized, the chief task takes care of reinitializing it; the other
+tasks just wait for the model to have been initialized.
+
+NOTE: This modified program still works fine as a single program.
+The single program marks itself as the chief.
+
+#### What `master` string to use
+
+Whether you are running on your machine or in the cluster you can use the
+following values for the --master flag:
+
+* Specifying `''` requests an in-process session that does not use RPC.
+
+* Specifying `'local'` requests a session that uses the RPC-based
+  "Master interface" to run TensorFlow programs. See
+  [`tf.train.Server.create_local_server()`](#Server.create_local_server) for
+  details.
+
+* Specifying `'grpc://hostname:port'` requests a session that uses
+  the RPC interface to a specific , and also allows the in-process
+  master to access remote tensorflow workers. Often, it is
+  appropriate to pass `server.target` (for some `tf.train.Server`
+  named `server).
+
+#### Advanced use
+
+##### Launching additional services
+
+`managed_session()` launches the Checkpoint and Summary services (threads).
+If you need more services to run you can simply launch them in the block
+controlled by `managed_session()`.
+
+Example: Start a thread to print losses.  We want this thread to run
+every 60 seconds, so we launch it with `sv.loop()`.
+
+  ```python
+  ...
+  sv = Supervisor(logdir='/tmp/mydir')
+  with sv.managed_session(FLAGS.master) as sess:
+    sv.loop(60, print_loss, (sess))
+    while not sv.should_stop():
+      sess.run(my_train_op)
+  ```
+
+##### Launching fewer services
+
+`managed_session()` launches the "summary" and "checkpoint" threads which use
+either the optionally `summary_op` and `saver` passed to the constructor, or
+default ones created automatically by the supervisor.  If you want to run
+your own summary and checkpointing logic, disable these services by passing
+`None` to the `summary_op` and `saver` parameters.
+
+Example: Create summaries manually every 100 steps in the chief.
+
+  ```python
+  # Create a Supervisor with no automatic summaries.
+  sv = Supervisor(logdir='/tmp/mydir', is_chief=is_chief, summary_op=None)
+  # As summary_op was None, managed_session() does not start the
+  # summary thread.
+  with sv.managed_session(FLAGS.master) as sess:
+    for step in xrange(1000000):
+      if sv.should_stop():
+        break
+      if is_chief and step % 100 == 0:
+        # Create the summary every 100 chief steps.
+        sv.summary_computed(sess, sess.run(my_summary_op))
+      else:
+        # Train normally
+        sess.run(my_train_op)
+  ```
+
+##### Custom model initialization
+
+`managed_session()` only supports initializing the model by running an
+`init_op` or restoring from the latest checkpoint.  If you have special
+initialization needs, see how to specify a `local_init_op` when creating the
+supervisor.  You can also use the `SessionManager` directly to create a
+session and check if it could be initialized automatically.
+
+- - -
+
+#### `tf.train.Supervisor.__init__(graph=None, ready_op=0, is_chief=True, init_op=0, init_feed_dict=None, local_init_op=0, logdir=None, summary_op=0, saver=0, global_step=0, save_summaries_secs=120, save_model_secs=600, recovery_wait_secs=30, stop_grace_secs=120, checkpoint_basename='model.ckpt', session_manager=None, summary_writer=0, init_fn=None)` {#Supervisor.__init__}
+
+Create a `Supervisor`.
+
+##### Args:
+
+
+*  <b>`graph`</b>: A `Graph`.  The graph that the model will use.  Defaults to the
+    default `Graph`.  The supervisor may add operations to the graph before
+    creating a session, but the graph should not be modified by the caller
+    after passing it to the supervisor.
+*  <b>`ready_op`</b>: 1-D string `Tensor`.  This tensor is evaluated by supervisors in
+    `prepare_or_wait_for_session()` to check if the model is ready to use.
+    The model is considered ready if it returns an empty array.  Defaults to
+    the tensor returned from `tf.report_uninitialized_variables()`  If
+    `None`, the model is not checked for readiness.
+*  <b>`is_chief`</b>: If True, create a chief supervisor in charge of initializing
+    and restoring the model.  If False, create a supervisor that relies
+    on a chief supervisor for inits and restore.
+*  <b>`init_op`</b>: `Operation`.  Used by chief supervisors to initialize the model
+    when it can not be recovered.  Defaults to an `Operation` that
+    initializes all variables.  If `None`, no initialization is done
+    automatically unless you pass a value for `init_fn`, see below.
+*  <b>`init_feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
+    This feed dictionary will be used when `init_op` is evaluated.
+*  <b>`local_init_op`</b>: `Operation`. Used by all supervisors to run initializations
+    that should run for every new supervisor instance. By default these
+    are table initializers and initializers for local variables.
+    If `None`, no further per supervisor-instance initialization is
+    done automatically.
+*  <b>`logdir`</b>: A string.  Optional path to a directory where to checkpoint the
+    model and log events for the visualizer.  Used by chief supervisors.
+    The directory will be created if it does not exist.
+*  <b>`summary_op`</b>: An `Operation` that returns a Summary for the event logs.
+    Used by chief supervisors if a `logdir` was specified.  Defaults to the
+    operation returned from merge_all_summaries().  If `None`, summaries are
+    not computed automatically.
+*  <b>`saver`</b>: A Saver object.  Used by chief supervisors if a `logdir` was
+    specified.  Defaults to the saved returned by Saver().
+    If `None`, the model is not saved automatically.
+*  <b>`global_step`</b>: An integer Tensor of size 1 that counts steps.  The value
+    from 'global_step' is used in summaries and checkpoint filenames.
+    Default to the op named 'global_step' in the graph if it exists, is of
+    rank 1, size 1, and of type tf.int32 ot tf.int64.  If `None` the global
+    step is not recorded in summaries and checkpoint files.  Used by chief
+    supervisors if a `logdir` was specified.
+*  <b>`save_summaries_secs`</b>: Number of seconds between the computation of
+    summaries for the event log.  Defaults to 120 seconds.  Pass 0 to
+    disable summaries.
+*  <b>`save_model_secs`</b>: Number of seconds between the creation of model
+    checkpoints.  Defaults to 600 seconds.  Pass 0 to disable checkpoints.
+*  <b>`recovery_wait_secs`</b>: Number of seconds between checks that the model
+    is ready.  Used by supervisors when waiting for a chief supervisor
+    to initialize or restore the model.  Defaults to 30 seconds.
+*  <b>`stop_grace_secs`</b>: Grace period, in seconds, given to running threads to
+    stop when `stop()` is called.  Defaults to 120 seconds.
+*  <b>`checkpoint_basename`</b>: The basename for checkpoint saving.
+*  <b>`session_manager`</b>: `SessionManager`, which manages Session creation and
+    recovery. If it is `None`, a default `SessionManager` will be created
+    with the set of arguments passed in for backwards compatibility.
+*  <b>`summary_writer`</b>: `SummaryWriter` to use or `USE_DEFAULT`.  Can be `None`
+    to indicate that no summaries should be written.
+*  <b>`init_fn`</b>: Optional callable used to initialize the model. Called
+    after the optional `init_op` is called.  The callable must accept one
+    argument, the session being initialized.
+
+##### Returns:
+
+  A `Supervisor`.
+
+
+- - -
+
+#### `tf.train.Supervisor.managed_session(master='', config=None, start_standard_services=True, close_summary_writer=True)` {#Supervisor.managed_session}
+
+Returns a context manager for a managed session.
+
+This context manager creates and automatically recovers a session.  It
+optionally starts the standard services that handle checkpoints and
+summaries.  It monitors exceptions raised from the `with` block or from the
+services and stops the supervisor as needed.
+
+The context manager is typically used as follows:
+
+```python
+def train():
+  sv = tf.train.Supervisor(...)
+  with sv.managed_session(<master>) as sess:
+    for step in xrange(..):
+      if sv.should_stop():
+        break
+      sess.run(<my training op>)
+      ...do other things needed at each training step...
+```
+
+An exception raised from the `with` block or one of the service threads is
+raised again when the block exits.  This is done after stopping all threads
+and closing the session.  For example, an `AbortedError` exception, raised
+in case of preemption of one of the workers in a distributed model, is
+raised again when the block exits.
+
+If you want to retry the training loop in case of preemption you can do it
+as follows:
+
+```python
+def main(...):
+  while True
+    try:
+      train()
+    except tf.errors.Aborted:
+      pass
+```
+
+As a special case, exceptions used for control flow, such as
+`OutOfRangeError` which reports that input queues are exhausted, are not
+raised again from the `with` block: they indicate a clean termination of
+the training loop and are considered normal termination.
+
+##### Args:
+
+
+*  <b>`master`</b>: name of the TensorFlow master to use.  See the `tf.Session`
+    constructor for how this is interpreted.
+*  <b>`config`</b>: Optional `ConfigProto` proto used to configure the session.
+    Passed as-is to create the session.
+*  <b>`start_standard_services`</b>: Whether to start the standard services,
+    such as checkpoint, summary and step counter.
+*  <b>`close_summary_writer`</b>: Whether to close the summary writer when
+    closing the session.  Defaults to True.
+
+##### Returns:
+
+  A context manager that yields a `Session` restored from the latest
+  checkpoint or initialized from scratch if not checkpoint exists.  The
+  session is closed when the `with` block exits.
+
+
+- - -
+
+#### `tf.train.Supervisor.prepare_or_wait_for_session(master='', config=None, wait_for_checkpoint=False, max_wait_secs=7200, start_standard_services=True)` {#Supervisor.prepare_or_wait_for_session}
+
+Make sure the model is ready to be used.
+
+Create a session on 'master', recovering or initializing the model as
+needed, or wait for a session to be ready.  If running as the chief
+and `start_standard_service` is set to True, also call the session
+manager to start the standard services.
+
+##### Args:
+
+
+*  <b>`master`</b>: name of the TensorFlow master to use.  See the `tf.Session`
+    constructor for how this is interpreted.
+*  <b>`config`</b>: Optional ConfigProto proto used to configure the session,
+    which is passed as-is to create the session.
+*  <b>`wait_for_checkpoint`</b>: Whether we should wait for the availability of a
+    checkpoint before creating Session. Defaults to False.
+*  <b>`max_wait_secs`</b>: Maximum time to wait for the session to become available.
+*  <b>`start_standard_services`</b>: Whether to start the standard services and the
+    queue runners.
+
+##### Returns:
+
+  A Session object that can be used to drive the model.
+
+
+- - -
+
+#### `tf.train.Supervisor.start_standard_services(sess)` {#Supervisor.start_standard_services}
+
+Start the standard services for 'sess'.
+
+This starts services in the background.  The services started depend
+on the parameters to the constructor and may include:
+
+  - A Summary thread computing summaries every save_summaries_secs.
+  - A Checkpoint thread saving the model every save_model_secs.
+  - A StepCounter thread measure step time.
+
+##### Args:
+
+
+*  <b>`sess`</b>: A Session.
+
+##### Returns:
+
+  A list of threads that are running the standard services.  You can use
+  the Supervisor's Coordinator to join these threads with:
+    sv.coord.Join(<list of threads>)
+
+##### Raises:
+
+
+*  <b>`RuntimeError`</b>: If called with a non-chief Supervisor.
+*  <b>`ValueError`</b>: If not `logdir` was passed to the constructor as the
+    services need a log directory.
+
+
+- - -
+
+#### `tf.train.Supervisor.start_queue_runners(sess, queue_runners=None)` {#Supervisor.start_queue_runners}
+
+Start threads for `QueueRunners`.
+
+Note that the queue runners collected in the graph key `QUEUE_RUNNERS`
+are already started automatically when you create a session with the
+supervisor, so unless you have non-collected queue runners to start
+you do not need to call this explicitely.
+
+##### Args:
+
+
+*  <b>`sess`</b>: A `Session`.
+*  <b>`queue_runners`</b>: A list of `QueueRunners`. If not specified, we'll use the
+    list of queue runners gathered in the graph under the key
+    `GraphKeys.QUEUE_RUNNERS`.
+
+##### Returns:
+
+  The list of threads started for the `QueueRunners`.
+
+
+- - -
+
+#### `tf.train.Supervisor.summary_computed(sess, summary, global_step=None)` {#Supervisor.summary_computed}
+
+Indicate that a summary was computed.
+
+##### Args:
+
+
+*  <b>`sess`</b>: A `Session` object.
+*  <b>`summary`</b>: A Summary proto, or a string holding a serialized summary proto.
+*  <b>`global_step`</b>: Int. global step this summary is associated with. If `None`,
+    it will try to fetch the current step.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if 'summary' is not a Summary proto or a string.
+*  <b>`RuntimeError`</b>: if the Supervisor was created without a `logdir`.
+
+
+
+- - -
+
+#### `tf.train.Supervisor.stop(threads=None, close_summary_writer=True)` {#Supervisor.stop}
+
+Stop the services and the coordinator.
+
+This does not close the session.
+
+##### Args:
+
+
+*  <b>`threads`</b>: Optional list of threads to join with the coordinator.  If
+    `None`, defaults to the threads running the standard services, the
+    threads started for `QueueRunners`, and the threads started by the
+    `loop()` method.  To wait on additional threads, pass the
+    list in this parameter.
+*  <b>`close_summary_writer`</b>: Whether to close the `summary_writer`.  Defaults to
+    `True` if the summary writer was created by the supervisor, `False`
+    otherwise.
+
+
+- - -
+
+#### `tf.train.Supervisor.request_stop(ex=None)` {#Supervisor.request_stop}
+
+Request that the coordinator stop the threads.
+
+See `Coordinator.request_stop()`.
+
+##### Args:
+
+
+*  <b>`ex`</b>: Optional `Exception`, or Python `exc_info` tuple as returned by
+    `sys.exc_info()`.  If this is the first call to `request_stop()` the
+    corresponding exception is recorded and re-raised from `join()`.
+
+
+- - -
+
+#### `tf.train.Supervisor.should_stop()` {#Supervisor.should_stop}
+
+Check if the coordinator was told to stop.
+
+See `Coordinator.should_stop()`.
+
+##### Returns:
+
+  True if the coordinator was told to stop, False otherwise.
+
+
+- - -
+
+#### `tf.train.Supervisor.stop_on_exception()` {#Supervisor.stop_on_exception}
+
+Context handler to stop the supervisor when an exception is raised.
+
+See `Coordinator.stop_on_exception()`.
+
+##### Returns:
+
+  A context handler.
+
+
+- - -
+
+#### `tf.train.Supervisor.wait_for_stop()` {#Supervisor.wait_for_stop}
+
+Block waiting for the coordinator to stop.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.train.Supervisor.Loop(timer_interval_secs, target, args=None, kwargs=None)` {#Supervisor.Loop}
+
+Start a LooperThread that calls a function periodically.
+
+If `timer_interval_secs` is None the thread calls `target(*args, **kwargs)`
+repeatedly.  Otherwise it calls it every `timer_interval_secs`
+seconds.  The thread terminates when a stop is requested.
+
+The started thread is added to the list of threads managed by the supervisor
+so it does not need to be passed to the `stop()` method.
+
+##### Args:
+
+
+*  <b>`timer_interval_secs`</b>: Number. Time boundaries at which to call `target`.
+*  <b>`target`</b>: A callable object.
+*  <b>`args`</b>: Optional arguments to pass to `target` when calling it.
+*  <b>`kwargs`</b>: Optional keyword arguments to pass to `target` when calling it.
+
+##### Returns:
+
+  The started thread.
+
+
+- - -
+
+#### `tf.train.Supervisor.PrepareSession(master='', config=None, wait_for_checkpoint=False, max_wait_secs=7200, start_standard_services=True)` {#Supervisor.PrepareSession}
+
+Make sure the model is ready to be used.
+
+Create a session on 'master', recovering or initializing the model as
+needed, or wait for a session to be ready.  If running as the chief
+and `start_standard_service` is set to True, also call the session
+manager to start the standard services.
+
+##### Args:
+
+
+*  <b>`master`</b>: name of the TensorFlow master to use.  See the `tf.Session`
+    constructor for how this is interpreted.
+*  <b>`config`</b>: Optional ConfigProto proto used to configure the session,
+    which is passed as-is to create the session.
+*  <b>`wait_for_checkpoint`</b>: Whether we should wait for the availability of a
+    checkpoint before creating Session. Defaults to False.
+*  <b>`max_wait_secs`</b>: Maximum time to wait for the session to become available.
+*  <b>`start_standard_services`</b>: Whether to start the standard services and the
+    queue runners.
+
+##### Returns:
+
+  A Session object that can be used to drive the model.
+
+
+- - -
+
+#### `tf.train.Supervisor.RequestStop(ex=None)` {#Supervisor.RequestStop}
+
+Request that the coordinator stop the threads.
+
+See `Coordinator.request_stop()`.
+
+##### Args:
+
+
+*  <b>`ex`</b>: Optional `Exception`, or Python `exc_info` tuple as returned by
+    `sys.exc_info()`.  If this is the first call to `request_stop()` the
+    corresponding exception is recorded and re-raised from `join()`.
+
+
+- - -
+
+#### `tf.train.Supervisor.ShouldStop()` {#Supervisor.ShouldStop}
+
+Check if the coordinator was told to stop.
+
+See `Coordinator.should_stop()`.
+
+##### Returns:
+
+  True if the coordinator was told to stop, False otherwise.
+
+
+- - -
+
+#### `tf.train.Supervisor.StartQueueRunners(sess, queue_runners=None)` {#Supervisor.StartQueueRunners}
+
+Start threads for `QueueRunners`.
+
+Note that the queue runners collected in the graph key `QUEUE_RUNNERS`
+are already started automatically when you create a session with the
+supervisor, so unless you have non-collected queue runners to start
+you do not need to call this explicitely.
+
+##### Args:
+
+
+*  <b>`sess`</b>: A `Session`.
+*  <b>`queue_runners`</b>: A list of `QueueRunners`. If not specified, we'll use the
+    list of queue runners gathered in the graph under the key
+    `GraphKeys.QUEUE_RUNNERS`.
+
+##### Returns:
+
+  The list of threads started for the `QueueRunners`.
+
+
+- - -
+
+#### `tf.train.Supervisor.StartStandardServices(sess)` {#Supervisor.StartStandardServices}
+
+Start the standard services for 'sess'.
+
+This starts services in the background.  The services started depend
+on the parameters to the constructor and may include:
+
+  - A Summary thread computing summaries every save_summaries_secs.
+  - A Checkpoint thread saving the model every save_model_secs.
+  - A StepCounter thread measure step time.
+
+##### Args:
+
+
+*  <b>`sess`</b>: A Session.
+
+##### Returns:
+
+  A list of threads that are running the standard services.  You can use
+  the Supervisor's Coordinator to join these threads with:
+    sv.coord.Join(<list of threads>)
+
+##### Raises:
+
+
+*  <b>`RuntimeError`</b>: If called with a non-chief Supervisor.
+*  <b>`ValueError`</b>: If not `logdir` was passed to the constructor as the
+    services need a log directory.
+
+
+- - -
+
+#### `tf.train.Supervisor.Stop(threads=None, close_summary_writer=True)` {#Supervisor.Stop}
+
+Stop the services and the coordinator.
+
+This does not close the session.
+
+##### Args:
+
+
+*  <b>`threads`</b>: Optional list of threads to join with the coordinator.  If
+    `None`, defaults to the threads running the standard services, the
+    threads started for `QueueRunners`, and the threads started by the
+    `loop()` method.  To wait on additional threads, pass the
+    list in this parameter.
+*  <b>`close_summary_writer`</b>: Whether to close the `summary_writer`.  Defaults to
+    `True` if the summary writer was created by the supervisor, `False`
+    otherwise.
+
+
+- - -
+
+#### `tf.train.Supervisor.StopOnException()` {#Supervisor.StopOnException}
+
+Context handler to stop the supervisor when an exception is raised.
+
+See `Coordinator.stop_on_exception()`.
+
+##### Returns:
+
+  A context handler.
+
+
+- - -
+
+#### `tf.train.Supervisor.SummaryComputed(sess, summary, global_step=None)` {#Supervisor.SummaryComputed}
+
+Indicate that a summary was computed.
+
+##### Args:
+
+
+*  <b>`sess`</b>: A `Session` object.
+*  <b>`summary`</b>: A Summary proto, or a string holding a serialized summary proto.
+*  <b>`global_step`</b>: Int. global step this summary is associated with. If `None`,
+    it will try to fetch the current step.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if 'summary' is not a Summary proto or a string.
+*  <b>`RuntimeError`</b>: if the Supervisor was created without a `logdir`.
+
+
+- - -
+
+#### `tf.train.Supervisor.WaitForStop()` {#Supervisor.WaitForStop}
+
+Block waiting for the coordinator to stop.
+
+
+- - -
+
+#### `tf.train.Supervisor.coord` {#Supervisor.coord}
+
+Return the Coordinator used by the Supervisor.
+
+The Coordinator can be useful if you want to run multiple threads
+during your training.
+
+##### Returns:
+
+  A Coordinator object.
+
+
+- - -
+
+#### `tf.train.Supervisor.global_step` {#Supervisor.global_step}
+
+Return the global_step Tensor used by the supervisor.
+
+##### Returns:
+
+  An integer Tensor for the global_step.
+
+
+- - -
+
+#### `tf.train.Supervisor.init_feed_dict` {#Supervisor.init_feed_dict}
+
+Return the feed dictionary used when evaluating the `init_op`.
+
+##### Returns:
+
+  A feed dictionary or `None`.
+
+
+- - -
+
+#### `tf.train.Supervisor.init_op` {#Supervisor.init_op}
+
+Return the Init Op used by the supervisor.
+
+##### Returns:
+
+  An Op or `None`.
+
+
+- - -
+
+#### `tf.train.Supervisor.is_chief` {#Supervisor.is_chief}
+
+Return True if this is a chief supervisor.
+
+##### Returns:
+
+  A bool.
+
+
+- - -
+
+#### `tf.train.Supervisor.loop(timer_interval_secs, target, args=None, kwargs=None)` {#Supervisor.loop}
+
+Start a LooperThread that calls a function periodically.
+
+If `timer_interval_secs` is None the thread calls `target(*args, **kwargs)`
+repeatedly.  Otherwise it calls it every `timer_interval_secs`
+seconds.  The thread terminates when a stop is requested.
+
+The started thread is added to the list of threads managed by the supervisor
+so it does not need to be passed to the `stop()` method.
+
+##### Args:
+
+
+*  <b>`timer_interval_secs`</b>: Number. Time boundaries at which to call `target`.
+*  <b>`target`</b>: A callable object.
+*  <b>`args`</b>: Optional arguments to pass to `target` when calling it.
+*  <b>`kwargs`</b>: Optional keyword arguments to pass to `target` when calling it.
+
+##### Returns:
+
+  The started thread.
+
+
+- - -
+
+#### `tf.train.Supervisor.ready_op` {#Supervisor.ready_op}
+
+Return the Ready Op used by the supervisor.
+
+##### Returns:
+
+  An Op or `None`.
+
+
+- - -
+
+#### `tf.train.Supervisor.save_model_secs` {#Supervisor.save_model_secs}
+
+Return the delay between checkpoints.
+
+##### Returns:
+
+  A timestamp.
+
+
+- - -
+
+#### `tf.train.Supervisor.save_path` {#Supervisor.save_path}
+
+Return the save path used by the supervisor.
+
+##### Returns:
+
+  A string.
+
+
+- - -
+
+#### `tf.train.Supervisor.save_summaries_secs` {#Supervisor.save_summaries_secs}
+
+Return the delay between summary computations.
+
+##### Returns:
+
+  A timestamp.
+
+
+- - -
+
+#### `tf.train.Supervisor.saver` {#Supervisor.saver}
+
+Return the Saver used by the supervisor.
+
+##### Returns:
+
+  A Saver object.
+
+
+- - -
+
+#### `tf.train.Supervisor.session_manager` {#Supervisor.session_manager}
+
+Return the SessionManager used by the Supervisor.
+
+##### Returns:
+
+  A SessionManager object.
+
+
+- - -
+
+#### `tf.train.Supervisor.summary_op` {#Supervisor.summary_op}
+
+Return the Summary Tensor used by the chief supervisor.
+
+##### Returns:
+
+  A string Tensor for the summary or `None`.
+
+
+- - -
+
+#### `tf.train.Supervisor.summary_writer` {#Supervisor.summary_writer}
+
+Return the SummaryWriter used by the chief supervisor.
+
+##### Returns:
+
+  A SummaryWriter.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.get_checkpoint_state.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.get_checkpoint_state.md
deleted file mode 100644
index e2852b2314..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.get_checkpoint_state.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.train.get_checkpoint_state(checkpoint_dir, latest_filename=None)` {#get_checkpoint_state}
-
-Returns CheckpointState proto from the "checkpoint" file.
-
-If the "checkpoint" file contains a valid CheckpointState
-proto, returns it.
-
-##### Args:
-
-
-*  <b>`checkpoint_dir`</b>: The directory of checkpoints.
-*  <b>`latest_filename`</b>: Optional name of the checkpoint file.  Default to
-    'checkpoint'.
-
-##### Returns:
-
-  A CheckpointState if the state was available, None
-  otherwise.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.input_producer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.input_producer.md
deleted file mode 100644
index 41a417aac3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.input_producer.md
+++ /dev/null
@@ -1,38 +0,0 @@
-### `tf.train.input_producer(input_tensor, element_shape=None, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, summary_name=None, name=None)` {#input_producer}
-
-Output the rows of `input_tensor` to a queue for an input pipeline.
-
-##### Args:
-
-
-*  <b>`input_tensor`</b>: A tensor with the rows to produce. Must be at
-    one-dimensional. Must either have a fully-defined shape, or
-    `element_shape` must be defined.
-*  <b>`element_shape`</b>: (Optional.) A `TensorShape` representing the shape of a
-    row of `input_tensor`, if it cannot be inferred.
-*  <b>`num_epochs`</b>: (Optional.) An integer. If specified `input_producer` produces
-    each row of `input_tensor` `num_epochs` times before generating an
-    `OutOfRange` error. If not specified, `input_producer` can cycle through
-    the rows of `input_tensor` an unlimited number of times.
-*  <b>`shuffle`</b>: (Optional.) A boolean. If true, the rows are randomly shuffled
-    within each eopch.
-*  <b>`seed`</b>: (Optional.) An integer. The seed to use if `shuffle` is true.
-*  <b>`capacity`</b>: (Optional.) The capacity of the queue to be used for buffering
-    the input.
-*  <b>`shared_name`</b>: (Optional.) If set, this queue will be shared under the given
-    name across multiple sessions.
-*  <b>`summary_name`</b>: (Optional.) If set, a scalar summary for the current queue
-    size will be generated, using this name as part of the tag.
-*  <b>`name`</b>: (Optional.) A name for queue.
-
-##### Returns:
-
-  A queue with the output rows.  A `QueueRunner` for the queue is
-  added to the current `QUEUE_RUNNER` collection of the current
-  graph.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the shape of the input cannot be inferred from the arguments.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.start_queue_runners.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.start_queue_runners.md
deleted file mode 100644
index 21ac6efee8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.start_queue_runners.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.train.start_queue_runners(sess=None, coord=None, daemon=True, start=True, collection='queue_runners')` {#start_queue_runners}
-
-Starts all queue runners collected in the graph.
-
-This is a companion method to `add_queue_runner()`.  It just starts
-threads for all queue runners collected in the graph.  It returns
-the list of all threads.
-
-##### Args:
-
-
-*  <b>`sess`</b>: `Session` used to run the queue ops.  Defaults to the
-    default session.
-*  <b>`coord`</b>: Optional `Coordinator` for coordinating the started threads.
-*  <b>`daemon`</b>: Whether the threads should be marked as `daemons`, meaning
-    they don't block program exit.
-*  <b>`start`</b>: Set to `False` to only create the threads, not start them.
-*  <b>`collection`</b>: A `GraphKey` specifying the graph collection to
-    get the queue runners from.  Defaults to `GraphKeys.QUEUE_RUNNERS`.
-
-##### Returns:
-
-  A list of threads.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.string_input_producer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.string_input_producer.md
new file mode 100644
index 0000000000..5ca2a4cb86
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.string_input_producer.md
@@ -0,0 +1,32 @@
+### `tf.train.string_input_producer(string_tensor, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None)` {#string_input_producer}
+
+Output strings (e.g. filenames) to a queue for an input pipeline.
+
+##### Args:
+
+
+*  <b>`string_tensor`</b>: A 1-D string tensor with the strings to produce.
+*  <b>`num_epochs`</b>: An integer (optional). If specified, `string_input_producer`
+    produces each string from `string_tensor` `num_epochs` times before
+    generating an `OutOfRange` error. If not specified,
+    `string_input_producer` can cycle through the strings in `string_tensor`
+    an unlimited number of times.
+*  <b>`shuffle`</b>: Boolean. If true, the strings are randomly shuffled within each
+    epoch.
+*  <b>`seed`</b>: An integer (optional). Seed used if shuffle == True.
+*  <b>`capacity`</b>: An integer. Sets the queue capacity.
+*  <b>`shared_name`</b>: (optional). If set, this queue will be shared under the given
+    name across multiple sessions.
+*  <b>`name`</b>: A name for the operations (optional).
+
+##### Returns:
+
+  A queue with the output strings.  A `QueueRunner` for the Queue
+  is added to the current `Graph`'s `QUEUE_RUNNER` collection.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the string_tensor is a null Python list.  At runtime,
+  will fail with an assertion if string_tensor becomes a null tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.update_checkpoint_state.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.update_checkpoint_state.md
deleted file mode 100644
index 68747fc0c7..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.train.update_checkpoint_state.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.train.update_checkpoint_state(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None, latest_filename=None)` {#update_checkpoint_state}
-
-Updates the content of the 'checkpoint' file.
-
-This updates the checkpoint file containing a CheckpointState
-proto.
-
-##### Args:
-
-
-*  <b>`save_dir`</b>: Directory where the model was saved.
-*  <b>`model_checkpoint_path`</b>: The checkpoint file.
-*  <b>`all_model_checkpoint_paths`</b>: List of strings.  Paths to all not-yet-deleted
-    checkpoints, sorted from oldest to newest.  If this is a non-empty list,
-    the last element must be equal to model_checkpoint_path.  These paths
-    are also saved in the CheckpointState proto.
-*  <b>`latest_filename`</b>: Optional name of the checkpoint file.  Default to
-    'checkpoint'.
-
-##### Raises:
-
-
-*  <b>`RuntimeError`</b>: If the save paths conflict.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.transpose.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.transpose.md
new file mode 100644
index 0000000000..c6b76c7824
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.transpose.md
@@ -0,0 +1,49 @@
+### `tf.transpose(a, perm=None, name='transpose')` {#transpose}
+
+Transposes `a`. Permutes the dimensions according to `perm`.
+
+The returned tensor's dimension i will correspond to the input dimension
+`perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
+the rank of the input tensor. Hence by default, this operation performs a
+regular matrix transpose on 2-D input Tensors.
+
+For example:
+
+```python
+# 'x' is [[1 2 3]
+#         [4 5 6]]
+tf.transpose(x) ==> [[1 4]
+                     [2 5]
+                     [3 6]]
+
+# Equivalently
+tf.transpose(x, perm=[1, 0]) ==> [[1 4]
+                                  [2 5]
+                                  [3 6]]
+
+# 'perm' is more useful for n-dimensional tensors, for n > 2
+# 'x' is   [[[1  2  3]
+#            [4  5  6]]
+#           [[7  8  9]
+#            [10 11 12]]]
+# Take the transpose of the matrices in dimension-0
+tf.transpose(x, perm=[0, 2, 1]) ==> [[[1  4]
+                                      [2  5]
+                                      [3  6]]
+
+                                     [[7 10]
+                                      [8 11]
+                                      [9 12]]]
+```
+
+##### Args:
+
+
+*  <b>`a`</b>: A `Tensor`.
+*  <b>`perm`</b>: A permutation of the dimensions of `a`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A transposed `Tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.truncated_normal_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.truncated_normal_initializer.md
deleted file mode 100644
index 0a335333e2..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.truncated_normal_initializer.md
+++ /dev/null
@@ -1,31 +0,0 @@
-### `tf.truncated_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)` {#truncated_normal_initializer}
-
-Returns an initializer that generates a truncated normal distribution.
-
-These values are similar to values from a `random_normal_initializer`
-except that values more than two standard deviations from the mean
-are discarded and re-drawn. This is the recommended initializer for
-neural network weights and filters.
-
-##### Args:
-
-
-*  <b>`mean`</b>: a python scalar or a scalar tensor. Mean of the random values
-    to generate.
-*  <b>`stddev`</b>: a python scalar or a scalar tensor. Standard deviation of the
-    random values to generate.
-*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`dtype`</b>: The data type. Only floating point types are supported.
-
-##### Returns:
-
-  An initializer that generates tensors with a truncated normal
-  distribution.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.uniform_unit_scaling_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.uniform_unit_scaling_initializer.md
new file mode 100644
index 0000000000..6033fbf53a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.uniform_unit_scaling_initializer.md
@@ -0,0 +1,47 @@
+### `tf.uniform_unit_scaling_initializer(factor=1.0, seed=None, dtype=tf.float32, full_shape=None)` {#uniform_unit_scaling_initializer}
+
+Returns an initializer that generates tensors without scaling variance.
+
+When initializing a deep network, it is in principle advantageous to keep
+the scale of the input variance constant, so it does not explode or diminish
+by reaching the final layer. If the input is `x` and the operation `x * W`,
+and we want to initialize `W` uniformly at random, we need to pick `W` from
+
+    [-sqrt(3) / sqrt(dim), sqrt(3) / sqrt(dim)]
+
+to keep the scale intact, where `dim = W.shape[0]` (the size of the input).
+A similar calculation for convolutional networks gives an analogous result
+with `dim` equal to the product of the first 3 dimensions.  When
+nonlinearities are present, we need to multiply this by a constant `factor`.
+See [Sussillo et al., 2014](https://arxiv.org/abs/1412.6558)
+([pdf](http://arxiv.org/pdf/1412.6558.pdf)) for deeper motivation, experiments
+and the calculation of constants. In section 2.3 there, the constants were
+numerically computed: for a linear layer it's 1.0, relu: ~1.43, tanh: ~1.15.
+
+If the shape tuple `full_shape` is provided, the scale will be calculated from
+this predefined shape.  This is useful when a `Variable` is being partitioned
+across several shards, and each shard has a smaller shape than the whole.
+Since the shards are usually concatenated when used, the scale should be
+based on the shape of the whole.
+
+##### Args:
+
+
+*  <b>`factor`</b>: Float.  A multiplicative factor by which the values will be scaled.
+*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`dtype`</b>: The data type. Only floating point types are supported.
+*  <b>`full_shape`</b>: Tuple or list of integers.  The shape used for calculating
+    scale normalization (instead of the shape passed at creation time).
+    Useful when creating sharded variables via partitioning.
+
+##### Returns:
+
+  An initializer that generates tensors with unit variance.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.unpack.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.unpack.md
new file mode 100644
index 0000000000..cc4884c720
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.unpack.md
@@ -0,0 +1,32 @@
+### `tf.unpack(value, num=None, name='unpack')` {#unpack}
+
+Unpacks the outer dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
+
+Unpacks `num` tensors from `value` along the first dimension.
+If `num` is not specified (the default), it is inferred from `value`'s shape.
+If `value.shape[0]` is not known, `ValueError` is raised.
+
+The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in
+`output` has shape `value.shape[1:]`.
+
+This is the opposite of pack.  The numpy equivalent is
+
+    tf.unpack(x, n) = list(x)
+
+##### Args:
+
+
+*  <b>`value`</b>: A rank `R > 0` `Tensor` to be unpacked.
+*  <b>`num`</b>: An `int`. The first dimension of value. Automatically inferred if
+    `None` (the default).
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The list of `Tensor` objects unpacked from `value`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `num` is unspecified and cannot be inferred.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.DType.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.DType.md
new file mode 100644
index 0000000000..4c77a143e0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.DType.md
@@ -0,0 +1,206 @@
+Represents the type of the elements in a `Tensor`.
+
+The following `DType` objects are defined:
+
+* `tf.float16`: 16-bit half-precision floating-point.
+* `tf.float32`: 32-bit single-precision floating-point.
+* `tf.float64`: 64-bit double-precision floating-point.
+* `tf.bfloat16`: 16-bit truncated floating-point.
+* `tf.complex64`: 64-bit single-precision complex.
+* `tf.complex128`: 128-bit double-precision complex.
+
+* `tf.int8`: 8-bit signed integer.
+* `tf.uint8`: 8-bit unsigned integer.
+* `tf.uint16`: 16-bit unsigned integer.
+* `tf.int16`: 16-bit signed integer.
+* `tf.int32`: 32-bit signed integer.
+* `tf.int64`: 64-bit signed integer.
+
+* `tf.bool`: Boolean.
+
+* `tf.string`: String.
+
+* `tf.qint8`: Quantized 8-bit signed integer.
+* `tf.quint8`: Quantized 8-bit unsigned integer.
+* `tf.qint16`: Quantized 16-bit signed integer.
+* `tf.quint16`: Quantized 16-bit unsigned integer.
+* `tf.qint32`: Quantized 32-bit signed integer.
+
+In addition, variants of these types with the `_ref` suffix are
+defined for reference-typed tensors.
+
+The `tf.as_dtype()` function converts numpy types and string type
+names to a `DType` object.
+
+- - -
+
+#### `tf.DType.is_compatible_with(other)` {#DType.is_compatible_with}
+
+Returns True if the `other` DType will be converted to this DType.
+
+The conversion rules are as follows:
+
+```
+DType(T)       .is_compatible_with(DType(T))        == True
+DType(T)       .is_compatible_with(DType(T).as_ref) == True
+DType(T).as_ref.is_compatible_with(DType(T))        == False
+DType(T).as_ref.is_compatible_with(DType(T).as_ref) == True
+```
+
+##### Args:
+
+
+*  <b>`other`</b>: A `DType` (or object that may be converted to a `DType`).
+
+##### Returns:
+
+  True if a Tensor of the `other` `DType` will be implicitly converted to
+  this `DType`.
+
+
+- - -
+
+#### `tf.DType.name` {#DType.name}
+
+Returns the string name for this `DType`.
+
+
+- - -
+
+#### `tf.DType.base_dtype` {#DType.base_dtype}
+
+Returns a non-reference `DType` based on this `DType`.
+
+
+- - -
+
+#### `tf.DType.real_dtype` {#DType.real_dtype}
+
+Returns the dtype correspond to this dtype's real part.
+
+
+- - -
+
+#### `tf.DType.is_ref_dtype` {#DType.is_ref_dtype}
+
+Returns `True` if this `DType` represents a reference type.
+
+
+- - -
+
+#### `tf.DType.as_ref` {#DType.as_ref}
+
+Returns a reference `DType` based on this `DType`.
+
+
+- - -
+
+#### `tf.DType.is_floating` {#DType.is_floating}
+
+Returns whether this is a (real) floating point type.
+
+
+- - -
+
+#### `tf.DType.is_complex` {#DType.is_complex}
+
+Returns whether this is a complex floating point type.
+
+
+- - -
+
+#### `tf.DType.is_integer` {#DType.is_integer}
+
+Returns whether this is a (non-quantized) integer type.
+
+
+- - -
+
+#### `tf.DType.is_quantized` {#DType.is_quantized}
+
+Returns whether this is a quantized data type.
+
+
+- - -
+
+#### `tf.DType.is_unsigned` {#DType.is_unsigned}
+
+Returns whether this type is unsigned.
+
+Non-numeric, unordered, and quantized types are not considered unsigned, and
+this function returns `False`.
+
+##### Returns:
+
+  Whether a `DType` is unsigned.
+
+
+
+- - -
+
+#### `tf.DType.as_numpy_dtype` {#DType.as_numpy_dtype}
+
+Returns a `numpy.dtype` based on this `DType`.
+
+
+- - -
+
+#### `tf.DType.as_datatype_enum` {#DType.as_datatype_enum}
+
+Returns a `types_pb2.DataType` enum value based on this `DType`.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.DType.__init__(type_enum)` {#DType.__init__}
+
+Creates a new `DataType`.
+
+NOTE(mrry): In normal circumstances, you should not need to
+construct a `DataType` object directly. Instead, use the
+`tf.as_dtype()` function.
+
+##### Args:
+
+
+*  <b>`type_enum`</b>: A `types_pb2.DataType` enum value.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `type_enum` is not a value `types_pb2.DataType`.
+
+
+- - -
+
+#### `tf.DType.max` {#DType.max}
+
+Returns the maximum representable value in this data type.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if this is a non-numeric, unordered, or quantized type.
+
+
+- - -
+
+#### `tf.DType.min` {#DType.min}
+
+Returns the minimum representable value in this data type.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if this is a non-numeric, unordered, or quantized type.
+
+
+- - -
+
+#### `tf.DType.size` {#DType.size}
+
+
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Dimension.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Dimension.md
new file mode 100644
index 0000000000..f149b6cb65
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Dimension.md
@@ -0,0 +1,83 @@
+Represents the value of one dimension in a TensorShape.
+- - -
+
+#### `tf.Dimension.__init__(value)` {#Dimension.__init__}
+
+Creates a new Dimension with the given value.
+
+
+- - -
+
+#### `tf.Dimension.assert_is_compatible_with(other)` {#Dimension.assert_is_compatible_with}
+
+Raises an exception if `other` is not compatible with this Dimension.
+
+##### Args:
+
+
+*  <b>`other`</b>: Another Dimension.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` and `other` are not compatible (see
+    is_compatible_with).
+
+
+- - -
+
+#### `tf.Dimension.is_compatible_with(other)` {#Dimension.is_compatible_with}
+
+Returns true if `other` is compatible with this Dimension.
+
+Two known Dimensions are compatible if they have the same value.
+An unknown Dimension is compatible with all other Dimensions.
+
+##### Args:
+
+
+*  <b>`other`</b>: Another Dimension.
+
+##### Returns:
+
+  True if this Dimension and `other` are compatible.
+
+
+- - -
+
+#### `tf.Dimension.merge_with(other)` {#Dimension.merge_with}
+
+Returns a Dimension that combines the information in `self` and `other`.
+
+Dimensions are combined as follows:
+
+    Dimension(n)   .merge_with(Dimension(n))    == Dimension(n)
+    Dimension(n)   .merge_with(Dimension(None)) == Dimension(n)
+    Dimension(None).merge_with(Dimension(n))    == Dimension(n)
+    Dimension(None).merge_with(Dimension(None)) == Dimension(None)
+    Dimension(n)   .merge_with(Dimension(m)) raises ValueError for n != m
+
+##### Args:
+
+
+*  <b>`other`</b>: Another Dimension.
+
+##### Returns:
+
+  A Dimension containing the combined information of `self` and
+  `other`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` and `other` are not compatible (see
+    is_compatible_with).
+
+
+- - -
+
+#### `tf.Dimension.value` {#Dimension.value}
+
+The value of this dimension, or None if it is unknown.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.FixedLenSequenceFeature.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.FixedLenSequenceFeature.md
new file mode 100644
index 0000000000..607b81a9bf
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.FixedLenSequenceFeature.md
@@ -0,0 +1,31 @@
+Configuration for a dense input feature in a sequence item.
+
+To treat a sparse input as dense, provide `allow_missing=True`; otherwise,
+the parse functions will fail on any examples missing this feature.
+
+Fields:
+  shape: Shape of input data.
+  dtype: Data type of input.
+  allow_missing: Whether to allow this feature to be missing from a feature
+    list item.
+- - -
+
+#### `tf.FixedLenSequenceFeature.allow_missing` {#FixedLenSequenceFeature.allow_missing}
+
+Alias for field number 2
+
+
+- - -
+
+#### `tf.FixedLenSequenceFeature.dtype` {#FixedLenSequenceFeature.dtype}
+
+Alias for field number 1
+
+
+- - -
+
+#### `tf.FixedLenSequenceFeature.shape` {#FixedLenSequenceFeature.shape}
+
+Alias for field number 0
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.FixedLengthRecordReader.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.FixedLengthRecordReader.md
new file mode 100644
index 0000000000..e8a94cb825
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.FixedLengthRecordReader.md
@@ -0,0 +1,148 @@
+A Reader that outputs fixed-length records from a file.
+
+See ReaderBase for supported methods.
+- - -
+
+#### `tf.FixedLengthRecordReader.__init__(record_bytes, header_bytes=None, footer_bytes=None, name=None)` {#FixedLengthRecordReader.__init__}
+
+Create a FixedLengthRecordReader.
+
+##### Args:
+
+
+*  <b>`record_bytes`</b>: An int.
+*  <b>`header_bytes`</b>: An optional int. Defaults to 0.
+*  <b>`footer_bytes`</b>: An optional int. Defaults to 0.
+*  <b>`name`</b>: A name for the operation (optional).
+
+
+- - -
+
+#### `tf.FixedLengthRecordReader.num_records_produced(name=None)` {#FixedLengthRecordReader.num_records_produced}
+
+Returns the number of records this reader has produced.
+
+This is the same as the number of Read executions that have
+succeeded.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.FixedLengthRecordReader.num_work_units_completed(name=None)` {#FixedLengthRecordReader.num_work_units_completed}
+
+Returns the number of work units this reader has finished processing.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.FixedLengthRecordReader.read(queue, name=None)` {#FixedLengthRecordReader.read}
+
+Returns the next record (key, value pair) produced by a reader.
+
+Will dequeue a work unit from queue if necessary (e.g. when the
+Reader needs to start reading from a new file since it has
+finished with the previous file).
+
+##### Args:
+
+
+*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
+    to a Queue, with string work items.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of Tensors (key, value).
+
+*  <b>`key`</b>: A string scalar Tensor.
+*  <b>`value`</b>: A string scalar Tensor.
+
+
+- - -
+
+#### `tf.FixedLengthRecordReader.reader_ref` {#FixedLengthRecordReader.reader_ref}
+
+Op that implements the reader.
+
+
+- - -
+
+#### `tf.FixedLengthRecordReader.reset(name=None)` {#FixedLengthRecordReader.reset}
+
+Restore a reader to its initial clean state.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.FixedLengthRecordReader.restore_state(state, name=None)` {#FixedLengthRecordReader.restore_state}
+
+Restore a reader to a previously saved state.
+
+Not all Readers support being restored, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`state`</b>: A string Tensor.
+    Result of a SerializeState of a Reader with matching type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.FixedLengthRecordReader.serialize_state(name=None)` {#FixedLengthRecordReader.serialize_state}
+
+Produce a string tensor that encodes the state of a reader.
+
+Not all Readers support being serialized, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A string Tensor.
+
+
+- - -
+
+#### `tf.FixedLengthRecordReader.supports_serialize` {#FixedLengthRecordReader.supports_serialize}
+
+Whether the Reader implementation can serialize its state.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.GraphKeys.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.GraphKeys.md
new file mode 100644
index 0000000000..1d656f4018
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.GraphKeys.md
@@ -0,0 +1,36 @@
+Standard names to use for graph collections.
+
+The standard library uses various well-known names to collect and
+retrieve values associated with a graph. For example, the
+`tf.Optimizer` subclasses default to optimizing the variables
+collected under `tf.GraphKeys.TRAINABLE_VARIABLES` if none is
+specified, but it is also possible to pass an explicit list of
+variables.
+
+The following standard keys are defined:
+
+* `VARIABLES`: the `Variable` objects that comprise a model, and
+  must be saved and restored together. See
+  [`tf.all_variables()`](../../api_docs/python/state_ops.md#all_variables)
+  for more details.
+* `TRAINABLE_VARIABLES`: the subset of `Variable` objects that will
+  be trained by an optimizer. See
+  [`tf.trainable_variables()`](../../api_docs/python/state_ops.md#trainable_variables)
+  for more details.
+* `SUMMARIES`: the summary `Tensor` objects that have been created in the
+  graph. See
+  [`tf.merge_all_summaries()`](../../api_docs/python/train.md#merge_all_summaries)
+  for more details.
+* `QUEUE_RUNNERS`: the `QueueRunner` objects that are used to
+  produce input for a computation. See
+  [`tf.start_queue_runners()`](../../api_docs/python/train.md#start_queue_runners)
+  for more details.
+* `MOVING_AVERAGE_VARIABLES`: the subset of `Variable` objects that will also
+  keep moving averages.  See
+  [`tf.moving_average_variables()`](../../api_docs/python/state_ops.md#moving_average_variables)
+  for more details.
+* `REGULARIZATION_LOSSES`: regularization losses collected during graph
+  construction.
+* `WEIGHTS`: weights inside neural network layers
+* `BIASES`: biases inside neural network layers
+* `ACTIVATIONS`: activations of neural network layers
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Session.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Session.md
new file mode 100644
index 0000000000..62982698dd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Session.md
@@ -0,0 +1,236 @@
+A class for running TensorFlow operations.
+
+A `Session` object encapsulates the environment in which `Operation`
+objects are executed, and `Tensor` objects are evaluated. For
+example:
+
+```python
+# Build a graph.
+a = tf.constant(5.0)
+b = tf.constant(6.0)
+c = a * b
+
+# Launch the graph in a session.
+sess = tf.Session()
+
+# Evaluate the tensor `c`.
+print(sess.run(c))
+```
+
+A session may own resources, such as
+[variables](../../api_docs/python/state_ops.md#Variable), [queues](../../api_docs/python/io_ops.md#QueueBase),
+and [readers](../../api_docs/python/io_ops.md#ReaderBase). It is important to release
+these resources when they are no longer required. To do this, either
+invoke the [`close()`](#Session.close) method on the session, or use
+the session as a context manager. The following two examples are
+equivalent:
+
+```python
+# Using the `close()` method.
+sess = tf.Session()
+sess.run(...)
+sess.close()
+
+# Using the context manager.
+with tf.Session() as sess:
+  sess.run(...)
+```
+
+The [`ConfigProto`]
+(https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)
+protocol buffer exposes various configuration options for a
+session. For example, to create a session that uses soft constraints
+for device placement, and log the resulting placement decisions,
+create a session as follows:
+
+```python
+# Launch the graph in a session that allows soft device placement and
+# logs the placement decisions.
+sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
+                                        log_device_placement=True))
+```
+
+- - -
+
+#### `tf.Session.__init__(target='', graph=None, config=None)` {#Session.__init__}
+
+Creates a new TensorFlow session.
+
+If no `graph` argument is specified when constructing the session,
+the default graph will be launched in the session. If you are
+using more than one graph (created with `tf.Graph()` in the same
+process, you will have to use different sessions for each graph,
+but each graph can be used in multiple sessions. In this case, it
+is often clearer to pass the graph to be launched explicitly to
+the session constructor.
+
+##### Args:
+
+
+*  <b>`target`</b>: (Optional.) The execution engine to connect to.
+    Defaults to using an in-process engine. At present, no value
+    other than the empty string is supported.
+*  <b>`graph`</b>: (Optional.) The `Graph` to be launched (described above).
+*  <b>`config`</b>: (Optional.) A [`ConfigProto`](https://www.tensorflow.org/code/tensorflow/core/protobuf/config.proto)
+    protocol buffer with configuration options for the session.
+
+
+- - -
+
+#### `tf.Session.run(fetches, feed_dict=None, options=None, run_metadata=None)` {#Session.run}
+
+Runs the operations and evaluates the tensors in `fetches`.
+
+This method runs one "step" of TensorFlow computation, by
+running the necessary graph fragment to execute every `Operation`
+and evaluate every `Tensor` in `fetches`, substituting the values in
+`feed_dict` for the corresponding input values.
+
+The `fetches` argument may be a list of graph elements or a single
+graph element, and these determine the return value of this
+method. A graph element can be one of the following types:
+
+* If the *i*th element of `fetches` is an
+  [`Operation`](../../api_docs/python/framework.md#Operation), the *i*th
+  return value will be `None`.
+* If the *i*th element of `fetches` is a
+  [`Tensor`](../../api_docs/python/framework.md#Tensor), the *i*th return
+  value will be a numpy ndarray containing the value of that tensor.
+* If the *i*th element of `fetches` is a
+  [`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
+  the *i*th return value will be a
+  [`SparseTensorValue`](../../api_docs/python/sparse_ops.md#SparseTensorValue)
+  containing the value of that sparse tensor.
+* If the *i*th element of `fetches` is produced by a `get_tensor_handle` op,
+  the *i*th return value will be a numpy ndarray containing the handle of
+  that tensor.
+
+The optional `feed_dict` argument allows the caller to override
+the value of tensors in the graph. Each key in `feed_dict` can be
+one of the following types:
+
+* If the key is a [`Tensor`](../../api_docs/python/framework.md#Tensor), the
+  value may be a Python scalar, string, list, or numpy ndarray
+  that can be converted to the same `dtype` as that
+  tensor. Additionally, if the key is a
+  [placeholder](../../api_docs/python/io_ops.md#placeholder), the shape of
+  the value will be checked for compatibility with the placeholder.
+* If the key is a
+  [`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
+  the value should be a
+  [`SparseTensorValue`](../../api_docs/python/sparse_ops.md#SparseTensorValue).
+
+Each value in `feed_dict` must be convertible to a numpy array of the dtype
+of the corresponding key.
+
+The optional `options` argument expects a [`RunOptions`] proto. The options
+allow controlling the behavior of this particular step (e.g. turning tracing
+on).
+
+The optional `run_metadata` argument expects a [`RunMetadata`] proto. When
+appropriate, the non-Tensor output of this step will be collected there. For
+example, when users turn on tracing in `options`, the profiled info will be
+collected into this argument and passed back.
+
+##### Args:
+
+
+*  <b>`fetches`</b>: A single graph element, or a list of graph elements
+    (described above).
+*  <b>`feed_dict`</b>: A dictionary that maps graph elements to values
+    (described above).
+*  <b>`options`</b>: A [`RunOptions`] protocol buffer
+*  <b>`run_metadata`</b>: A [`RunMetadata`] protocol buffer
+
+##### Returns:
+
+  Either a single value if `fetches` is a single graph element, or
+  a list of values if `fetches` is a list (described above).
+
+##### Raises:
+
+
+*  <b>`RuntimeError`</b>: If this `Session` is in an invalid state (e.g. has been
+    closed).
+*  <b>`TypeError`</b>: If `fetches` or `feed_dict` keys are of an inappropriate type.
+*  <b>`ValueError`</b>: If `fetches` or `feed_dict` keys are invalid or refer to a
+    `Tensor` that doesn't exist.
+
+
+- - -
+
+#### `tf.Session.close()` {#Session.close}
+
+Closes this session.
+
+Calling this method frees all resources associated with the session.
+
+##### Raises:
+
+  tf.errors.OpError: Or one of its subclasses if an error occurs while
+    closing the TensorFlow session.
+
+
+
+- - -
+
+#### `tf.Session.graph` {#Session.graph}
+
+The graph that was launched in this session.
+
+
+
+- - -
+
+#### `tf.Session.as_default()` {#Session.as_default}
+
+Returns a context manager that makes this object the default session.
+
+Use with the `with` keyword to specify that calls to
+[`Operation.run()`](../../api_docs/python/framework.md#Operation.run) or
+[`Tensor.run()`](../../api_docs/python/framework.md#Tensor.run) should be
+executed in this session.
+
+```python
+c = tf.constant(..)
+sess = tf.Session()
+
+with sess.as_default():
+  assert tf.get_default_session() is sess
+  print(c.eval())
+```
+
+To get the current default session, use
+[`tf.get_default_session()`](#get_default_session).
+
+
+*N.B.* The `as_default` context manager *does not* close the
+session when you exit the context, and you must close the session
+explicitly.
+
+```python
+c = tf.constant(...)
+sess = tf.Session()
+with sess.as_default():
+  print(c.eval())
+# ...
+with sess.as_default():
+  print(c.eval())
+
+sess.close()
+```
+
+Alternatively, you can use `with tf.Session():` to create a
+session that is automatically closed on exiting the context,
+including when an uncaught exception is raised.
+
+*N.B.* The default graph is a property of the current thread. If you
+create a new thread, and wish to use the default session in that
+thread, you must explicitly add a `with sess.as_default():` in that
+thread's function.
+
+##### Returns:
+
+  A context manager using this session as the default session.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.SparseTensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.SparseTensor.md
new file mode 100644
index 0000000000..a999b3862f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.SparseTensor.md
@@ -0,0 +1,143 @@
+Represents a sparse tensor.
+
+Tensorflow represents a sparse tensor as three separate dense tensors:
+`indices`, `values`, and `shape`.  In Python, the three tensors are
+collected into a `SparseTensor` class for ease of use.  If you have separate
+`indices`, `values`, and `shape` tensors, wrap them in a `SparseTensor`
+object before passing to the ops below.
+
+Concretely, the sparse tensor `SparseTensor(indices, values, shape)` is
+
+* `indices`: A 2-D int64 tensor of shape `[N, ndims]`.
+* `values`: A 1-D tensor of any type and shape `[N]`.
+* `shape`: A 1-D int64 tensor of shape `[ndims]`.
+
+where `N` and `ndims` are the number of values, and number of dimensions in
+the `SparseTensor` respectively.
+
+The corresponding dense tensor satisfies
+
+```python
+dense.shape = shape
+dense[tuple(indices[i])] = values[i]
+```
+
+By convention, `indices` should be sorted in row-major order (or equivalently
+lexicographic order on the tuples `indices[i]`).  This is not enforced when
+`SparseTensor` objects are constructed, but most ops assume correct ordering.
+If the ordering of sparse tensor `st` is wrong, a fixed version can be
+obtained by calling `tf.sparse_reorder(st)`.
+
+Example: The sparse tensor
+
+```python
+SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], shape=[3, 4])
+```
+
+represents the dense tensor
+
+```python
+[[1, 0, 0, 0]
+ [0, 0, 2, 0]
+ [0, 0, 0, 0]]
+```
+
+- - -
+
+#### `tf.SparseTensor.__init__(indices, values, shape)` {#SparseTensor.__init__}
+
+Creates a `SparseTensor`.
+
+##### Args:
+
+
+*  <b>`indices`</b>: A 2-D int64 tensor of shape `[N, ndims]`.
+*  <b>`values`</b>: A 1-D tensor of any type and shape `[N]`.
+*  <b>`shape`</b>: A 1-D int64 tensor of shape `[ndims]`.
+
+##### Returns:
+
+  A `SparseTensor`
+
+
+- - -
+
+#### `tf.SparseTensor.indices` {#SparseTensor.indices}
+
+The indices of non-zero values in the represented dense tensor.
+
+##### Returns:
+
+  A 2-D Tensor of int64 with shape `[N, ndims]`, where `N` is the
+    number of non-zero values in the tensor, and `ndims` is the rank.
+
+
+- - -
+
+#### `tf.SparseTensor.values` {#SparseTensor.values}
+
+The non-zero values in the represented dense tensor.
+
+##### Returns:
+
+  A 1-D Tensor of any data type.
+
+
+- - -
+
+#### `tf.SparseTensor.dtype` {#SparseTensor.dtype}
+
+The `DType` of elements in this tensor.
+
+
+- - -
+
+#### `tf.SparseTensor.shape` {#SparseTensor.shape}
+
+A 1-D Tensor of int64 representing the shape of the dense tensor.
+
+
+- - -
+
+#### `tf.SparseTensor.graph` {#SparseTensor.graph}
+
+The `Graph` that contains the index, value, and shape tensors.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.SparseTensor.eval(feed_dict=None, session=None)` {#SparseTensor.eval}
+
+Evaluates this sparse tensor in a `Session`.
+
+Calling this method will execute all preceding operations that
+produce the inputs needed for the operation that produces this
+tensor.
+
+*N.B.* Before invoking `SparseTensor.eval()`, its graph must have been
+launched in a session, and either a default session must be
+available, or `session` must be specified explicitly.
+
+##### Args:
+
+
+*  <b>`feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
+    See [`Session.run()`](../../api_docs/python/client.md#Session.run) for a
+    description of the valid feed values.
+*  <b>`session`</b>: (Optional.) The `Session` to be used to evaluate this sparse
+    tensor. If none, the default session will be used.
+
+##### Returns:
+
+  A `SparseTensorValue` object.
+
+
+- - -
+
+#### `tf.SparseTensor.from_value(cls, sparse_tensor_value)` {#SparseTensor.from_value}
+
+
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.TFRecordReader.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.TFRecordReader.md
deleted file mode 100644
index 31c6ffacfb..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.TFRecordReader.md
+++ /dev/null
@@ -1,145 +0,0 @@
-A Reader that outputs the records from a TFRecords file.
-
-See ReaderBase for supported methods.
-- - -
-
-#### `tf.TFRecordReader.__init__(name=None)` {#TFRecordReader.__init__}
-
-Create a TFRecordReader.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-
-- - -
-
-#### `tf.TFRecordReader.num_records_produced(name=None)` {#TFRecordReader.num_records_produced}
-
-Returns the number of records this reader has produced.
-
-This is the same as the number of Read executions that have
-succeeded.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.TFRecordReader.num_work_units_completed(name=None)` {#TFRecordReader.num_work_units_completed}
-
-Returns the number of work units this reader has finished processing.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.TFRecordReader.read(queue, name=None)` {#TFRecordReader.read}
-
-Returns the next record (key, value pair) produced by a reader.
-
-Will dequeue a work unit from queue if necessary (e.g. when the
-Reader needs to start reading from a new file since it has
-finished with the previous file).
-
-##### Args:
-
-
-*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
-    to a Queue, with string work items.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of Tensors (key, value).
-
-*  <b>`key`</b>: A string scalar Tensor.
-*  <b>`value`</b>: A string scalar Tensor.
-
-
-- - -
-
-#### `tf.TFRecordReader.reader_ref` {#TFRecordReader.reader_ref}
-
-Op that implements the reader.
-
-
-- - -
-
-#### `tf.TFRecordReader.reset(name=None)` {#TFRecordReader.reset}
-
-Restore a reader to its initial clean state.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.TFRecordReader.restore_state(state, name=None)` {#TFRecordReader.restore_state}
-
-Restore a reader to a previously saved state.
-
-Not all Readers support being restored, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`state`</b>: A string Tensor.
-    Result of a SerializeState of a Reader with matching type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.TFRecordReader.serialize_state(name=None)` {#TFRecordReader.serialize_state}
-
-Produce a string tensor that encodes the state of a reader.
-
-Not all Readers support being serialized, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A string Tensor.
-
-
-- - -
-
-#### `tf.TFRecordReader.supports_serialize` {#TFRecordReader.supports_serialize}
-
-Whether the Reader implementation can serialize its state.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Variable.from_proto.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Variable.from_proto.md
deleted file mode 100644
index 5b10d329bc..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Variable.from_proto.md
+++ /dev/null
@@ -1,4 +0,0 @@
-#### `tf.Variable.from_proto(variable_def)` {#Variable.from_proto}
-
-Returns a `Variable` object created from `variable_def`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Variable.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Variable.md
deleted file mode 100644
index b300fac583..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.Variable.md
+++ /dev/null
@@ -1,460 +0,0 @@
-See the [Variables How To](../../how_tos/variables/index.md) for a high
-level overview.
-
-A variable maintains state in the graph across calls to `run()`. You add a
-variable to the graph by constructing an instance of the class `Variable`.
-
-The `Variable()` constructor requires an initial value for the variable,
-which can be a `Tensor` of any type and shape. The initial value defines the
-type and shape of the variable. After construction, the type and shape of
-the variable are fixed. The value can be changed using one of the assign
-methods.
-
-If you want to change the shape of a variable later you have to use an
-`assign` Op with `validate_shape=False`.
-
-Just like any `Tensor`, variables created with `Variable()` can be used as
-inputs for other Ops in the graph. Additionally, all the operators
-overloaded for the `Tensor` class are carried over to variables, so you can
-also add nodes to the graph by just doing arithmetic on variables.
-
-```python
-import tensorflow as tf
-
-# Create a variable.
-w = tf.Variable(<initial-value>, name=<optional-name>)
-
-# Use the variable in the graph like any Tensor.
-y = tf.matmul(w, ...another variable or tensor...)
-
-# The overloaded operators are available too.
-z = tf.sigmoid(w + y)
-
-# Assign a new value to the variable with `assign()` or a related method.
-w.assign(w + 1.0)
-w.assign_add(1.0)
-```
-
-When you launch the graph, variables have to be explicitly initialized before
-you can run Ops that use their value. You can initialize a variable by
-running its *initializer op*, restoring the variable from a save file, or
-simply running an `assign` Op that assigns a value to the variable. In fact,
-the variable *initializer op* is just an `assign` Op that assigns the
-variable's initial value to the variable itself.
-
-```python
-# Launch the graph in a session.
-with tf.Session() as sess:
-    # Run the variable initializer.
-    sess.run(w.initializer)
-    # ...you now can run ops that use the value of 'w'...
-```
-
-The most common initialization pattern is to use the convenience function
-`initialize_all_variables()` to add an Op to the graph that initializes
-all the variables. You then run that Op after launching the graph.
-
-```python
-# Add an Op to initialize all variables.
-init_op = tf.initialize_all_variables()
-
-# Launch the graph in a session.
-with tf.Session() as sess:
-    # Run the Op that initializes all variables.
-    sess.run(init_op)
-    # ...you can now run any Op that uses variable values...
-```
-
-If you need to create a variable with an initial value dependent on another
-variable, use the other variable's `initialized_value()`. This ensures that
-variables are initialized in the right order.
-
-All variables are automatically collected in the graph where they are
-created. By default, the constructor adds the new variable to the graph
-collection `GraphKeys.VARIABLES`. The convenience function
-`all_variables()` returns the contents of that collection.
-
-When building a machine learning model it is often convenient to distinguish
-betwen variables holding the trainable model parameters and other variables
-such as a `global step` variable used to count training steps. To make this
-easier, the variable constructor supports a `trainable=<bool>` parameter. If
-`True`, the new variable is also added to the graph collection
-`GraphKeys.TRAINABLE_VARIABLES`. The convenience function
-`trainable_variables()` returns the contents of this collection. The
-various `Optimizer` classes use this collection as the default list of
-variables to optimize.
-
-
-Creating a variable.
-
-- - -
-
-#### `tf.Variable.__init__(initial_value=None, trainable=True, collections=None, validate_shape=True, caching_device=None, name=None, variable_def=None, dtype=None)` {#Variable.__init__}
-
-Creates a new variable with value `initial_value`.
-
-The new variable is added to the graph collections listed in `collections`,
-which defaults to `[GraphKeys.VARIABLES]`.
-
-If `trainable` is `True` the variable is also added to the graph collection
-`GraphKeys.TRAINABLE_VARIABLES`.
-
-This constructor creates both a `variable` Op and an `assign` Op to set the
-variable to its initial value.
-
-##### Args:
-
-
-*  <b>`initial_value`</b>: A `Tensor`, or Python object convertible to a `Tensor`,
-    which is the initial value for the Variable. The initial value must have
-    a shape specified unless `validate_shape` is set to False. Can also be a
-    callable with no argument that returns the initial value when called. In
-    that case, `dtype` must be specified. (Note that initializer functions
-    from init_ops.py must first be bound to a shape before being used here.)
-*  <b>`trainable`</b>: If `True`, the default, also adds the variable to the graph
-    collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as
-    the default list of variables to use by the `Optimizer` classes.
-*  <b>`collections`</b>: List of graph collections keys. The new variable is added to
-    these collections. Defaults to `[GraphKeys.VARIABLES]`.
-*  <b>`validate_shape`</b>: If `False`, allows the variable to be initialized with a
-    value of unknown shape. If `True`, the default, the shape of
-    `initial_value` must be known.
-*  <b>`caching_device`</b>: Optional device string describing where the Variable
-    should be cached for reading.  Defaults to the Variable's device.
-    If not `None`, caches on another device.  Typical use is to cache
-    on the device where the Ops using the Variable reside, to deduplicate
-    copying through `Switch` and other conditional statements.
-*  <b>`name`</b>: Optional name for the variable. Defaults to `'Variable'` and gets
-    uniquified automatically.
-*  <b>`variable_def`</b>: `VariableDef` protocol buffer. If not `None`, recreates
-    the Variable object with its contents. `variable_def` and the other
-    arguments are mutually exclusive.
-*  <b>`dtype`</b>: If set, initial_value will be converted to the given type.
-    If `None`, either the datatype will be kept (if `initial_value` is
-    a Tensor), or `convert_to_tensor` will decide.
-
-##### Returns:
-
-  A Variable.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If both `variable_def` and initial_value are specified.
-*  <b>`ValueError`</b>: If the initial value is not specified, or does not have a
-    shape and `validate_shape` is `True`.
-
-
-- - -
-
-#### `tf.Variable.initialized_value()` {#Variable.initialized_value}
-
-Returns the value of the initialized variable.
-
-You should use this instead of the variable itself to initialize another
-variable with a value that depends on the value of this variable.
-
-```python
-# Initialize 'v' with a random tensor.
-v = tf.Variable(tf.truncated_normal([10, 40]))
-# Use `initialized_value` to guarantee that `v` has been
-# initialized before its value is used to initialize `w`.
-# The random values are picked only once.
-w = tf.Variable(v.initialized_value() * 2.0)
-```
-
-##### Returns:
-
-  A `Tensor` holding the value of this variable after its initializer
-  has run.
-
-
-
-Changing a variable value.
-
-- - -
-
-#### `tf.Variable.assign(value, use_locking=False)` {#Variable.assign}
-
-Assigns a new value to the variable.
-
-This is essentially a shortcut for `assign(self, value)`.
-
-##### Args:
-
-
-*  <b>`value`</b>: A `Tensor`. The new value for this variable.
-*  <b>`use_locking`</b>: If `True`, use locking during the assignment.
-
-##### Returns:
-
-  A `Tensor` that will hold the new value of this variable after
-  the assignment has completed.
-
-
-- - -
-
-#### `tf.Variable.assign_add(delta, use_locking=False)` {#Variable.assign_add}
-
-Adds a value to this variable.
-
- This is essentially a shortcut for `assign_add(self, delta)`.
-
-##### Args:
-
-
-*  <b>`delta`</b>: A `Tensor`. The value to add to this variable.
-*  <b>`use_locking`</b>: If `True`, use locking during the operation.
-
-##### Returns:
-
-  A `Tensor` that will hold the new value of this variable after
-  the addition has completed.
-
-
-- - -
-
-#### `tf.Variable.assign_sub(delta, use_locking=False)` {#Variable.assign_sub}
-
-Subtracts a value from this variable.
-
-This is essentially a shortcut for `assign_sub(self, delta)`.
-
-##### Args:
-
-
-*  <b>`delta`</b>: A `Tensor`. The value to subtract from this variable.
-*  <b>`use_locking`</b>: If `True`, use locking during the operation.
-
-##### Returns:
-
-  A `Tensor` that will hold the new value of this variable after
-  the subtraction has completed.
-
-
-- - -
-
-#### `tf.Variable.scatter_sub(sparse_delta, use_locking=False)` {#Variable.scatter_sub}
-
-Subtracts `IndexedSlices` from this variable.
-
-This is essentially a shortcut for `scatter_sub(self, sparse_delta.indices,
-sparse_delta.values)`.
-
-##### Args:
-
-
-*  <b>`sparse_delta`</b>: `IndexedSlices` to be subtracted from this variable.
-*  <b>`use_locking`</b>: If `True`, use locking during the operation.
-
-##### Returns:
-
-  A `Tensor` that will hold the new value of this variable after
-  the scattered subtraction has completed.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `sparse_delta` is not an `IndexedSlices`.
-
-
-- - -
-
-#### `tf.Variable.count_up_to(limit)` {#Variable.count_up_to}
-
-Increments this variable until it reaches `limit`.
-
-When that Op is run it tries to increment the variable by `1`. If
-incrementing the variable would bring it above `limit` then the Op raises
-the exception `OutOfRangeError`.
-
-If no error is raised, the Op outputs the value of the variable before
-the increment.
-
-This is essentially a shortcut for `count_up_to(self, limit)`.
-
-##### Args:
-
-
-*  <b>`limit`</b>: value at which incrementing the variable raises an error.
-
-##### Returns:
-
-  A `Tensor` that will hold the variable value before the increment. If no
-  other Op modifies this variable, the values produced will all be
-  distinct.
-
-
-
-- - -
-
-#### `tf.Variable.eval(session=None)` {#Variable.eval}
-
-In a session, computes and returns the value of this variable.
-
-This is not a graph construction method, it does not add ops to the graph.
-
-This convenience method requires a session where the graph containing this
-variable has been launched. If no session is passed, the default session is
-used.  See the [Session class](../../api_docs/python/client.md#Session) for
-more information on launching a graph and on sessions.
-
-```python
-v = tf.Variable([1, 2])
-init = tf.initialize_all_variables()
-
-with tf.Session() as sess:
-    sess.run(init)
-    # Usage passing the session explicitly.
-    print(v.eval(sess))
-    # Usage with the default session.  The 'with' block
-    # above makes 'sess' the default session.
-    print(v.eval())
-```
-
-##### Args:
-
-
-*  <b>`session`</b>: The session to use to evaluate this variable. If
-    none, the default session is used.
-
-##### Returns:
-
-  A numpy `ndarray` with a copy of the value of this variable.
-
-
-
-Properties.
-
-- - -
-
-#### `tf.Variable.name` {#Variable.name}
-
-The name of this variable.
-
-
-- - -
-
-#### `tf.Variable.dtype` {#Variable.dtype}
-
-The `DType` of this variable.
-
-
-- - -
-
-#### `tf.Variable.get_shape()` {#Variable.get_shape}
-
-The `TensorShape` of this variable.
-
-##### Returns:
-
-  A `TensorShape`.
-
-
-- - -
-
-#### `tf.Variable.device` {#Variable.device}
-
-The device of this variable.
-
-
-- - -
-
-#### `tf.Variable.initializer` {#Variable.initializer}
-
-The initializer operation for this variable.
-
-
-- - -
-
-#### `tf.Variable.graph` {#Variable.graph}
-
-The `Graph` of this variable.
-
-
-- - -
-
-#### `tf.Variable.op` {#Variable.op}
-
-The `Operation` of this variable.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.Variable.from_proto(variable_def)` {#Variable.from_proto}
-
-Returns a `Variable` object created from `variable_def`.
-
-
-- - -
-
-#### `tf.Variable.initial_value` {#Variable.initial_value}
-
-Returns the Tensor used as the initial value for the variable.
-
-Note that this is different from `initialized_value()` which runs
-the op that initializes the variable before returning its value.
-This method returns the tensor that is used by the op that initializes
-the variable.
-
-##### Returns:
-
-  A `Tensor`.
-
-
-- - -
-
-#### `tf.Variable.ref()` {#Variable.ref}
-
-Returns a reference to this variable.
-
-You usually do not need to call this method as all ops that need a reference
-to the variable call it automatically.
-
-Returns is a `Tensor` which holds a reference to the variable.  You can
-assign a new value to the variable by passing the tensor to an assign op.
-See [`value()`](#Variable.value) if you want to get the value of the
-variable.
-
-##### Returns:
-
-  A `Tensor` that is a reference to the variable.
-
-
-- - -
-
-#### `tf.Variable.to_proto()` {#Variable.to_proto}
-
-Converts a `Variable` to a `VariableDef` protocol buffer.
-
-##### Returns:
-
-  A `VariableDef` protocol buffer.
-
-
-- - -
-
-#### `tf.Variable.value()` {#Variable.value}
-
-Returns the last snapshot of this variable.
-
-You usually do not need to call this method as all ops that need the value
-of the variable call it automatically through a `convert_to_tensor()` call.
-
-Returns a `Tensor` which holds the value of the variable.  You can not
-assign a new value to this tensor as it is not a reference to the variable.
-See [`ref()`](#Variable.ref) if you want to get a reference to the
-variable.
-
-To avoid copies, if the consumer of the returned value is on the same device
-as the variable, this actually returns the live value of the variable, not
-a copy.  Updates to the variable are seen by the consumer.  If the consumer
-is on a different device it will get a copy of the variable.
-
-##### Returns:
-
-  A `Tensor` containing the value of the variable.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.accumulate_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.accumulate_n.md
new file mode 100644
index 0000000000..a85d0d7f87
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.accumulate_n.md
@@ -0,0 +1,37 @@
+### `tf.accumulate_n(inputs, shape=None, tensor_dtype=None, name=None)` {#accumulate_n}
+
+Returns the element-wise sum of a list of tensors.
+
+Optionally, pass `shape` and `tensor_dtype` for shape and type checking,
+otherwise, these are inferred.
+
+For example:
+
+```python
+# tensor 'a' is [[1, 2], [3, 4]]
+# tensor `b` is [[5, 0], [0, 6]]
+tf.accumulate_n([a, b, a]) ==> [[7, 4], [6, 14]]
+
+# Explicitly pass shape and type
+tf.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
+  ==> [[7, 4], [6, 14]]
+```
+
+##### Args:
+
+
+*  <b>`inputs`</b>: A list of `Tensor` objects, each with same shape and type.
+*  <b>`shape`</b>: Shape of elements of `inputs`.
+*  <b>`tensor_dtype`</b>: The type of `inputs`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of same shape and type as the elements of `inputs`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `inputs` don't all have same shape and dtype or the shape
+  cannot be inferred.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.assert_equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.assert_equal.md
new file mode 100644
index 0000000000..ea4fd3a1fd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.assert_equal.md
@@ -0,0 +1,35 @@
+### `tf.assert_equal(x, y, data=None, summarize=None, name=None)` {#assert_equal}
+
+Assert the condition `x == y` holds element-wise.
+
+Example of adding a dependency to an operation:
+
+```python
+with tf.control_dependencies([tf.assert_equal(x, y)]):
+  output = tf.reduce_sum(x)
+```
+
+Example of adding dependency to the tensor being checked:
+
+```python
+x = tf.with_dependencies([tf.assert_equal(x, y)], x)
+```
+
+This condition holds if for every pair of (possibly broadcast) elements
+`x[i]`, `y[i]`, we have `x[i] == y[i]`.
+If both `x` and `y` are empty, this is trivially satisfied.
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`y`</b>: Numeric `Tensor`, same dtype as and broadcastable to `x`.
+*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
+    error message and first few entries of `x`, `y`.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_equal".
+
+##### Returns:
+
+  Op that raises `InvalidArgumentError` if `x == y` is False.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.assert_non_positive.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.assert_non_positive.md
new file mode 100644
index 0000000000..83eb36a95c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.assert_non_positive.md
@@ -0,0 +1,34 @@
+### `tf.assert_non_positive(x, data=None, summarize=None, name=None)` {#assert_non_positive}
+
+Assert the condition `x <= 0` holds element-wise.
+
+Example of adding a dependency to an operation:
+
+```python
+with tf.control_dependencies([tf.assert_non_positive(x)]):
+  output = tf.reduce_sum(x)
+```
+
+Example of adding dependency to the tensor being checked:
+
+```python
+x = tf.with_dependencies([tf.assert_non_positive(x)], x)
+```
+
+Non-positive means, for every element `x[i]` of `x`, we have `x[i] <= 0`.
+If `x` is empty this is trivially satisfied.
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
+    error message and first few entries of `x`.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).
+    Defaults to "assert_non_positive".
+
+##### Returns:
+
+  Op raising `InvalidArgumentError` unless `x` is all non-positive.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.assert_positive.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.assert_positive.md
new file mode 100644
index 0000000000..8b727d6215
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.assert_positive.md
@@ -0,0 +1,33 @@
+### `tf.assert_positive(x, data=None, summarize=None, name=None)` {#assert_positive}
+
+Assert the condition `x > 0` holds element-wise.
+
+Example of adding a dependency to an operation:
+
+```python
+with tf.control_dependencies([tf.assert_positive(x)]):
+  output = tf.reduce_sum(x)
+```
+
+Example of adding dependency to the tensor being checked:
+
+```python
+x = tf.with_dependencies([tf.assert_positive(x)], x)
+```
+
+Positive means, for every element `x[i]` of `x`, we have `x[i] > 0`.
+If `x` is empty this is trivially satisfied.
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
+    error message and first few entries of `x`.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_positive".
+
+##### Returns:
+
+  Op raising `InvalidArgumentError` unless `x` is all positive.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.batch_matmul.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.batch_matmul.md
new file mode 100644
index 0000000000..a4764435b8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.batch_matmul.md
@@ -0,0 +1,41 @@
+### `tf.batch_matmul(x, y, adj_x=None, adj_y=None, name=None)` {#batch_matmul}
+
+Multiplies slices of two tensors in batches.
+
+Multiplies all slices of `Tensor` `x` and `y` (each slice can be
+viewed as an element of a batch), and arranges the individual results
+in a single output tensor of the same batch size. Each of the
+individual slices can optionally be adjointed (to adjoint a matrix
+means to transpose and conjugate it) before multiplication by setting
+the `adj_x` or `adj_y` flag to `True`, which are by default `False`.
+
+The input tensors `x` and `y` are 3-D or higher with shape `[..., r_x, c_x]`
+and `[..., r_y, c_y]`.
+
+The output tensor is 3-D or higher with shape `[..., r_o, c_o]`, where:
+
+    r_o = c_x if adj_x else r_x
+    c_o = r_y if adj_y else c_y
+
+It is computed as:
+
+    output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `complex64`, `complex128`.
+    3-D or higher with shape `[..., r_x, c_x]`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+    3-D or higher with shape `[..., r_y, c_y]`.
+*  <b>`adj_x`</b>: An optional `bool`. Defaults to `False`.
+    If `True`, adjoint the slices of `x`. Defaults to `False`.
+*  <b>`adj_y`</b>: An optional `bool`. Defaults to `False`.
+    If `True`, adjoint the slices of `y`. Defaults to `False`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+  3-D or higher with shape `[..., r_o, c_o]`
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.clip_by_norm.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.clip_by_norm.md
new file mode 100644
index 0000000000..a393375986
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.clip_by_norm.md
@@ -0,0 +1,29 @@
+### `tf.clip_by_norm(t, clip_norm, name=None)` {#clip_by_norm}
+
+Clips tensor values to a maximum L2-norm.
+
+Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
+normalizes `t` so that its L2-norm is less than or equal to `clip_norm`.
+Specifically, if the L2-norm is already less than or equal to `clip_norm`,
+then `t` is not modified. If the L2-norm is greater than `clip_norm`, then
+this operation returns a tensor of the same type and shape as `t` with its
+values set to:
+
+`t * clip_norm / l2norm(t)`
+
+In this case, the L2-norm of the output tensor is `clip_norm`.
+
+This operation is typically used to clip gradients before applying them with
+an optimizer.
+
+##### Args:
+
+
+*  <b>`t`</b>: A `Tensor`.
+*  <b>`clip_norm`</b>: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A clipped `Tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.concat.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.concat.md
deleted file mode 100644
index c54a7503da..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.concat.md
+++ /dev/null
@@ -1,45 +0,0 @@
-### `tf.concat(concat_dim, values, name='concat')` {#concat}
-
-Concatenates tensors along one dimension.
-
-Concatenates the list of tensors `values` along dimension `concat_dim`.  If
-`values[i].shape = [D0, D1, ... Dconcat_dim(i), ...Dn]`, the concatenated
-result has shape
-
-    [D0, D1, ... Rconcat_dim, ...Dn]
-
-where
-
-    Rconcat_dim = sum(Dconcat_dim(i))
-
-That is, the data from the input tensors is joined along the `concat_dim`
-dimension.
-
-The number of dimensions of the input tensors must match, and all dimensions
-except `concat_dim` must be equal.
-
-For example:
-
-```python
-t1 = [[1, 2, 3], [4, 5, 6]]
-t2 = [[7, 8, 9], [10, 11, 12]]
-tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
-tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
-
-# tensor t3 with shape [2, 3]
-# tensor t4 with shape [2, 3]
-tf.shape(tf.concat(0, [t3, t4])) ==> [4, 3]
-tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]
-```
-
-##### Args:
-
-
-*  <b>`concat_dim`</b>: 0-D `int32` `Tensor`.  Dimension along which to concatenate.
-*  <b>`values`</b>: A list of `Tensor` objects or a single `Tensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` resulting from concatenation of the input tensors.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.copy_graph.copy_variable_to_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.copy_graph.copy_variable_to_graph.md
new file mode 100644
index 0000000000..85e336a29b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.copy_graph.copy_variable_to_graph.md
@@ -0,0 +1,20 @@
+### `tf.contrib.copy_graph.copy_variable_to_graph(org_instance, to_graph, scope='')` {#copy_variable_to_graph}
+
+Given a `Variable` instance from one `Graph`, initializes and returns
+a copy of it from another `Graph`, under the specified scope
+(default `""`).
+
+Args:
+org_instance: A `Variable` from some `Graph`.
+to_graph: The `Graph` to copy the `Variable` to.
+scope: A scope for the new `Variable` (default `""`).
+
+##### Returns:
+
+    The copied `Variable` from `to_graph`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `org_instance` is not a `Variable`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.Chi2.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.Chi2.md
deleted file mode 100644
index 61ca5fb9d3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.Chi2.md
+++ /dev/null
@@ -1,260 +0,0 @@
-The Chi2 distribution with degrees of freedom df.
-
-The PDF of this distribution is:
-
-```pdf(x) = (x^(df/2 - 1)e^(-x/2))/(2^(k/2)Gamma(k/2)), x > 0```
-
-Note that the Chi2 distribution is a special case of the Gamma distribution,
-with Chi2(df) = Gamma(df/2, 1/2).
-- - -
-
-#### `tf.contrib.distributions.Chi2.__init__(df, name='Chi2')` {#Chi2.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.alpha` {#Chi2.alpha}
-
-Shape parameter.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.batch_shape(name='batch_shape')` {#Chi2.batch_shape}
-
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-The product of the dimensions of the `batch_shape` is the number of
-independent distributions of this kind the instance represents.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `batch_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.beta` {#Chi2.beta}
-
-Inverse scale parameter.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.cdf(x, name='cdf')` {#Chi2.cdf}
-
-CDF of observations `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.df` {#Chi2.df}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.dtype` {#Chi2.dtype}
-
-dtype of samples from this distribution.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.entropy(name='entropy')` {#Chi2.entropy}
-
-The entropy of Gamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.event_shape(name='event_shape')` {#Chi2.event_shape}
-
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `event_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.get_batch_shape()` {#Chi2.get_batch_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `batch_shape`. May be only partially defined.
-
-##### Returns:
-
-  `TensorShape` object.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.get_event_shape()` {#Chi2.get_event_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `event_shape`. May be only partially defined.
-
-##### Returns:
-
-  `TensorShape` object.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.is_reparameterized` {#Chi2.is_reparameterized}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.log_cdf(x, name='log_cdf')` {#Chi2.log_cdf}
-
-Log CDF of observations `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.log_pdf(x, name='log_pdf')` {#Chi2.log_pdf}
-
-Log pdf of observations in `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.mean` {#Chi2.mean}
-
-Mean of each batch member.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.name` {#Chi2.name}
-
-Name to prepend to all ops.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.pdf(x, name='pdf')` {#Chi2.pdf}
-
-Pdf of observations in `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`pdf`</b>: tensor of dtype `dtype`, the PDFs of `x`
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.sample(n, seed=None, name=None)` {#Chi2.sample}
-
-Generate `n` samples.
-
-##### Args:
-
-
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
-
-##### Returns:
-
-
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Chi2.variance` {#Chi2.variance}
-
-Variance of each batch member.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.Gamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.Gamma.md
deleted file mode 100644
index 5a7bbea7ae..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.distributions.Gamma.md
+++ /dev/null
@@ -1,284 +0,0 @@
-The `Gamma` distribution with parameter alpha and beta.
-
-The parameters are the shape and inverse scale parameters alpha, beta.
-
-The PDF of this distribution is:
-
-```pdf(x) = (beta^alpha)(x^(alpha-1))e^(-x*beta)/Gamma(alpha), x > 0```
-
-and the CDF of this distribution is:
-
-```cdf(x) =  GammaInc(alpha, beta * x) / Gamma(alpha), x > 0```
-
-where GammaInc is the incomplete lower Gamma function.
-
-Examples:
-
-```python
-dist = Gamma(alpha=3.0, beta=2.0)
-dist2 = Gamma(alpha=[3.0, 4.0], beta=[2.0, 3.0])
-```
-- - -
-
-#### `tf.contrib.distributions.Gamma.__init__(alpha, beta, name='Gamma')` {#Gamma.__init__}
-
-Construct Gamma distributions with parameters `alpha` and `beta`.
-
-The parameters `alpha` and `beta` must be shaped in a way that supports
-broadcasting (e.g. `alpha + beta` is a valid operation).
-
-##### Args:
-
-
-*  <b>`alpha`</b>: `float` or `double` tensor, the shape params of the
-    distribution(s).
-    alpha must contain only positive values.
-*  <b>`beta`</b>: `float` or `double` tensor, the inverse scale params of the
-    distribution(s).
-    beta must contain only positive values.
-*  <b>`name`</b>: The name to prepend to all ops created by this distribution.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `alpha` and `beta` are different dtypes.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.alpha` {#Gamma.alpha}
-
-Shape parameter.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.batch_shape(name='batch_shape')` {#Gamma.batch_shape}
-
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-The product of the dimensions of the `batch_shape` is the number of
-independent distributions of this kind the instance represents.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `batch_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.beta` {#Gamma.beta}
-
-Inverse scale parameter.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.cdf(x, name='cdf')` {#Gamma.cdf}
-
-CDF of observations `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.dtype` {#Gamma.dtype}
-
-dtype of samples from this distribution.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.entropy(name='entropy')` {#Gamma.entropy}
-
-The entropy of Gamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.event_shape(name='event_shape')` {#Gamma.event_shape}
-
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `event_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.get_batch_shape()` {#Gamma.get_batch_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `batch_shape`. May be only partially defined.
-
-##### Returns:
-
-  `TensorShape` object.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.get_event_shape()` {#Gamma.get_event_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `event_shape`. May be only partially defined.
-
-##### Returns:
-
-  `TensorShape` object.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.is_reparameterized` {#Gamma.is_reparameterized}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.log_cdf(x, name='log_cdf')` {#Gamma.log_cdf}
-
-Log CDF of observations `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.log_pdf(x, name='log_pdf')` {#Gamma.log_pdf}
-
-Log pdf of observations in `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.mean` {#Gamma.mean}
-
-Mean of each batch member.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.name` {#Gamma.name}
-
-Name to prepend to all ops.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.pdf(x, name='pdf')` {#Gamma.pdf}
-
-Pdf of observations in `x` under these Gamma distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`pdf`</b>: tensor of dtype `dtype`, the PDFs of `x`
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.sample(n, seed=None, name=None)` {#Gamma.sample}
-
-Generate `n` samples.
-
-##### Args:
-
-
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
-
-##### Returns:
-
-
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.variance` {#Gamma.variance}
-
-Variance of each batch member.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.layers.convolution2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.layers.convolution2d.md
new file mode 100644
index 0000000000..f296166c9c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.layers.convolution2d.md
@@ -0,0 +1,43 @@
+### `tf.contrib.layers.convolution2d(*args, **kwargs)` {#convolution2d}
+
+Adds a 2D convolution followed by an optional batch_norm layer.
+
+`convolution2d` creates a variable called `weights`, representing the
+convolutional kernel, that is convolved with the `inputs` to produce a
+`Tensor` of activations. If a `normalizer_fn` is provided (such as
+`batch_norm`), it is then applied. Otherwise, if `normalizer_fn` is
+None and a `biases_initializer` is provided then a `biases` variable would be
+created and added the activations. Finally, if `activation_fn` is not `None`,
+it is applied to the activations as well.
+
+##### Args:
+
+
+*  <b>`inputs`</b>: a 4-D tensor  `[batch_size, height, width, channels]`.
+*  <b>`num_outputs`</b>: integer, the number of output filters.
+*  <b>`kernel_size`</b>: a list of length 2 `[kernel_height, kernel_width]` of
+    of the filters. Can be an int if both values are the same.
+*  <b>`stride`</b>: a list of length 2 `[stride_height, stride_width]`.
+    Can be an int if both strides are the same. Note that presently
+    both strides must have the same value.
+*  <b>`padding`</b>: one of `VALID` or `SAME`.
+*  <b>`activation_fn`</b>: activation function.
+*  <b>`normalizer_fn`</b>: normalization function to use instead of `biases`. If
+    `normalize_fn` is provided then `biases_initializer` and
+    `biases_regularizer` are ignored and `biases` are not created nor added.
+*  <b>`normalizer_params`</b>: normalization function parameters.
+*  <b>`weights_initializer`</b>: An initializer for the weights.
+*  <b>`weights_regularizer`</b>: Optional regularizer for the weights.
+*  <b>`biases_initializer`</b>: An initializer for the biases. If None skip biases.
+*  <b>`biases_regularizer`</b>: Optional regularizer for the biases.
+*  <b>`reuse`</b>: whether or not the layer and its variables should be reused. To be
+    able to reuse the layer scope must be given.
+*  <b>`variables_collections`</b>: optional list of collections for all the variables or
+    a dictionay containing a different list of collection per variable.
+*  <b>`outputs_collections`</b>: collection to add the outputs.
+*  <b>`scope`</b>: Optional scope for `variable_op_scope`.
+
+##### Returns:
+
+  a tensor representing the output of the operation.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.layers.l2_regularizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.layers.l2_regularizer.md
new file mode 100644
index 0000000000..9c3d06393b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.layers.l2_regularizer.md
@@ -0,0 +1,22 @@
+### `tf.contrib.layers.l2_regularizer(scale)` {#l2_regularizer}
+
+Returns a function that can be used to apply L2 regularization to weights.
+
+Small values of L2 can help prevent overfitting the training data.
+
+##### Args:
+
+
+*  <b>`scale`</b>: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
+
+##### Returns:
+
+  A function with signature `l2(weights, name=None)` that applies L2
+  regularization.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If scale is outside of the range [0.0, 1.0] or if scale is not a
+  float.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.layers.xavier_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.layers.xavier_initializer.md
deleted file mode 100644
index 55631e4b05..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.layers.xavier_initializer.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32)` {#xavier_initializer}
-
-Returns an initializer performing "Xavier" initialization for weights.
-
-This function implements the weight initialization from:
-
-Xavier Glorot and Yoshua Bengio (2010):
-         Understanding the difficulty of training deep feedforward neural
-         networks. International conference on artificial intelligence and
-         statistics.
-
-This initializer is designed to keep the scale of the gradients roughly the
-same in all layers. In uniform distribution this ends up being the range:
-`x = sqrt(6. / (in + out)); [-x, x]` and for normal distribution a standard
-deviation of `sqrt(3. / (in + out))` is used.
-
-##### Args:
-
-
-*  <b>`uniform`</b>: Whether to use uniform or normal distributed random initialization.
-*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`dtype`</b>: The data type. Only floating point types are supported.
-
-##### Returns:
-
-  An initializer for a weight matrix.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.RunConfig.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.RunConfig.md
new file mode 100644
index 0000000000..ffdf8703c0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.RunConfig.md
@@ -0,0 +1,47 @@
+This class specifies the specific configurations for the run.
+
+Parameters:
+  execution_mode: Runners use this flag to execute different tasks, like
+    training vs evaluation. 'all' (the default) executes both training and
+    eval.
+  master: TensorFlow master. Empty string (the default) for local.
+  task: Task id of the replica running the training (default: 0).
+  num_ps_replicas: Number of parameter server tasks to use (default: 0).
+  training_worker_session_startup_stagger_secs: Seconds to sleep between the
+    startup of each worker task session (default: 5).
+  training_worker_max_startup_secs: Max seconds to wait before starting any
+    worker (default: 60).
+  eval_delay_secs: Number of seconds between the beginning of each eval run.
+    If one run takes more than this amount of time, the next run will start
+    immediately once that run completes (default 60).
+  eval_steps: Number of steps to run in each eval (default: 100).
+  num_cores: Number of cores to be used (default: 4).
+  verbose: Controls the verbosity, possible values:
+    0: the algorithm and debug information is muted.
+    1: trainer prints the progress.
+    2: log device placement is printed.
+  gpu_memory_fraction: Fraction of GPU memory used by the process on
+    each GPU uniformly on the same machine.
+  tf_random_seed: Random seed for TensorFlow initializers.
+    Setting this value allows consistency between reruns.
+  keep_checkpoint_max: The maximum number of recent checkpoint files to keep.
+    As new files are created, older files are deleted.
+    If None or 0, all checkpoint files are kept.
+    Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
+  keep_checkpoint_every_n_hours: Number of hours between each checkpoint
+    to be saved. The default value of 10,000 hours effectively disables
+    the feature.
+
+Attributes:
+  tf_master: Tensorflow master.
+  tf_config: Tensorflow Session Config proto.
+  tf_random_seed: Tensorflow random seed.
+  keep_checkpoint_max: Maximum number of checkpoints to keep.
+  keep_checkpoint_every_n_hours: Number of hours between each checkpoint.
+- - -
+
+#### `tf.contrib.learn.RunConfig.__init__(execution_mode='all', master='', task=0, num_ps_replicas=0, training_worker_session_startup_stagger_secs=5, training_worker_max_startup_secs=60, eval_delay_secs=60, eval_steps=100, num_cores=4, verbose=1, gpu_memory_fraction=1, tf_random_seed=42, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000)` {#RunConfig.__init__}
+
+
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.evaluate.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.evaluate.md
deleted file mode 100644
index 022662c3f6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.evaluate.md
+++ /dev/null
@@ -1,44 +0,0 @@
-### `tf.contrib.learn.evaluate(graph, output_dir, checkpoint_path, eval_dict, update_op=None, global_step_tensor=None, supervisor_master='', log_every_steps=10, feed_fn=None, max_steps=None)` {#evaluate}
-
-Evaluate a model loaded from a checkpoint.
-
-Given `graph`, a directory to write summaries to (`output_dir`), a checkpoint
-to restore variables from, and a `dict` of `Tensor`s to evaluate, run an eval
-loop for `max_steps` steps.
-
-In each step of evaluation, all tensors in the `eval_dict` are evaluated, and
-every `log_every_steps` steps, they are logged. At the very end of evaluation,
-a summary is evaluated (finding the summary ops using `Supervisor`'s logic)
-and written to `output_dir`.
-
-##### Args:
-
-
-*  <b>`graph`</b>: A `Graph` to train. It is expected that this graph is not in use
-    elsewhere.
-*  <b>`output_dir`</b>: A string containing the directory to write a summary to.
-*  <b>`checkpoint_path`</b>: A string containing the path to a checkpoint to restore.
-    Can be `None` if the graph doesn't require loading any variables.
-*  <b>`eval_dict`</b>: A `dict` mapping string names to tensors to evaluate. It is
-    evaluated in every logging step. The result of the final evaluation is
-    returned. If update_op is None, then it's evaluated in every step.
-*  <b>`update_op`</b>: A `Tensor` which is run in every step.
-*  <b>`global_step_tensor`</b>: A `Variable` containing the global step. If `None`,
-    one is extracted from the graph using the same logic as in `Supervisor`.
-    Used to place eval summaries on training curves.
-*  <b>`supervisor_master`</b>: The master string to use when preparing the session.
-*  <b>`log_every_steps`</b>: Integer. Output logs every `log_every_steps` evaluation
-    steps. The logs contain the `eval_dict` and timing information.
-*  <b>`feed_fn`</b>: A function that is called every iteration to produce a `feed_dict`
-    passed to `session.run` calls. Optional.
-*  <b>`max_steps`</b>: Integer. Evaluate `eval_dict` this many times.
-
-##### Returns:
-
-  A tuple `(eval_results, global_step)`:
-
-*  <b>`eval_results`</b>: A `dict` mapping `string` to numeric values (`int`, `float`)
-    that are the result of running eval_dict in the last step. `None` if no
-    eval steps were run.
-*  <b>`global_step`</b>: The global step this evaluation corresponds to.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.infer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.infer.md
deleted file mode 100644
index 616e74f3a4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.infer.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.contrib.learn.infer(restore_checkpoint_path, output_dict, feed_dict=None)` {#infer}
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.run_feeds.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.run_feeds.md
new file mode 100644
index 0000000000..f5c3e977d0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.run_feeds.md
@@ -0,0 +1,28 @@
+### `tf.contrib.learn.run_feeds(output_dict, feed_dicts, restore_checkpoint_path=None)` {#run_feeds}
+
+Run `output_dict` tensors with each input in `feed_dicts`.
+
+If `checkpoint_path` is supplied, restore from checkpoint. Otherwise, init all
+variables.
+
+##### Args:
+
+
+*  <b>`output_dict`</b>: A `dict` mapping string names to `Tensor` objects to run.
+    Tensors must all be from the same graph.
+*  <b>`feed_dicts`</b>: Iterable of `dict` objects of input values to feed.
+*  <b>`restore_checkpoint_path`</b>: A string containing the path to a checkpoint to
+    restore.
+
+##### Returns:
+
+  A list of dicts of values read from `output_dict` tensors, one item in the
+  list for each item in `feed_dicts`. Keys are the same as `output_dict`,
+  values are the results read from the corresponding `Tensor` in
+  `output_dict`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `output_dict` or `feed_dicts` is None or empty.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.metrics.streaming_mean.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.metrics.streaming_mean.md
deleted file mode 100644
index 780ecbaa7b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.metrics.streaming_mean.md
+++ /dev/null
@@ -1,44 +0,0 @@
-### `tf.contrib.metrics.streaming_mean(values, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean}
-
-Computes the (weighted) mean of the given values.
-
-The `streaming_mean` function creates two local variables, `total` and `count`
-that are used to compute the average of `values`. This average is ultimately
-returned as `mean` which is an idempotent operation that simply divides
-`total` by `count`. To facilitate the estimation of a mean over a stream
-of data, the function creates an `update_op` operation whose behavior is
-dependent on the value of `weights`. If `weights` is None, then `update_op`
-increments `total` with the reduced sum of `values` and increments `count`
-with the number of elements in `values`. If `weights` is not `None`, then
-`update_op` increments `total` with the reduced sum of the product of `values`
-and `weights` and increments `count` with the reduced sum of weights.
-In addition to performing the updates, `update_op` also returns the
-`mean`.
-
-##### Args:
-
-
-*  <b>`values`</b>: A `Tensor` of arbitrary dimensions.
-*  <b>`weights`</b>: An optional set of weights of the same shape as `values`. If
-    `weights` is not None, the function computes a weighted mean.
-*  <b>`metrics_collections`</b>: An optional list of collections that `mean`
-    should be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that `update_op`
-    should be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`mean`</b>: A tensor representing the current mean, the value of `total` divided
-    by `count`.
-*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
-    appropriately and whose value matches `mean_value`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `weights` is not `None` and its shape doesn't match `values`
-    or if either `metrics_collections` or `updates_collections` are not a list
-    or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.metrics.streaming_mean_cosine_distance.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.metrics.streaming_mean_cosine_distance.md
deleted file mode 100644
index 1900cd1a97..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.metrics.streaming_mean_cosine_distance.md
+++ /dev/null
@@ -1,48 +0,0 @@
-### `tf.contrib.metrics.streaming_mean_cosine_distance(predictions, labels, dim, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean_cosine_distance}
-
-Computes the cosine distance between the labels and predictions.
-
-The `streaming_mean_cosine_distance` function creates two local variables,
-`total` and `count` that are used to compute the average cosine distance
-between `predictions` and `labels`. This average is ultimately returned as
-`mean_distance` which is an idempotent operation that simply divides `total`
-by `count. To facilitate the estimation of a mean over multiple batches
-of data, the function creates an `update_op` operation whose behavior is
-dependent on the value of `weights`. If `weights` is None, then `update_op`
-increments `total` with the reduced sum of `values and increments `count` with
-the number of elements in `values`. If `weights` is not `None`, then
-`update_op` increments `total` with the reduced sum of the product of `values`
-and `weights` and increments `count` with the reduced sum of weights.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: A tensor of the same size as labels.
-*  <b>`labels`</b>: A tensor of arbitrary size.
-*  <b>`dim`</b>: The dimension along which the cosine distance is computed.
-*  <b>`weights`</b>: An optional set of weights which indicates which predictions to
-    ignore during metric computation. Its size matches that of labels except
-    for the value of 'dim' which should be 1. For example if labels has
-    dimensions [32, 100, 200, 3], then `weights` should have dimensions
-    [32, 100, 200, 1].
-*  <b>`metrics_collections`</b>: An optional list of collections that the metric
-    value variable should be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that the metric update
-    ops should be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`mean_distance`</b>: A tensor representing the current mean, the value of `total`
-    divided by `count`.
-*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
-    appropriately.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If labels and predictions are of different sizes or if the
-    ignore_mask is of the wrong size or if either `metrics_collections` or
-    `updates_collections` are not a list or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.decode_csv.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.decode_csv.md
new file mode 100644
index 0000000000..f2ebf6945b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.decode_csv.md
@@ -0,0 +1,26 @@
+### `tf.decode_csv(records, record_defaults, field_delim=None, name=None)` {#decode_csv}
+
+Convert CSV records to tensors. Each column maps to one tensor.
+
+RFC 4180 format is expected for the CSV records.
+(https://tools.ietf.org/html/rfc4180)
+Note that we allow leading and trailing spaces with int or float field.
+
+##### Args:
+
+
+*  <b>`records`</b>: A `Tensor` of type `string`.
+    Each string is a record/row in the csv and all records should have
+    the same format.
+*  <b>`record_defaults`</b>: A list of `Tensor` objects with types from: `float32`, `int32`, `int64`, `string`.
+    One tensor per column of the input record, with either a
+    scalar default value for that column or empty if the column is required.
+*  <b>`field_delim`</b>: An optional `string`. Defaults to `","`.
+    delimiter to separate fields in a record.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A list of `Tensor` objects. Has the same type as `record_defaults`.
+  Each tensor will have the same shape as records.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.digamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.digamma.md
new file mode 100644
index 0000000000..5af2d11062
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.digamma.md
@@ -0,0 +1,16 @@
+### `tf.digamma(x, name=None)` {#digamma}
+
+Computes Psi, the derivative of Lgamma (the log of the absolute value of
+
+`Gamma(x)`), element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.dynamic_partition.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.dynamic_partition.md
new file mode 100644
index 0000000000..3fbb885055
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.dynamic_partition.md
@@ -0,0 +1,50 @@
+### `tf.dynamic_partition(data, partitions, num_partitions, name=None)` {#dynamic_partition}
+
+Partitions `data` into `num_partitions` tensors using indices from `partitions`.
+
+For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
+becomes part of `outputs[partitions[js]]`.  The slices with `partitions[js] = i`
+are placed in `outputs[i]` in lexicographic order of `js`, and the first
+dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`.
+In detail,
+
+    outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]
+
+    outputs[i] = pack([data[js, ...] for js if partitions[js] == i])
+
+`data.shape` must start with `partitions.shape`.
+
+For example:
+
+    # Scalar partitions
+    partitions = 1
+    num_partitions = 2
+    data = [10, 20]
+    outputs[0] = []  # Empty with shape [0, 2]
+    outputs[1] = [[10, 20]]
+
+    # Vector partitions
+    partitions = [0, 0, 1, 1, 0]
+    num_partitions = 2
+    data = [10, 20, 30, 40, 50]
+    outputs[0] = [10, 20, 50]
+    outputs[1] = [30, 40]
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/DynamicPartition.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`data`</b>: A `Tensor`.
+*  <b>`partitions`</b>: A `Tensor` of type `int32`.
+    Any shape.  Indices in the range `[0, num_partitions)`.
+*  <b>`num_partitions`</b>: An `int` that is `>= 1`.
+    The number of partitions to output.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A list of `num_partitions` `Tensor` objects of the same type as data.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.equal.md
new file mode 100644
index 0000000000..998db9189f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.equal.md
@@ -0,0 +1,15 @@
+### `tf.equal(x, y, name=None)` {#equal}
+
+Returns the truth value of (x == y) element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `quint8`, `qint8`, `qint32`, `string`, `bool`, `complex128`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.AbortedError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.AbortedError.md
new file mode 100644
index 0000000000..f2bc775dcb
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.AbortedError.md
@@ -0,0 +1,15 @@
+The operation was aborted, typically due to a concurrent action.
+
+For example, running a
+[`queue.enqueue()`](../../api_docs/python/io_ops.md#QueueBase.enqueue)
+operation may raise `AbortedError` if a
+[`queue.close()`](../../api_docs/python/io_ops.md#QueueBase.close) operation
+previously ran.
+
+- - -
+
+#### `tf.errors.AbortedError.__init__(node_def, op, message)` {#AbortedError.__init__}
+
+Creates an `AbortedError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.PermissionDeniedError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.PermissionDeniedError.md
new file mode 100644
index 0000000000..a8a81494c8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.PermissionDeniedError.md
@@ -0,0 +1,14 @@
+Raised when the caller does not have permission to run an operation.
+
+For example, running the
+[`tf.WholeFileReader.read()`](../../api_docs/python/io_ops.md#WholeFileReader)
+operation could raise `PermissionDeniedError` if it receives the name of a
+file for which the user does not have the read file permission.
+
+- - -
+
+#### `tf.errors.PermissionDeniedError.__init__(node_def, op, message)` {#PermissionDeniedError.__init__}
+
+Creates a `PermissionDeniedError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.ResourceExhaustedError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.ResourceExhaustedError.md
new file mode 100644
index 0000000000..a01e255be5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.ResourceExhaustedError.md
@@ -0,0 +1,12 @@
+Some resource has been exhausted.
+
+For example, this error might be raised if a per-user quota is
+exhausted, or perhaps the entire file system is out of space.
+
+- - -
+
+#### `tf.errors.ResourceExhaustedError.__init__(node_def, op, message)` {#ResourceExhaustedError.__init__}
+
+Creates a `ResourceExhaustedError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.UnauthenticatedError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.UnauthenticatedError.md
deleted file mode 100644
index d3344dc6b1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.errors.UnauthenticatedError.md
+++ /dev/null
@@ -1,11 +0,0 @@
-The request does not have valid authentication credentials.
-
-This exception is not currently used.
-
-- - -
-
-#### `tf.errors.UnauthenticatedError.__init__(node_def, op, message)` {#UnauthenticatedError.__init__}
-
-Creates an `UnauthenticatedError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.expand_dims.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.expand_dims.md
new file mode 100644
index 0000000000..a188cda506
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.expand_dims.md
@@ -0,0 +1,50 @@
+### `tf.expand_dims(input, dim, name=None)` {#expand_dims}
+
+Inserts a dimension of 1 into a tensor's shape.
+
+Given a tensor `input`, this operation inserts a dimension of 1 at the
+dimension index `dim` of `input`'s shape. The dimension index `dim` starts at
+zero; if you specify a negative number for `dim` it is counted backward from
+the end.
+
+This operation is useful if you want to add a batch dimension to a single
+element. For example, if you have a single image of shape `[height, width,
+channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
+which will make the shape `[1, height, width, channels]`.
+
+Other examples:
+
+```prettyprint
+# 't' is a tensor of shape [2]
+shape(expand_dims(t, 0)) ==> [1, 2]
+shape(expand_dims(t, 1)) ==> [2, 1]
+shape(expand_dims(t, -1)) ==> [2, 1]
+
+# 't2' is a tensor of shape [2, 3, 5]
+shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
+shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
+shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
+```
+
+This operation requires that:
+
+`-1-input.dims() <= dim <= input.dims()`
+
+This operation is related to `squeeze()`, which removes dimensions of
+size 1.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`.
+*  <b>`dim`</b>: A `Tensor` of type `int32`.
+    0-D (scalar). Specifies the dimension index at which to
+    expand the shape of `input`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+  Contains the same data as `input`, but its shape has an additional
+  dimension of size 1 added.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.fill.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.fill.md
new file mode 100644
index 0000000000..b6e51fa634
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.fill.md
@@ -0,0 +1,26 @@
+### `tf.fill(dims, value, name=None)` {#fill}
+
+Creates a tensor filled with a scalar value.
+
+This operation creates a tensor of shape `dims` and fills it with `value`.
+
+For example:
+
+```prettyprint
+# Output tensor has shape [2, 3].
+fill([2, 3], 9) ==> [[9, 9, 9]
+                     [9, 9, 9]]
+```
+
+##### Args:
+
+
+*  <b>`dims`</b>: A `Tensor` of type `int32`.
+    1-D. Represents the shape of the output tensor.
+*  <b>`value`</b>: A `Tensor`. 0-D (scalar). Value to fill the returned tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `value`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.get_default_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.get_default_graph.md
deleted file mode 100644
index bd734d1b98..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.get_default_graph.md
+++ /dev/null
@@ -1,17 +0,0 @@
-### `tf.get_default_graph()` {#get_default_graph}
-
-Returns the default graph for the current thread.
-
-The returned graph will be the innermost graph on which a
-`Graph.as_default()` context has been entered, or a global default
-graph if none has been explicitly created.
-
-NOTE: The default graph is a property of the current thread. If you
-create a new thread, and wish to use the default graph in that
-thread, you must explicitly add a `with g.as_default():` in that
-thread's function.
-
-##### Returns:
-
-  The default `Graph` being used in the current thread.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.get_seed.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.get_seed.md
deleted file mode 100644
index ccf6712418..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.get_seed.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.get_seed(op_seed)` {#get_seed}
-
-Returns the local seeds an operation should use given an op-specific seed.
-
-Given operation-specific seed, `op_seed`, this helper function returns two
-seeds derived from graph-level and op-level seeds. Many random operations
-internally use the two seeds to allow user to change the seed globally for a
-graph, or for only specific operations.
-
-For details on how the graph-level seed interacts with op seeds, see
-[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed).
-
-##### Args:
-
-
-*  <b>`op_seed`</b>: integer.
-
-##### Returns:
-
-  A tuple of two integers that should be used for the local seed of this
-  operation.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.get_session_tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.get_session_tensor.md
new file mode 100644
index 0000000000..215647a989
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.get_session_tensor.md
@@ -0,0 +1,22 @@
+### `tf.get_session_tensor(dtype, name=None)` {#get_session_tensor}
+
+Get the tensor of type `dtype` by feeding a tensor handle.
+
+This is EXPERIMENTAL and subject to change.
+
+Get the value of the tensor from a tensor handle. The tensor
+is produced in a previous run() and stored in the state of the
+session.
+
+##### Args:
+
+
+*  <b>`dtype`</b>: The type of the output tensor.
+*  <b>`name`</b>: Optional name prefix for the return tensor.
+
+##### Returns:
+
+  A pair of tensors. The first is a placeholder for feeding a
+  tensor handle and the second is the tensor in the session state
+  keyed by the tensor handle.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.global_norm.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.global_norm.md
new file mode 100644
index 0000000000..d37d4228b2
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.global_norm.md
@@ -0,0 +1,27 @@
+### `tf.global_norm(t_list, name=None)` {#global_norm}
+
+Computes the global norm of multiple tensors.
+
+Given a tuple or list of tensors `t_list`, this operation returns the
+global norm of the elements in all tensors in `t_list`. The global norm is
+computed as:
+
+`global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))`
+
+Any entries in `t_list` that are of type None are ignored.
+
+##### Args:
+
+
+*  <b>`t_list`</b>: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A 0-D (scalar) `Tensor` of type `float`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `t_list` is not a sequence.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.ifft2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.ifft2d.md
new file mode 100644
index 0000000000..0ca8eb8dc1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.ifft2d.md
@@ -0,0 +1,15 @@
+### `tf.ifft2d(input, name=None)` {#ifft2d}
+
+Compute the inverse 2-dimensional discrete Fourier Transform.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 matrix.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`.
+  The inverse 2D Fourier Transform of `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.adjust_brightness.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.adjust_brightness.md
new file mode 100644
index 0000000000..7743f0180c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.adjust_brightness.md
@@ -0,0 +1,25 @@
+### `tf.image.adjust_brightness(image, delta)` {#adjust_brightness}
+
+Adjust the brightness of RGB or Grayscale images.
+
+This is a convenience method that converts an RGB image to float
+representation, adjusts its brightness, and then converts it back to the
+original data type. If several adjustments are chained it is advisable to
+minimize the number of redundant conversions.
+
+The value `delta` is added to all components of the tensor `image`. Both
+`image` and `delta` are converted to `float` before adding (and `image` is
+scaled appropriately if it is in fixed-point representation). For regular
+images, `delta` should be in the range `[0,1)`, as it is added to the image in
+floating point representation, where pixel values are in the `[0,1)` range.
+
+##### Args:
+
+
+*  <b>`image`</b>: A tensor.
+*  <b>`delta`</b>: A scalar. Amount to add to the pixel values.
+
+##### Returns:
+
+  A brightness-adjusted tensor of the same shape and type as `image`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.encode_jpeg.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.encode_jpeg.md
new file mode 100644
index 0000000000..24b1886c10
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.encode_jpeg.md
@@ -0,0 +1,51 @@
+### `tf.image.encode_jpeg(image, format=None, quality=None, progressive=None, optimize_size=None, chroma_downsampling=None, density_unit=None, x_density=None, y_density=None, xmp_metadata=None, name=None)` {#encode_jpeg}
+
+JPEG-encode an image.
+
+`image` is a 3-D uint8 Tensor of shape `[height, width, channels]`.
+
+The attr `format` can be used to override the color format of the encoded
+output.  Values can be:
+
+*   `''`: Use a default format based on the number of channels in the image.
+*   `grayscale`: Output a grayscale JPEG image.  The `channels` dimension
+    of `image` must be 1.
+*   `rgb`: Output an RGB JPEG image. The `channels` dimension
+    of `image` must be 3.
+
+If `format` is not specified or is the empty string, a default format is picked
+in function of the number of channels in `image`:
+
+*   1: Output a grayscale image.
+*   3: Output an RGB image.
+
+##### Args:
+
+
+*  <b>`image`</b>: A `Tensor` of type `uint8`.
+    3-D with shape `[height, width, channels]`.
+*  <b>`format`</b>: An optional `string` from: `"", "grayscale", "rgb"`. Defaults to `""`.
+    Per pixel image format.
+*  <b>`quality`</b>: An optional `int`. Defaults to `95`.
+    Quality of the compression from 0 to 100 (higher is better and slower).
+*  <b>`progressive`</b>: An optional `bool`. Defaults to `False`.
+    If True, create a JPEG that loads progressively (coarse to fine).
+*  <b>`optimize_size`</b>: An optional `bool`. Defaults to `False`.
+    If True, spend CPU/RAM to reduce size with no quality change.
+*  <b>`chroma_downsampling`</b>: An optional `bool`. Defaults to `True`.
+    See http://en.wikipedia.org/wiki/Chroma_subsampling.
+*  <b>`density_unit`</b>: An optional `string` from: `"in", "cm"`. Defaults to `"in"`.
+    Unit used to specify `x_density` and `y_density`:
+    pixels per inch (`'in'`) or centimeter (`'cm'`).
+*  <b>`x_density`</b>: An optional `int`. Defaults to `300`.
+    Horizontal pixels per density unit.
+*  <b>`y_density`</b>: An optional `int`. Defaults to `300`.
+    Vertical pixels per density unit.
+*  <b>`xmp_metadata`</b>: An optional `string`. Defaults to `""`.
+    If not empty, embed this XMP metadata in the image header.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `string`. 0-D. JPEG-encoded image.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.encode_png.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.encode_png.md
new file mode 100644
index 0000000000..fa073a771f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.encode_png.md
@@ -0,0 +1,28 @@
+### `tf.image.encode_png(image, compression=None, name=None)` {#encode_png}
+
+PNG-encode an image.
+
+`image` is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]`
+where `channels` is:
+
+*   1: for grayscale.
+*   2: for grayscale + alpha.
+*   3: for RGB.
+*   4: for RGBA.
+
+The ZLIB compression level, `compression`, can be -1 for the PNG-encoder
+default or a value from 0 to 9.  9 is the highest compression level, generating
+the smallest output, but is slower.
+
+##### Args:
+
+
+*  <b>`image`</b>: A `Tensor`. Must be one of the following types: `uint8`, `uint16`.
+    3-D with shape `[height, width, channels]`.
+*  <b>`compression`</b>: An optional `int`. Defaults to `-1`. Compression level.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `string`. 0-D. PNG-encoded image.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.pad_to_bounding_box.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.pad_to_bounding_box.md
deleted file mode 100644
index 04c155c03c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.pad_to_bounding_box.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.image.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width)` {#pad_to_bounding_box}
-
-Pad `image` with zeros to the specified `height` and `width`.
-
-Adds `offset_height` rows of zeros on top, `offset_width` columns of
-zeros on the left, and then pads the image on the bottom and right
-with zeros until it has dimensions `target_height`, `target_width`.
-
-This op does nothing if `offset_*` is zero and the image already has size
-`target_height` by `target_width`.
-
-##### Args:
-
-
-*  <b>`image`</b>: 3-D tensor with shape `[height, width, channels]`
-*  <b>`offset_height`</b>: Number of rows of zeros to add on top.
-*  <b>`offset_width`</b>: Number of columns of zeros to add on the left.
-*  <b>`target_height`</b>: Height of output image.
-*  <b>`target_width`</b>: Width of output image.
-
-##### Returns:
-
-  3-D tensor of shape `[target_height, target_width, channels]`
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the shape of `image` is incompatible with the `offset_*` or
-    `target_*` arguments
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.random_flip_up_down.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.random_flip_up_down.md
deleted file mode 100644
index 7ed36f5df2..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.random_flip_up_down.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.image.random_flip_up_down(image, seed=None)` {#random_flip_up_down}
-
-Randomly flips an image vertically (upside down).
-
-With a 1 in 2 chance, outputs the contents of `image` flipped along the first
-dimension, which is `height`.  Otherwise output the image as-is.
-
-##### Args:
-
-
-*  <b>`image`</b>: A 3-D tensor of shape `[height, width, channels].`
-*  <b>`seed`</b>: A Python integer. Used to create a random seed. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-
-##### Returns:
-
-  A 3-D tensor of the same type and shape as `image`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if the shape of `image` not supported.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.resize_bilinear.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.resize_bilinear.md
new file mode 100644
index 0000000000..a9580ca199
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.resize_bilinear.md
@@ -0,0 +1,24 @@
+### `tf.image.resize_bilinear(images, size, align_corners=None, name=None)` {#resize_bilinear}
+
+Resize `images` to `size` using bilinear interpolation.
+
+Input images can be of different types but output images are always float.
+
+##### Args:
+
+
+*  <b>`images`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
+    4-D with shape `[batch, height, width, channels]`.
+*  <b>`size`</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
+    new size for the images.
+*  <b>`align_corners`</b>: An optional `bool`. Defaults to `False`.
+    If true, rescale input by (new_height - 1) / (height - 1), which
+    exactly aligns the 4 corners of images and resized images. If false, rescale
+    by new_height / height. Treat similarly the width dimension.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `float32`. 4-D with shape
+  `[batch, new_height, new_width, channels]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.rgb_to_grayscale.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.rgb_to_grayscale.md
deleted file mode 100644
index bf9b6846e0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.image.rgb_to_grayscale.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.image.rgb_to_grayscale(images, name=None)` {#rgb_to_grayscale}
-
-Converts one or more images from RGB to Grayscale.
-
-Outputs a tensor of the same `DType` and rank as `images`.  The size of the
-last dimension of the output is 1, containing the Grayscale value of the
-pixels.
-
-##### Args:
-
-
-*  <b>`images`</b>: The RGB tensor to convert. Last dimension must have size 3 and
-    should contain RGB values.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The converted grayscale image(s).
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.initialize_local_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.initialize_local_variables.md
deleted file mode 100644
index 2a56dbb9d6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.initialize_local_variables.md
+++ /dev/null
@@ -1,10 +0,0 @@
-### `tf.initialize_local_variables()` {#initialize_local_variables}
-
-Returns an Op that initializes all local variables.
-
-This is just a shortcut for `initialize_variables(local_variables())`
-
-##### Returns:
-
-  An Op that initializes all local variables in the graph.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.inv.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.inv.md
deleted file mode 100644
index dfff52be12..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.inv.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.inv(x, name=None)` {#inv}
-
-Computes the reciprocal of x element-wise.
-
-I.e., \\(y = 1 / x\\).
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.is_finite.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.is_finite.md
new file mode 100644
index 0000000000..db038e9919
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.is_finite.md
@@ -0,0 +1,14 @@
+### `tf.is_finite(x, name=None)` {#is_finite}
+
+Returns which elements of x are finite.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.is_variable_initialized.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.is_variable_initialized.md
new file mode 100644
index 0000000000..d8383439ab
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.is_variable_initialized.md
@@ -0,0 +1,14 @@
+### `tf.is_variable_initialized(variable)` {#is_variable_initialized}
+
+Tests if a variable has been initialized.
+
+##### Args:
+
+
+*  <b>`variable`</b>: A `Variable`.
+
+##### Returns:
+
+  Returns a scalar boolean Tensor, `True` if the variable has been
+  initialized, `False` otherwise.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.lbeta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.lbeta.md
new file mode 100644
index 0000000000..e3ee18dfb3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.lbeta.md
@@ -0,0 +1,31 @@
+### `tf.lbeta(x, name='lbeta')` {#lbeta}
+
+Computes `ln(|Beta(x)|)`, reducing along the last dimension.
+
+Given one-dimensional `z = [z_0,...,z_{K-1}]`, we define
+
+```Beta(z) = \prod_j Gamma(z_j) / Gamma(\sum_j z_j)```
+
+And for `n + 1` dimensional `x` with shape `[N1, ..., Nn, K]`, we define
+`lbeta(x)[i1, ..., in] = Log(|Beta(x[i1, ..., in, :])|)`.  In other words,
+the last dimension is treated as the `z` vector.
+
+Note that if `z = [u, v]`, then
+`Beta(z) = int_0^1 t^{u-1} (1 - t)^{v-1} dt`, which defines the traditional
+bivariate beta function.
+
+##### Args:
+
+
+*  <b>`x`</b>: A rank `n + 1` `Tensor` with type `float`, or `double`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The logarithm of `|Beta(x)|` reducing along the last dimension.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `x` is empty with rank one or less.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.less.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.less.md
new file mode 100644
index 0000000000..8791d0366a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.less.md
@@ -0,0 +1,15 @@
+### `tf.less(x, y, name=None)` {#less}
+
+Returns the truth value of (x < y) element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.load_file_system_library.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.load_file_system_library.md
new file mode 100644
index 0000000000..60d768a624
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.load_file_system_library.md
@@ -0,0 +1,23 @@
+### `tf.load_file_system_library(library_filename)` {#load_file_system_library}
+
+Loads a TensorFlow plugin, containing file system implementation.
+
+Pass `library_filename` to a platform-specific mechanism for dynamically
+loading a library. The rules for determining the exact location of the
+library are platform-specific and are not documented here.
+
+##### Args:
+
+
+*  <b>`library_filename`</b>: Path to the plugin.
+    Relative or absolute filesystem path to a dynamic library file.
+
+##### Returns:
+
+  None.
+
+##### Raises:
+
+
+*  <b>`RuntimeError`</b>: when unable to load the library.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.logical_not.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.logical_not.md
deleted file mode 100644
index 40a0bb2e43..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.logical_not.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.logical_not(x, name=None)` {#logical_not}
-
-Returns the truth value of NOT x element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` of type `bool`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.logical_or.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.logical_or.md
new file mode 100644
index 0000000000..be18e65e92
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.logical_or.md
@@ -0,0 +1,15 @@
+### `tf.logical_or(x, y, name=None)` {#logical_or}
+
+Returns the truth value of x OR y element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` of type `bool`.
+*  <b>`y`</b>: A `Tensor` of type `bool`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.logical_xor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.logical_xor.md
deleted file mode 100644
index 20db3e60a6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.logical_xor.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.logical_xor(x, y, name='LogicalXor')` {#logical_xor}
-
-x ^ y = (x | y) & ~(x & y).
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.matrix_inverse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.matrix_inverse.md
deleted file mode 100644
index 4172badef5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.matrix_inverse.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.matrix_inverse(input, adjoint=None, name=None)` {#matrix_inverse}
-
-Calculates the inverse of a square invertible matrix or its adjoint (conjugate
-
-transpose).
-
-The op uses LU decomposition with partial pivoting to compute the inverse.
-
-If the matrix is not invertible there is no guarantee what the op does. It
-may detect the condition and raise an exception or it may simply return a
-garbage result.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
-    Shape is `[M, M]`.
-*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-  Shape is `[M, M]`. If `adjoint` is `False` then `output` contains the
-  matrix inverse of `input`. If `adjoint` is `True` then `output` contains the
-  matrix inverse of the adjoint of `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.matrix_triangular_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.matrix_triangular_solve.md
deleted file mode 100644
index 5787145231..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.matrix_triangular_solve.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.matrix_triangular_solve(matrix, rhs, lower=None, adjoint=None, name=None)` {#matrix_triangular_solve}
-
-Solves a system of linear equations with an upper or lower triangular matrix by
-
-backsubstitution.
-
-`matrix` is a matrix of shape `[M, M]`. If `lower` is `True` then the strictly
-upper triangular part of `matrix` is assumed to be zero and not accessed.
-If `lower` is False then the strictly lower triangular part of `matrix` is
-assumed to be zero and not accessed.
-`rhs` is a matrix of shape [M, K]`.
-
-The output is a matrix of shape `[M, K]`. If `adjoint` is `False` the output
-satisfies the matrix equation `matrix` * `output` = `rhs`.
-If `adjoint` is `False` then `output` satisfies the matrix equation
-`matrix` * `output` = `rhs`.
-If `adjoint` is `True` then `output` satisfies the matrix equation
-`adjoint(matrix)` * `output` = `rhs`.
-
-##### Args:
-
-
-*  <b>`matrix`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
-    Shape is `[M, M]`.
-*  <b>`rhs`</b>: A `Tensor`. Must have the same type as `matrix`. Shape is `[M, K]`.
-*  <b>`lower`</b>: An optional `bool`. Defaults to `True`.
-    Boolean indicating whether `matrix` is lower or upper triangular
-*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
-    Boolean indicating whether to solve with `matrix` or its adjoint.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `matrix`. Shape is `[M, K]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.mod.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.mod.md
new file mode 100644
index 0000000000..5bfe1058a7
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.mod.md
@@ -0,0 +1,15 @@
+### `tf.mod(x, y, name=None)` {#mod}
+
+Returns element-wise remainder of division.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.neg.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.neg.md
new file mode 100644
index 0000000000..519fd9a875
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.neg.md
@@ -0,0 +1,16 @@
+### `tf.neg(x, name=None)` {#neg}
+
+Computes numerical negative value element-wise.
+
+I.e., \\(y = -x\\).
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.compute_accidental_hits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.compute_accidental_hits.md
new file mode 100644
index 0000000000..9d5bb30303
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.compute_accidental_hits.md
@@ -0,0 +1,45 @@
+### `tf.nn.compute_accidental_hits(true_classes, sampled_candidates, num_true, seed=None, name=None)` {#compute_accidental_hits}
+
+Compute the position ids in `sampled_candidates` matching `true_classes`.
+
+In Candidate Sampling, this operation facilitates virtually removing
+sampled classes which happen to match target classes.  This is done
+in Sampled Softmax and Sampled Logistic.
+
+See our [Candidate Sampling Algorithms
+Reference](http://www.tensorflow.org/extras/candidate_sampling.pdf).
+
+We presuppose that the `sampled_candidates` are unique.
+
+We call it an 'accidental hit' when one of the target classes
+matches one of the sampled classes.  This operation reports
+accidental hits as triples `(index, id, weight)`, where `index`
+represents the row number in `true_classes`, `id` represents the
+position in `sampled_candidates`, and weight is `-FLOAT_MAX`.
+
+The result of this op should be passed through a `sparse_to_dense`
+operation, then added to the logits of the sampled classes. This
+removes the contradictory effect of accidentally sampling the true
+target classes as noise classes for the same example.
+
+##### Args:
+
+
+*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
+    num_true]`. The target classes.
+*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
+    The sampled_candidates output of CandidateSampler.
+*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
+*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+
+*  <b>`indices`</b>: A `Tensor` of type `int32` and shape `[num_accidental_hits]`.
+    Values indicate rows in `true_classes`.
+*  <b>`ids`</b>: A `Tensor` of type `int64` and shape `[num_accidental_hits]`.
+    Values indicate positions in `sampled_candidates`.
+*  <b>`weights`</b>: A `Tensor` of type `float` and shape `[num_accidental_hits]`.
+    Each value is `-FLOAT_MAX`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.conv2d.md
new file mode 100644
index 0000000000..684a3d5727
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.conv2d.md
@@ -0,0 +1,49 @@
+### `tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)` {#conv2d}
+
+Computes a 2-D convolution given 4-D `input` and `filter` tensors.
+
+Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
+and a filter / kernel tensor of shape
+`[filter_height, filter_width, in_channels, out_channels]`, this op
+performs the following:
+
+1. Flattens the filter to a 2-D matrix with shape
+   `[filter_height * filter_width * in_channels, output_channels]`.
+2. Extracts image patches from the input tensor to form a *virtual*
+   tensor of shape `[batch, out_height, out_width,
+   filter_height * filter_width * in_channels]`.
+3. For each patch, right-multiplies the filter matrix and the image patch
+   vector.
+
+In detail, with the default NHWC format,
+
+    output[b, i, j, k] =
+        sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
+                        filter[di, dj, q, k]
+
+Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
+horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`filter`</b>: A `Tensor`. Must have the same type as `input`.
+*  <b>`strides`</b>: A list of `ints`.
+    1-D of length 4.  The stride of the sliding window for each dimension
+    of `input`. Must be in the same order as the dimension specified with format.
+*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
+    The type of padding algorithm to use.
+*  <b>`use_cudnn_on_gpu`</b>: An optional `bool`. Defaults to `True`.
+*  <b>`data_format`</b>: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`.
+    Specify the data format of the input and output data. With the
+    default format "NHWC", the data is stored in the order of:
+        [batch, in_height, in_width, in_channels].
+    Alternatively, the format could be "NCHW", the data storage order of:
+        [batch, in_channels, in_height, in_width].
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.conv3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.conv3d.md
deleted file mode 100644
index 886744c595..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.conv3d.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.nn.conv3d(input, filter, strides, padding, name=None)` {#conv3d}
-
-Computes a 3-D convolution given 5-D `input` and `filter` tensors.
-
-In signal processing, cross-correlation is a measure of similarity of
-two waveforms as a function of a time-lag applied to one of them. This
-is also known as a sliding dot product or sliding inner-product.
-
-Our Conv3D implements a form of cross-correlation.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-    Shape `[batch, in_depth, in_height, in_width, in_channels]`.
-*  <b>`filter`</b>: A `Tensor`. Must have the same type as `input`.
-    Shape `[filter_depth, filter_height, filter_width, in_channels, out_channels]`.
-    `in_channels` must match between `input` and `filter`.
-*  <b>`strides`</b>: A list of `ints` that has length `>= 5`.
-    1-D tensor of length 5. The stride of the sliding window for each
-    dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
-    The type of padding algorithm to use.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.depthwise_conv2d_native.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.depthwise_conv2d_native.md
deleted file mode 100644
index c2736f1ba9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.depthwise_conv2d_native.md
+++ /dev/null
@@ -1,37 +0,0 @@
-### `tf.nn.depthwise_conv2d_native(input, filter, strides, padding, name=None)` {#depthwise_conv2d_native}
-
-Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
-
-Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
-and a filter / kernel tensor of shape
-`[filter_height, filter_width, in_channels, channel_multiplier]`, containing
-`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
-a different filter to each input channel (expanding from 1 channel to
-`channel_multiplier` channels for each), then concatenates the results
-together. Thus, the output has `in_channels * channel_multiplier` channels.
-
-for k in 0..in_channels-1
-  for q in 0..channel_multiplier-1
-    output[b, i, j, k * channel_multiplier + q] =
-      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
-                        filter[di, dj, k, q]
-
-Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
-horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-*  <b>`filter`</b>: A `Tensor`. Must have the same type as `input`.
-*  <b>`strides`</b>: A list of `ints`.
-    1-D of length 4.  The stride of the sliding window for each dimension
-    of `input`.
-*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
-    The type of padding algorithm to use.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.elu.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.elu.md
deleted file mode 100644
index cef8dedb50..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.elu.md
+++ /dev/null
@@ -1,17 +0,0 @@
-### `tf.nn.elu(features, name=None)` {#elu}
-
-Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
-
-See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
-](http://arxiv.org/abs/1511.07289)
-
-##### Args:
-
-
-*  <b>`features`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `features`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.in_top_k.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.in_top_k.md
deleted file mode 100644
index f46780649d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.in_top_k.md
+++ /dev/null
@@ -1,33 +0,0 @@
-### `tf.nn.in_top_k(predictions, targets, k, name=None)` {#in_top_k}
-
-Says whether the targets are in the top `K` predictions.
-
-This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the
-prediction for the target class is among the top `k` predictions among
-all predictions for example `i`. Note that the behavior of `InTopK` differs
-from the `TopK` op in its handling of ties; if multiple classes have the
-same prediction value and straddle the top-`k` boundary, all of those
-classes are considered to be in the top `k`.
-
-More formally, let
-
-  \\(predictions_i\\) be the predictions for all classes for example `i`,
-  \\(targets_i\\) be the target class for example `i`,
-  \\(out_i\\) be the output for example `i`,
-
-$$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$
-
-##### Args:
-
-
-*  <b>`predictions`</b>: A `Tensor` of type `float32`.
-    A `batch_size` x `classes` tensor.
-*  <b>`targets`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    A `batch_size` vector of class ids.
-*  <b>`k`</b>: An `int`. Number of top elements to look at for computing precision.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.top_k.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.top_k.md
new file mode 100644
index 0000000000..819c0ad068
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.top_k.md
@@ -0,0 +1,31 @@
+### `tf.nn.top_k(input, k=1, sorted=True, name=None)` {#top_k}
+
+Finds values and indices of the `k` largest entries for the last dimension.
+
+If the input is a vector (rank-1), finds the `k` largest entries in the vector
+and outputs their values and indices as vectors.  Thus `values[j]` is the
+`j`-th largest entry in `input`, and its index is `indices[j]`.
+
+For matrices (resp. higher rank input), computes the top `k` entries in each
+row (resp. vector along the last dimension).  Thus,
+
+    values.shape = indices.shape = input.shape[:-1] + [k]
+
+If two elements are equal, the lower-index element appears first.
+
+##### Args:
+
+
+*  <b>`input`</b>: 1-D or higher `Tensor` with last dimension at least `k`.
+*  <b>`k`</b>: 0-D `int32` `Tensor`.  Number of top elements to look for along the last
+    dimension (along each row for matrices).
+*  <b>`sorted`</b>: If true the resulting `k` elements will be sorted by the values in
+    descending order.
+*  <b>`name`</b>: Optional name for the operation.
+
+##### Returns:
+
+
+*  <b>`values`</b>: The `k` largest elements along each last dimensional slice.
+*  <b>`indices`</b>: The indices of `values` within the last dimension of `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.uniform_candidate_sampler.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.uniform_candidate_sampler.md
new file mode 100644
index 0000000000..c34056dc84
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.uniform_candidate_sampler.md
@@ -0,0 +1,49 @@
+### `tf.nn.uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)` {#uniform_candidate_sampler}
+
+Samples a set of classes using a uniform base distribution.
+
+This operation randomly samples a tensor of sampled classes
+(`sampled_candidates`) from the range of integers `[0, range_max)`.
+
+The elements of `sampled_candidates` are drawn without replacement
+(if `unique=True`) or with replacement (if `unique=False`) from
+the base distribution.
+
+The base distribution for this operation is the uniform distribution
+over the range of integers `[0, range_max)`.
+
+In addition, this operation returns tensors `true_expected_count`
+and `sampled_expected_count` representing the number of times each
+of the target classes (`true_classes`) and the sampled
+classes (`sampled_candidates`) is expected to occur in an average
+tensor of sampled classes.  These values correspond to `Q(y|x)`
+defined in [this
+document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
+If `unique=True`, then these are post-rejection probabilities and we
+compute them approximately.
+
+##### Args:
+
+
+*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
+    num_true]`. The target classes.
+*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
+*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
+*  <b>`unique`</b>: A `bool`. Determines whether all sampled classes in a batch are
+    unique.
+*  <b>`range_max`</b>: An `int`. The number of possible classes.
+*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+
+*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
+    The sampled classes.
+*  <b>`true_expected_count`</b>: A tensor of type `float`.  Same shape as
+    `true_classes`. The expected counts under the sampling distribution
+    of each of `true_classes`.
+*  <b>`sampled_expected_count`</b>: A tensor of type `float`. Same shape as
+    `sampled_candidates`. The expected counts under the sampling distribution
+    of each of `sampled_candidates`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.one_hot.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.one_hot.md
new file mode 100644
index 0000000000..eebb6ab643
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.one_hot.md
@@ -0,0 +1,129 @@
+### `tf.one_hot(indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None)` {#one_hot}
+
+Returns a one-hot tensor.
+
+The locations represented by indices in `indices` take value `on_value`,
+while all other locations take value `off_value`.
+
+`on_value` and `off_value` must have matching data types. If `dtype` is also
+provided, they must be the same data type as specified by `dtype`.
+
+If `on_value` is not provided, it will default to the value `1` with type
+`dtype`
+
+If `off_value` is not provided, it will default to the value `0` with type
+`dtype`
+
+If the input `indices` is rank `N`, the output will have rank `N+1`. The
+new axis is created at dimension `axis` (default: the new axis is appended
+at the end).
+
+If `indices` is a scalar the output shape will be a vector of length `depth`
+
+If `indices` is a vector of length `features`, the output shape will be:
+```
+  features x depth if axis == -1
+  depth x features if axis == 0
+```
+
+If `indices` is a matrix (batch) with shape `[batch, features]`, the output
+shape will be:
+```
+  batch x features x depth if axis == -1
+  batch x depth x features if axis == 1
+  depth x batch x features if axis == 0
+```
+
+If `dtype` is not provided, it will attempt to assume the data type of
+`on_value` or `off_value`, if one or both are passed in. If none of
+`on_value`, `off_value`, or `dtype` are provided, `dtype` will default to the
+value `tf.float32`
+
+Note: If a non-numeric data type output is desired (tf.string, tf.bool, etc.),
+both `on_value` and `off_value` _must_ be provided to `one_hot`
+
+Examples
+=========
+
+Suppose that
+
+```
+  indices = [0, 2, -1, 1]
+  depth = 3
+  on_value = 5.0
+  off_value = 0.0
+  axis = -1
+```
+
+Then output is `[4 x 3]`:
+
+```
+  output =
+  [5.0 0.0 0.0]  // one_hot(0)
+  [0.0 0.0 5.0]  // one_hot(2)
+  [0.0 0.0 0.0]  // one_hot(-1)
+  [0.0 5.0 0.0]  // one_hot(1)
+```
+
+Suppose that
+
+```
+  indices = [[0, 2], [1, -1]]
+  depth = 3
+  on_value = 1.0
+  off_value = 0.0
+  axis = -1
+```
+
+Then output is `[2 x 2 x 3]`:
+
+```
+  output =
+  [
+    [1.0, 0.0, 0.0]  // one_hot(0)
+    [0.0, 0.0, 1.0]  // one_hot(2)
+  ][
+    [0.0, 1.0, 0.0]  // one_hot(1)
+    [0.0, 0.0, 0.0]  // one_hot(-1)
+  ]
+```
+
+Using default values for `on_value` and `off_value`:
+
+```
+  indices = [0, 1, 2]
+  depth = 3
+```
+
+The output will be
+
+```
+  output =
+  [[1., 0., 0.],
+   [0., 1., 0.],
+   [0., 0., 1.]]
+```
+
+##### Args:
+
+
+*  <b>`indices`</b>: A `Tensor` of indices.
+*  <b>`depth`</b>: A scalar defining the depth of the one hot dimension.
+*  <b>`on_value`</b>: A scalar defining the value to fill in output when `indices[j]
+    = i`. (default: 1)
+*  <b>`off_value`</b>: A scalar defining the value to fill in output when `indices[j]
+    != i`. (default: 0)
+*  <b>`axis`</b>: The axis to fill (default: -1, a new inner-most axis).
+*  <b>`dtype`</b>: The data type of the output tensor.
+
+##### Returns:
+
+
+*  <b>`output`</b>: The one-hot tensor.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If dtype of either `on_value` or `off_value` don't match `dtype`
+*  <b>`TypeError`</b>: If dtype of `on_value` and `off_value` don't match one another
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.op_scope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.op_scope.md
deleted file mode 100644
index c1002fd125..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.op_scope.md
+++ /dev/null
@@ -1,36 +0,0 @@
-### `tf.op_scope(values, name, default_name=None)` {#op_scope}
-
-Returns a context manager for use when defining a Python op.
-
-This context manager validates that the given `values` are from the
-same graph, ensures that graph is the default graph, and pushes a
-name scope.
-
-For example, to define a new Python op called `my_op`:
-
-```python
-def my_op(a, b, c, name=None):
-  with tf.op_scope([a, b, c], name, "MyOp") as scope:
-    a = tf.convert_to_tensor(a, name="a")
-    b = tf.convert_to_tensor(b, name="b")
-    c = tf.convert_to_tensor(c, name="c")
-    # Define some computation that uses `a`, `b`, and `c`.
-    return foo_op(..., name=scope)
-```
-
-##### Args:
-
-
-*  <b>`values`</b>: The list of `Tensor` arguments that are passed to the op function.
-*  <b>`name`</b>: The name argument that is passed to the op function.
-*  <b>`default_name`</b>: The default name to use if the `name` argument is `None`.
-
-##### Returns:
-
-  A context manager for use in defining Python ops. Yields the name scope.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if neither `name` nor `default_name` is provided.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.parse_example.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.parse_example.md
new file mode 100644
index 0000000000..2f2f511196
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.parse_example.md
@@ -0,0 +1,153 @@
+### `tf.parse_example(serialized, features, name=None, example_names=None)` {#parse_example}
+
+Parses `Example` protos into a `dict` of tensors.
+
+Parses a number of serialized [`Example`]
+(https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
+protos given in `serialized`.
+
+`example_names` may contain descriptive names for the corresponding serialized
+protos. These may be useful for debugging purposes, but they have no effect on
+the output. If not `None`, `example_names` must be the same length as `serialized`.
+
+This op parses serialized examples into a dictionary mapping keys to `Tensor`
+and `SparseTensor` objects. `features` is a dict from keys to `VarLenFeature`
+and `FixedLenFeature` objects. Each `VarLenFeature` is mapped to a
+`SparseTensor`, and each `FixedLenFeature` is mapped to a `Tensor`.
+
+Each `VarLenFeature` maps to a `SparseTensor` of the specified type
+representing a ragged matrix. Its indices are `[batch, index]` where `batch`
+is the batch entry the value is from in `serialized`, and `index` is the
+value's index in the list of values associated with that feature and example.
+
+Each `FixedLenFeature` `df` maps to a `Tensor` of the specified type (or
+`tf.float32` if not specified) and shape `(serialized.size(),) + df.shape`.
+
+`FixedLenFeature` entries with a `default_value` are optional. With no default
+value, we will fail if that `Feature` is missing from any example in
+`serialized`.
+
+Examples:
+
+For example, if one expects a `tf.float32` sparse feature `ft` and three
+serialized `Example`s are provided:
+
+```
+serialized = [
+  features
+    { feature { key: "ft" value { float_list { value: [1.0, 2.0] } } } },
+  features
+    { feature []},
+  features
+    { feature { key: "ft" value { float_list { value: [3.0] } } }
+]
+```
+
+then the output will look like:
+
+```
+{"ft": SparseTensor(indices=[[0, 0], [0, 1], [2, 0]],
+                    values=[1.0, 2.0, 3.0],
+                    shape=(3, 2)) }
+```
+
+Given two `Example` input protos in `serialized`:
+
+```
+[
+  features {
+    feature { key: "kw" value { bytes_list { value: [ "knit", "big" ] } } }
+    feature { key: "gps" value { float_list { value: [] } } }
+  },
+  features {
+    feature { key: "kw" value { bytes_list { value: [ "emmy" ] } } }
+    feature { key: "dank" value { int64_list { value: [ 42 ] } } }
+    feature { key: "gps" value { } }
+  }
+]
+```
+
+And arguments
+
+```
+example_names: ["input0", "input1"],
+features: {
+    "kw": VarLenFeature(tf.string),
+    "dank": VarLenFeature(tf.int64),
+    "gps": VarLenFeature(tf.float32),
+}
+```
+
+Then the output is a dictionary:
+
+```python
+{
+  "kw": SparseTensor(
+      indices=[[0, 0], [0, 1], [1, 0]],
+      values=["knit", "big", "emmy"]
+      shape=[2, 2]),
+  "dank": SparseTensor(
+      indices=[[1, 0]],
+      values=[42],
+      shape=[2, 1]),
+  "gps": SparseTensor(
+      indices=[],
+      values=[],
+      shape=[2, 0]),
+}
+```
+
+For dense results in two serialized `Example`s:
+
+```
+[
+  features {
+    feature { key: "age" value { int64_list { value: [ 0 ] } } }
+    feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
+   },
+   features {
+    feature { key: "age" value { int64_list { value: [] } } }
+    feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
+  }
+]
+```
+
+We can use arguments:
+
+```
+example_names: ["input0", "input1"],
+features: {
+    "age": FixedLenFeature([], dtype=tf.int64, default_value=-1),
+    "gender": FixedLenFeature([], dtype=tf.string),
+}
+```
+
+And the expected output is:
+
+```python
+{
+  "age": [[0], [-1]],
+  "gender": [["f"], ["f"]],
+}
+```
+
+##### Args:
+
+
+*  <b>`serialized`</b>: A vector (1-D Tensor) of strings, a batch of binary
+    serialized `Example` protos.
+*  <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
+    `VarLenFeature` values.
+*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`example_names`</b>: A vector (1-D Tensor) of strings (optional), the names of
+    the serialized protos in the batch.
+
+##### Returns:
+
+  A `dict` mapping feature keys to `Tensor` and `SparseTensor` values.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if any feature is invalid.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.random_shuffle.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.random_shuffle.md
new file mode 100644
index 0000000000..14f40d64af
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.random_shuffle.md
@@ -0,0 +1,29 @@
+### `tf.random_shuffle(value, seed=None, name=None)` {#random_shuffle}
+
+Randomly shuffles a tensor along its first dimension.
+
+The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
+to one and only one `output[i]`. For example, a mapping that might occur for a
+3x2 tensor is:
+
+```python
+[[1, 2],       [[5, 6],
+ [3, 4],  ==>   [1, 2],
+ [5, 6]]        [3, 4]]
+```
+
+##### Args:
+
+
+*  <b>`value`</b>: A Tensor to be shuffled.
+*  <b>`seed`</b>: A Python integer. Used to create a random seed for the distribution.
+    See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tensor of same shape and type as `value`, shuffled along its first
+  dimension.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reduce_join.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reduce_join.md
deleted file mode 100644
index c65c6022ba..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reduce_join.md
+++ /dev/null
@@ -1,49 +0,0 @@
-### `tf.reduce_join(inputs, reduction_indices, keep_dims=None, separator=None, name=None)` {#reduce_join}
-
-Joins a string Tensor across the given dimensions.
-
-Computes the string join across dimensions in the given string Tensor of shape
-`[d_0, d_1, ..., d_n-1]`.  Returns a new Tensor created by joining the input
-strings with the given separator (default: empty string).  Negative indices are
-counted backwards from the end, with `-1` being equivalent to `n - 1`.  Passing
-an empty `reduction_indices` joins all strings in linear index order and outputs
-a scalar string.
-
-
-For example:
-```
-# tensor `a` is [["a", "b"], ["c", "d"]]
-tf.reduce_join(a, 0) ==> ["ac", "bd"]
-tf.reduce_join(a, 1) ==> ["ab", "cd"]
-tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"]
-tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"]
-tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]]
-tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]]
-tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"]
-tf.reduce_join(a, [0, 1]) ==> ["acbd"]
-tf.reduce_join(a, [1, 0]) ==> ["abcd"]
-tf.reduce_join(a, []) ==> ["abcd"]
-```
-
-##### Args:
-
-
-*  <b>`inputs`</b>: A `Tensor` of type `string`.
-    The input to be joined.  All reduced indices must have non-zero size.
-*  <b>`reduction_indices`</b>: A `Tensor` of type `int32`.
-    The dimensions to reduce over.  Dimensions are reduced in the
-    order specified.  If `reduction_indices` has higher rank than `1`, it is
-    flattened.  Omitting `reduction_indices` is equivalent to passing
-    `[n-1, n-2, ..., 0]`.  Negative indices from `-n` to `-1` are supported.
-*  <b>`keep_dims`</b>: An optional `bool`. Defaults to `False`.
-    If `True`, retain reduced dimensions with length `1`.
-*  <b>`separator`</b>: An optional `string`. Defaults to `""`.
-    The separator to use when joining.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `string`.
-  Has shape equal to that of the input with reduced dimensions removed or
-  set to `1` depending on `keep_dims`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reduce_max.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reduce_max.md
new file mode 100644
index 0000000000..f137e8091c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reduce_max.md
@@ -0,0 +1,25 @@
+### `tf.reduce_max(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_max}
+
+Computes the maximum of elements across dimensions of a tensor.
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reduce_min.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reduce_min.md
new file mode 100644
index 0000000000..c93a902adc
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reduce_min.md
@@ -0,0 +1,25 @@
+### `tf.reduce_min(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_min}
+
+Computes the minimum of elements across dimensions of a tensor.
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.register_tensor_conversion_function.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.register_tensor_conversion_function.md
deleted file mode 100644
index e15dcf7b40..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.register_tensor_conversion_function.md
+++ /dev/null
@@ -1,42 +0,0 @@
-### `tf.register_tensor_conversion_function(base_type, conversion_func, priority=100)` {#register_tensor_conversion_function}
-
-Registers a function for converting objects of `base_type` to `Tensor`.
-
-The conversion function must have the following signature:
-
-    def conversion_func(value, dtype=None, name=None, as_ref=False):
-      # ...
-
-It must return a `Tensor` with the given `dtype` if specified. If the
-conversion function creates a new `Tensor`, it should use the given
-`name` if specified. All exceptions will be propagated to the caller.
-
-The conversion function may return `NotImplemented` for some
-inputs. In this case, the conversion process will continue to try
-subsequent conversion functions.
-
-If `as_ref` is true, the function must return a `Tensor` reference,
-such as a `Variable`.
-
-NOTE: The conversion functions will execute in order of priority,
-followed by order of registration. To ensure that a conversion function
-`F` runs before another conversion function `G`, ensure that `F` is
-registered with a smaller priority than `G`.
-
-##### Args:
-
-
-*  <b>`base_type`</b>: The base type or tuple of base types for all objects that
-    `conversion_func` accepts.
-*  <b>`conversion_func`</b>: A function that converts instances of `base_type` to
-    `Tensor`.
-*  <b>`priority`</b>: Optional integer that indicates the priority for applying this
-    conversion function. Conversion functions with smaller priority values
-    run earlier than conversion functions with larger priority values.
-    Defaults to 100.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If the arguments do not have the appropriate type.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reshape.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reshape.md
new file mode 100644
index 0000000000..057b29e91f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.reshape.md
@@ -0,0 +1,72 @@
+### `tf.reshape(tensor, shape, name=None)` {#reshape}
+
+Reshapes a tensor.
+
+Given `tensor`, this operation returns a tensor that has the same values
+as `tensor` with shape `shape`.
+
+If one component of `shape` is the special value -1, the size of that dimension
+is computed so that the total size remains constant.  In particular, a `shape`
+of `[-1]` flattens into 1-D.  At most one component of `shape` can be -1.
+
+If `shape` is 1-D or higher, then the operation returns a tensor with shape
+`shape` filled with the values of `tensor`. In this case, the number of elements
+implied by `shape` must be the same as the number of elements in `tensor`.
+
+For example:
+
+```prettyprint
+# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
+# tensor 't' has shape [9]
+reshape(t, [3, 3]) ==> [[1, 2, 3],
+                        [4, 5, 6],
+                        [7, 8, 9]]
+
+# tensor 't' is [[[1, 1], [2, 2]],
+#                [[3, 3], [4, 4]]]
+# tensor 't' has shape [2, 2, 2]
+reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
+                        [3, 3, 4, 4]]
+
+# tensor 't' is [[[1, 1, 1],
+#                 [2, 2, 2]],
+#                [[3, 3, 3],
+#                 [4, 4, 4]],
+#                [[5, 5, 5],
+#                 [6, 6, 6]]]
+# tensor 't' has shape [3, 2, 3]
+# pass '[-1]' to flatten 't'
+reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
+
+# -1 can also be used to infer the shape
+
+# -1 is inferred to be 9:
+reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
+                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
+# -1 is inferred to be 2:
+reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
+                         [4, 4, 4, 5, 5, 5, 6, 6, 6]]
+# -1 is inferred to be 3:
+reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
+                              [2, 2, 2],
+                              [3, 3, 3]],
+                             [[4, 4, 4],
+                              [5, 5, 5],
+                              [6, 6, 6]]]
+
+# tensor 't' is [7]
+# shape `[]` reshapes to a scalar
+reshape(t, []) ==> 7
+```
+
+##### Args:
+
+
+*  <b>`tensor`</b>: A `Tensor`.
+*  <b>`shape`</b>: A `Tensor` of type `int32`. Defines the shape of the output tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.round.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.round.md
new file mode 100644
index 0000000000..8d2ce32921
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.round.md
@@ -0,0 +1,21 @@
+### `tf.round(x, name=None)` {#round}
+
+Rounds the values of a tensor to the nearest integer, element-wise.
+
+For example:
+
+```python
+# 'a' is [0.9, 2.5, 2.3, -4.4]
+tf.round(a) ==> [ 1.0, 3.0, 2.0, -4.0 ]
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` of type `float` or `double`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of same shape and type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.saturate_cast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.saturate_cast.md
deleted file mode 100644
index 6a77c2791e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.saturate_cast.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.saturate_cast(value, dtype, name=None)` {#saturate_cast}
-
-Performs a safe saturating cast of `value` to `dtype`.
-
-This function casts the input to `dtype` without applying any scaling.  If
-there is a danger that values would over or underflow in the cast, this op
-applies the appropriate clamping before the cast.
-
-##### Args:
-
-
-*  <b>`value`</b>: A `Tensor`.
-*  <b>`dtype`</b>: The desired output `DType`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  `value` safely cast to `dtype`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.scatter_update.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.scatter_update.md
deleted file mode 100644
index f865b8e9e8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.scatter_update.md
+++ /dev/null
@@ -1,46 +0,0 @@
-### `tf.scatter_update(ref, indices, updates, use_locking=None, name=None)` {#scatter_update}
-
-Applies sparse updates to a variable reference.
-
-This operation computes
-
-    # Scalar indices
-    ref[indices, ...] = updates[...]
-
-    # Vector indices (for each i)
-    ref[indices[i], ...] = updates[i, ...]
-
-    # High rank indices (for each i, ..., j)
-    ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
-
-This operation outputs `ref` after the update is done.
-This makes it easier to chain operations that need to use the reset value.
-
-If values in `ref` is to be updated more than once, because there are
-duplicate entires in `indices`, the order at which the updates happen
-for each value is undefined.
-
-Requires `updates.shape = indices.shape + ref.shape[1:]`.
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/ScatterUpdate.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`ref`</b>: A mutable `Tensor`. Should be from a `Variable` node.
-*  <b>`indices`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    A tensor of indices into the first dimension of `ref`.
-*  <b>`updates`</b>: A `Tensor`. Must have the same type as `ref`.
-    A tensor of updated values to store in `ref`.
-*  <b>`use_locking`</b>: An optional `bool`. Defaults to `True`.
-    If True, the assignment will be protected by a lock;
-    otherwise the behavior is undefined, but may exhibit less contention.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  Same as `ref`.  Returned as a convenience for operations that want
-  to use the updated values after the update is done.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.segment_min.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.segment_min.md
new file mode 100644
index 0000000000..5cacf2cf72
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.segment_min.md
@@ -0,0 +1,31 @@
+### `tf.segment_min(data, segment_ids, name=None)` {#segment_min}
+
+Computes the minimum along segments of a tensor.
+
+Read [the section on
+Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
+of segments.
+
+Computes a tensor such that
+\\(output_i = \min_j(data_j)\\) where `min` is over `j` such
+that `segment_ids[j] == i`.
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/SegmentMin.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    A 1-D tensor whose rank is equal to the rank of `data`'s
+    first dimension.  Values should be sorted and can be repeated.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `data`.
+  Has same shape as data, except for dimension 0 which
+  has size `k`, the number of segments.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.select.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.select.md
deleted file mode 100644
index b77c9612e8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.select.md
+++ /dev/null
@@ -1,56 +0,0 @@
-### `tf.select(condition, t, e, name=None)` {#select}
-
-Selects elements from `t` or `e`, depending on `condition`.
-
-The `t`, and `e` tensors must all have the same shape,
-and the output will also have that shape.  The `condition` tensor
-must be a scalar if `t` and `e` are scalars.  If `t` and `e` are vectors
-or higher rank, then `condition` must be either a vector with size
-matching the first dimension of `t`, or must have the same shape as `t`.
-
-The `condition` tensor acts as a mask that chooses, based on the value at each
-element, whether the corresponding element / row in the output should be
-taken from `t` (if true) or `e` (if false).
-
-If `condition` is a vector and `t` and `e` are higher rank matrices, then
-it chooses which row (outer dimension) to copy from `t` and `e`.
-If `condition` has the same shape as `t` and `e`, then it chooses which
-element to copy from `t` and `e`.
-
-For example:
-
-```prettyprint
-# 'condition' tensor is [[True,  False]
-#                        [False, True]]
-# 't' is [[1, 2],
-#         [3, 4]]
-# 'e' is [[5, 6],
-#         [7, 8]]
-select(condition, t, e) ==> [[1, 6],
-                             [7, 4]]
-
-
-# 'condition' tensor is [True, False]
-# 't' is [[1, 2],
-#         [3, 4]]
-# 'e' is [[5, 6],
-#         [7, 8]]
-select(condition, t, e) ==> [[1, 2],
-                             [7, 8]]
-
-```
-
-##### Args:
-
-
-*  <b>`condition`</b>: A `Tensor` of type `bool`.
-*  <b>`t`</b>: A `Tensor` which may have the same shape as `condition`.
-    If `condition` is rank 1, `t` may have higher rank,
-    but its first dimension must match the size of `condition`.
-*  <b>`e`</b>: A `Tensor` with the same type and shape as `t`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` with the same type and shape as `t` and `e`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sigmoid.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sigmoid.md
new file mode 100644
index 0000000000..b056a48716
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sigmoid.md
@@ -0,0 +1,18 @@
+### `tf.sigmoid(x, name=None)` {#sigmoid}
+
+Computes sigmoid of `x` element-wise.
+
+Specifically, `y = 1 / (1 + exp(-x))`.
+
+##### Args:
+
+
+*  <b>`x`</b>: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`,
+    or `qint32`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A Tensor with the same type as `x` if `x.dtype != qint32`
+    otherwise the return type is `quint8`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.space_to_batch.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.space_to_batch.md
new file mode 100644
index 0000000000..1999f21ea3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.space_to_batch.md
@@ -0,0 +1,44 @@
+### `tf.space_to_batch(input, paddings, block_size, name=None)` {#space_to_batch}
+
+SpaceToBatch for 4-D tensors of type T.
+
+Zero-pads and then rearranges (permutes) blocks of spatial data into batch.
+More specifically, this op outputs a copy of the input tensor where values from
+the `height` and `width` dimensions are moved to the `batch` dimension. After
+the zero-padding, both `height` and `width` of the input must be divisible by the
+block size.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. 4-D with shape `[batch, height, width, depth]`.
+*  <b>`paddings`</b>: A `Tensor` of type `int32`.
+    2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
+      the padding of the input with zeros across the spatial dimensions as follows:
+
+          paddings = [[pad_top, pad_bottom], [pad_left, pad_right]]
+
+      The effective spatial dimensions of the zero-padded input tensor will be:
+
+          height_pad = pad_top + height + pad_bottom
+          width_pad = pad_left + width + pad_right
+
+    The attr `block_size` must be greater than one. It indicates the block size.
+
+      * Non-overlapping blocks of size `block_size x block size` in the height and
+        width dimensions are rearranged into the batch dimension at each location.
+      * The batch of the output tensor is `batch * block_size * block_size`.
+      * Both height_pad and width_pad must be divisible by block_size.
+
+    The shape of the output will be:
+
+        [batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
+         depth]
+
+*  <b>`block_size`</b>: An `int`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_retain.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_retain.md
new file mode 100644
index 0000000000..dcaa303627
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_retain.md
@@ -0,0 +1,33 @@
+### `tf.sparse_retain(sp_input, to_retain)` {#sparse_retain}
+
+Retains specified non-empty values within a `SparseTensor`.
+
+For example, if `sp_input` has shape `[4, 5]` and 4 non-empty string values:
+
+    [0, 1]: a
+    [0, 3]: b
+    [2, 0]: c
+    [3, 1]: d
+
+and `to_retain = [True, False, False, True]`, then the output will
+be a `SparseTensor` of shape `[4, 5]` with 2 non-empty values:
+
+    [0, 1]: a
+    [3, 1]: d
+
+##### Args:
+
+
+*  <b>`sp_input`</b>: The input `SparseTensor` with `N` non-empty elements.
+*  <b>`to_retain`</b>: A bool vector of length `N` with `M` true values.
+
+##### Returns:
+
+  A `SparseTensor` with the same shape as the input and `M` non-empty
+  elements corresponding to the true positions in `to_retain`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_tensor_dense_matmul.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_tensor_dense_matmul.md
new file mode 100644
index 0000000000..5bb99ef029
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_tensor_dense_matmul.md
@@ -0,0 +1,163 @@
+### `tf.sparse_tensor_dense_matmul(sp_a, b, adjoint_a=False, adjoint_b=False, name=None)` {#sparse_tensor_dense_matmul}
+
+Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
+
+No validity checking is performed on the indices of A.  However, the following
+input format is recommended for optimal behavior:
+
+if adjoint_a == false:
+  A should be sorted in lexicographically increasing order.  Use
+  sparse_reorder if you're not sure.
+if adjoint_a == true:
+  A should be sorted in order of increasing dimension 1 (i.e., "column major"
+  order instead of "row major" order).
+
+Deciding when to use sparse_tensor_dense_matmul vs. matmul(sp_a=True):
+
+There are a number of questions to ask in the decision process, including:
+
+* Will the SparseTensor A fit in memory if densified?
+* Is the column count of the product large (>> 1)?
+* Is the density of A larger than approximately 15%?
+
+If the answer to several of these questions is yes, consider
+converting the SparseTensor to a dense one and using tf.matmul with sp_a=True.
+
+This operation tends to perform well when A is more sparse, if the column size
+of the product is small (e.g. matrix-vector multiplication), if sp_a.shape
+takes on large values.
+
+Below is a rough speed comparison between sparse_tensor_dense_matmul,
+labelled 'sparse', and matmul(sp_a=True), labelled 'dense'.  For purposes of
+the comparison, the time spent converting from a SparseTensor to a dense
+Tensor is not included, so it is overly conservative with respect to
+the time ratio.
+
+Benchmark system:
+CPU: Intel Ivybridge with HyperThreading (6 cores) dL1:32KB dL2:256KB dL3:12MB
+GPU: NVidia Tesla k40c
+
+Compiled with:
+-c opt --config=cuda --copt=-mavx
+
+```tensorflow/python/sparse_tensor_dense_matmul_op_test --benchmarks
+A sparse [m, k] with % nonzero values between 1% and 80%
+B dense [k, n]
+
+% nnz    n       gpu     m       k       dt(dense)       dt(sparse)      dt(sparse)/dt(dense)
+0.01     1       True    100     100     0.000221166     0.00010154      0.459112
+0.01     1       True    100     1000    0.00033858      0.000109275     0.322745
+0.01     1       True    1000    100     0.000310557     9.85661e-05     0.317385
+0.01     1       True    1000    1000    0.0008721       0.000100875     0.115669
+0.01     1       False   100     100     0.000208085     0.000107603     0.51711
+0.01     1       False   100     1000    0.000327112     9.51118e-05     0.290762
+0.01     1       False   1000    100     0.000308222     0.00010345      0.335635
+0.01     1       False   1000    1000    0.000865721     0.000101397     0.117124
+0.01     10      True    100     100     0.000218522     0.000105537     0.482958
+0.01     10      True    100     1000    0.000340882     0.000111641     0.327506
+0.01     10      True    1000    100     0.000315472     0.000117376     0.372064
+0.01     10      True    1000    1000    0.000905493     0.000123263     0.136128
+0.01     10      False   100     100     0.000221529     9.82571e-05     0.44354
+0.01     10      False   100     1000    0.000330552     0.000112615     0.340687
+0.01     10      False   1000    100     0.000341277     0.000114097     0.334324
+0.01     10      False   1000    1000    0.000819944     0.000120982     0.147549
+0.01     25      True    100     100     0.000207806     0.000105977     0.509981
+0.01     25      True    100     1000    0.000322879     0.00012921      0.400181
+0.01     25      True    1000    100     0.00038262      0.000141583     0.370035
+0.01     25      True    1000    1000    0.000865438     0.000202083     0.233504
+0.01     25      False   100     100     0.000209401     0.000104696     0.499979
+0.01     25      False   100     1000    0.000321161     0.000130737     0.407076
+0.01     25      False   1000    100     0.000377012     0.000136801     0.362856
+0.01     25      False   1000    1000    0.000861125     0.00020272      0.235413
+0.2      1       True    100     100     0.000206952     9.69219e-05     0.46833
+0.2      1       True    100     1000    0.000348674     0.000147475     0.422959
+0.2      1       True    1000    100     0.000336908     0.00010122      0.300439
+0.2      1       True    1000    1000    0.001022        0.000203274     0.198898
+0.2      1       False   100     100     0.000207532     9.5412e-05      0.459746
+0.2      1       False   100     1000    0.000356127     0.000146824     0.41228
+0.2      1       False   1000    100     0.000322664     0.000100918     0.312764
+0.2      1       False   1000    1000    0.000998987     0.000203442     0.203648
+0.2      10      True    100     100     0.000211692     0.000109903     0.519165
+0.2      10      True    100     1000    0.000372819     0.000164321     0.440753
+0.2      10      True    1000    100     0.000338651     0.000144806     0.427596
+0.2      10      True    1000    1000    0.00108312      0.000758876     0.70064
+0.2      10      False   100     100     0.000215727     0.000110502     0.512231
+0.2      10      False   100     1000    0.000375419     0.0001613       0.429653
+0.2      10      False   1000    100     0.000336999     0.000145628     0.432132
+0.2      10      False   1000    1000    0.00110502      0.000762043     0.689618
+0.2      25      True    100     100     0.000218705     0.000129913     0.594009
+0.2      25      True    100     1000    0.000394794     0.00029428      0.745402
+0.2      25      True    1000    100     0.000404483     0.0002693       0.665788
+0.2      25      True    1000    1000    0.0012002       0.00194494      1.62052
+0.2      25      False   100     100     0.000221494     0.0001306       0.589632
+0.2      25      False   100     1000    0.000396436     0.000297204     0.74969
+0.2      25      False   1000    100     0.000409346     0.000270068     0.659754
+0.2      25      False   1000    1000    0.00121051      0.00193737      1.60046
+0.5      1       True    100     100     0.000214981     9.82111e-05     0.456836
+0.5      1       True    100     1000    0.000415328     0.000223073     0.537101
+0.5      1       True    1000    100     0.000358324     0.00011269      0.314492
+0.5      1       True    1000    1000    0.00137612      0.000437401     0.317851
+0.5      1       False   100     100     0.000224196     0.000101423     0.452386
+0.5      1       False   100     1000    0.000400987     0.000223286     0.556841
+0.5      1       False   1000    100     0.000368825     0.00011224      0.304318
+0.5      1       False   1000    1000    0.00136036      0.000429369     0.31563
+0.5      10      True    100     100     0.000222125     0.000112308     0.505608
+0.5      10      True    100     1000    0.000461088     0.00032357      0.701753
+0.5      10      True    1000    100     0.000394624     0.000225497     0.571422
+0.5      10      True    1000    1000    0.00158027      0.00190898      1.20801
+0.5      10      False   100     100     0.000232083     0.000114978     0.495418
+0.5      10      False   100     1000    0.000454574     0.000324632     0.714146
+0.5      10      False   1000    100     0.000379097     0.000227768     0.600817
+0.5      10      False   1000    1000    0.00160292      0.00190168      1.18638
+0.5      25      True    100     100     0.00023429      0.000151703     0.647501
+0.5      25      True    100     1000    0.000497462     0.000598873     1.20386
+0.5      25      True    1000    100     0.000460778     0.000557038     1.20891
+0.5      25      True    1000    1000    0.00170036      0.00467336      2.74845
+0.5      25      False   100     100     0.000228981     0.000155334     0.678371
+0.5      25      False   100     1000    0.000496139     0.000620789     1.25124
+0.5      25      False   1000    100     0.00045473      0.000551528     1.21287
+0.5      25      False   1000    1000    0.00171793      0.00467152      2.71927
+0.8      1       True    100     100     0.000222037     0.000105301     0.47425
+0.8      1       True    100     1000    0.000410804     0.000329327     0.801664
+0.8      1       True    1000    100     0.000349735     0.000131225     0.375212
+0.8      1       True    1000    1000    0.00139219      0.000677065     0.48633
+0.8      1       False   100     100     0.000214079     0.000107486     0.502085
+0.8      1       False   100     1000    0.000413746     0.000323244     0.781261
+0.8      1       False   1000    100     0.000348983     0.000131983     0.378193
+0.8      1       False   1000    1000    0.00136296      0.000685325     0.50282
+0.8      10      True    100     100     0.000229159     0.00011825      0.516017
+0.8      10      True    100     1000    0.000498845     0.000532618     1.0677
+0.8      10      True    1000    100     0.000383126     0.00029935      0.781336
+0.8      10      True    1000    1000    0.00162866      0.00307312      1.88689
+0.8      10      False   100     100     0.000230783     0.000124958     0.541452
+0.8      10      False   100     1000    0.000493393     0.000550654     1.11606
+0.8      10      False   1000    100     0.000377167     0.000298581     0.791642
+0.8      10      False   1000    1000    0.00165795      0.00305103      1.84024
+0.8      25      True    100     100     0.000233496     0.000175241     0.75051
+0.8      25      True    100     1000    0.00055654      0.00102658      1.84458
+0.8      25      True    1000    100     0.000463814     0.000783267     1.68875
+0.8      25      True    1000    1000    0.00186905      0.00755344      4.04132
+0.8      25      False   100     100     0.000240243     0.000175047     0.728625
+0.8      25      False   100     1000    0.000578102     0.00104499      1.80763
+0.8      25      False   1000    100     0.000485113     0.000776849     1.60138
+0.8      25      False   1000    1000    0.00211448      0.00752736      3.55992
+```
+
+##### Args:
+
+
+*  <b>`sp_a`</b>: SparseTensor A, of rank 2.
+*  <b>`b`</b>: A dense Matrix with the same dtype as sp_a.
+*  <b>`adjoint_a`</b>: Use the adjoint of A in the matrix multiply.  If A is complex,
+    this is transpose(conj(A)).  Otherwise it's transpose(A).
+*  <b>`adjoint_b`</b>: Use the adjoint of B in the matrix multiply.  If B is complex,
+    this is transpose(conj(B)).  Otherwise it's transpose(B).
+*  <b>`name`</b>: A name prefix for the returned tensors (optional)
+
+##### Returns:
+
+  A dense matrix (pseudo-code in dense np.matrix notation):
+    A = A.H if adjoint_a else A
+    B = B.H if adjoint_b else B
+    return A*B
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_tensor_to_dense.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_tensor_to_dense.md
deleted file mode 100644
index a0c0a6ca9c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_tensor_to_dense.md
+++ /dev/null
@@ -1,43 +0,0 @@
-### `tf.sparse_tensor_to_dense(sp_input, default_value=0, validate_indices=True, name=None)` {#sparse_tensor_to_dense}
-
-Converts a `SparseTensor` into a dense tensor.
-
-This op is a convenience wrapper around `sparse_to_dense` for `SparseTensor`s.
-
-For example, if `sp_input` has shape `[3, 5]` and non-empty string values:
-
-    [0, 1]: a
-    [0, 3]: b
-    [2, 0]: c
-
-and `default_value` is `x`, then the output will be a dense `[3, 5]`
-string tensor with values:
-
-    [[x a x b x]
-     [x x x x x]
-     [c x x x x]]
-
-Indices must be without repeats.  This is only
-tested if validate_indices is True.
-
-##### Args:
-
-
-*  <b>`sp_input`</b>: The input `SparseTensor`.
-*  <b>`default_value`</b>: Scalar value to set for indices not specified in
-    `sp_input`.  Defaults to zero.
-*  <b>`validate_indices`</b>: A boolean value.  If `True`, indices are checked to make
-    sure they are sorted in lexicographic order and that there are no repeats.
-*  <b>`name`</b>: A name prefix for the returned tensors (optional).
-
-##### Returns:
-
-  A dense tensor with shape `sp_input.shape` and values specified by
-  the non-empty values in `sp_input`. Indices not in `sp_input` are assigned
-  `default_value`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_to_dense.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_to_dense.md
new file mode 100644
index 0000000000..d4df5a9183
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.sparse_to_dense.md
@@ -0,0 +1,45 @@
+### `tf.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=0, validate_indices=True, name=None)` {#sparse_to_dense}
+
+Converts a sparse representation into a dense tensor.
+
+Builds an array `dense` with shape `output_shape` such that
+
+```python
+# If sparse_indices is scalar
+dense[i] = (i == sparse_indices ? sparse_values : default_value)
+
+# If sparse_indices is a vector, then for each i
+dense[sparse_indices[i]] = sparse_values[i]
+
+# If sparse_indices is an n by d matrix, then for each i in [0, n)
+dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
+```
+
+All other values in `dense` are set to `default_value`.  If `sparse_values`
+is a scalar, all sparse indices are set to this single value.
+
+Indices should be sorted in lexicographic order, and indices must not
+contain any repeats. If `validate_indices` is True, these properties
+are checked during execution.
+
+##### Args:
+
+
+*  <b>`sparse_indices`</b>: A 0-D, 1-D, or 2-D `Tensor` of type `int32` or `int64`.
+    `sparse_indices[i]` contains the complete index where `sparse_values[i]`
+    will be placed.
+*  <b>`output_shape`</b>: A 1-D `Tensor` of the same type as `sparse_indices`.  Shape
+    of the dense output tensor.
+*  <b>`sparse_values`</b>: A 0-D or 1-D `Tensor`.  Values corresponding to each row of
+    `sparse_indices`, or a scalar value to be used for all sparse indices.
+*  <b>`default_value`</b>: A 0-D `Tensor` of the same type as `sparse_values`.  Value
+    to set for indices not specified in `sparse_indices`.  Defaults to zero.
+*  <b>`validate_indices`</b>: A boolean value.  If True, indices are checked to make
+    sure they are sorted in lexicographic order and that there are no repeats.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  Dense `Tensor` of shape `output_shape`.  Has the same type as
+  `sparse_values`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.string_to_hash_bucket_fast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.string_to_hash_bucket_fast.md
deleted file mode 100644
index e684058326..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.string_to_hash_bucket_fast.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.string_to_hash_bucket_fast(input, num_buckets, name=None)` {#string_to_hash_bucket_fast}
-
-Converts each string in the input Tensor to its hash mod by a number of buckets.
-
-The hash function is deterministic on the content of the string within the
-process and will never change. However, it is not suitable for cryptography.
-This function may be used when CPU time is scarce and inputs are trusted or
-unimportant. There is a risk of adversaries constructing inputs that all hash
-to the same bucket. To prevent this problem, use a strong hash function with
-`tf.string_to_hash_bucket_strong`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `string`. The strings to assign a hash bucket.
-*  <b>`num_buckets`</b>: An `int` that is `>= 1`. The number of buckets.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `int64`.
-  A Tensor of the same shape as the input `string_tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.string_to_number.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.string_to_number.md
new file mode 100644
index 0000000000..dfbc0c6b6c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.string_to_number.md
@@ -0,0 +1,20 @@
+### `tf.string_to_number(string_tensor, out_type=None, name=None)` {#string_to_number}
+
+Converts each string in the input Tensor to the specified numeric type.
+
+(Note that int32 overflow results in an error while float overflow
+results in a rounded value.)
+
+##### Args:
+
+
+*  <b>`string_tensor`</b>: A `Tensor` of type `string`.
+*  <b>`out_type`</b>: An optional `tf.DType` from: `tf.float32, tf.int32`. Defaults to `tf.float32`.
+    The numeric type to interpret each string in string_tensor as.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `out_type`.
+  A Tensor of the same shape as the input `string_tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.test.compute_gradient_error.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.test.compute_gradient_error.md
deleted file mode 100644
index d2c91a66b3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.test.compute_gradient_error.md
+++ /dev/null
@@ -1,36 +0,0 @@
-### `tf.test.compute_gradient_error(x, x_shape, y, y_shape, x_init_value=None, delta=0.001, init_targets=None)` {#compute_gradient_error}
-
-Computes the gradient error.
-
-Computes the maximum error for dy/dx between the computed Jacobian and the
-numerically estimated Jacobian.
-
-This function will modify the tensors passed in as it adds more operations
-and hence changing the consumers of the operations of the input tensors.
-
-This function adds operations to the current session. To compute the error
-using a particular device, such as a GPU, use the standard methods for
-setting a device (e.g. using with sess.graph.device() or setting a device
-function in the session constructor).
-
-##### Args:
-
-
-*  <b>`x`</b>: a tensor or list of tensors
-*  <b>`x_shape`</b>: the dimensions of x as a tuple or an array of ints. If x is a list,
-  then this is the list of shapes.
-
-*  <b>`y`</b>: a tensor
-*  <b>`y_shape`</b>: the dimensions of y as a tuple or an array of ints.
-*  <b>`x_init_value`</b>: (optional) a numpy array of the same shape as "x"
-    representing the initial value of x. If x is a list, this should be a list
-    of numpy arrays.  If this is none, the function will pick a random tensor
-    as the initial value.
-*  <b>`delta`</b>: (optional) the amount of perturbation.
-*  <b>`init_targets`</b>: list of targets to run to initialize model params.
-    TODO(mrry): Remove this argument.
-
-##### Returns:
-
-  The maximum error in between the two Jacobians.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.SessionManager.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.SessionManager.md
new file mode 100644
index 0000000000..8bebb8bd29
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.SessionManager.md
@@ -0,0 +1,187 @@
+Training helper that restores from checkpoint and creates session.
+
+This class is a small wrapper that takes care of session creation and
+checkpoint recovery. It also provides functions that to facilitate
+coordination among multiple training threads or processes.
+
+* Checkpointing trained variables as the training progresses.
+* Initializing variables on startup, restoring them from the most recent
+  checkpoint after a crash, or wait for checkpoints to become available.
+
+### Usage:
+
+```python
+with tf.Graph().as_default():
+   ...add operations to the graph...
+  # Create a SessionManager that will checkpoint the model in '/tmp/mydir'.
+  sm = SessionManager()
+  sess = sm.prepare_session(master, init_op, saver, checkpoint_dir)
+  # Use the session to train the graph.
+  while True:
+    sess.run(<my_train_op>)
+```
+
+`prepare_session()` initializes or restores a model. It requires `init_op`
+and `saver` as an argument.
+
+A second process could wait for the model to be ready by doing the following:
+
+```python
+with tf.Graph().as_default():
+   ...add operations to the graph...
+  # Create a SessionManager that will wait for the model to become ready.
+  sm = SessionManager()
+  sess = sm.wait_for_session(master)
+  # Use the session to train the graph.
+  while True:
+    sess.run(<my_train_op>)
+```
+
+`wait_for_session()` waits for a model to be initialized by other processes.
+- - -
+
+#### `tf.train.SessionManager.__init__(local_init_op=None, ready_op=None, graph=None, recovery_wait_secs=30)` {#SessionManager.__init__}
+
+Creates a SessionManager.
+
+The `local_init_op` is an `Operation` that is run always after a new session
+was created. If `None`, this step is skipped.
+
+The `ready_op` is an `Operation` used to check if the model is ready.  The
+model is considered ready if that operation returns an empty string tensor.
+If the operation returns non empty string tensor, the elements are
+concatenated and used to indicate to the user why the model is not ready.
+
+If `ready_op` is `None`, the model is not checked for readiness.
+
+`recovery_wait_secs` is the number of seconds between checks that
+the model is ready.  It is used by processes to wait for a model to
+be initialized or restored.  Defaults to 30 seconds.
+
+##### Args:
+
+
+*  <b>`local_init_op`</b>: An `Operation` run immediately after session creation.
+     Usually used to initialize tables and local variables.
+*  <b>`ready_op`</b>: An `Operation` to check if the model is initialized.
+*  <b>`graph`</b>: The `Graph` that the model will use.
+*  <b>`recovery_wait_secs`</b>: Seconds between checks for the model to be ready.
+
+
+- - -
+
+#### `tf.train.SessionManager.prepare_session(master, init_op=None, saver=None, checkpoint_dir=None, wait_for_checkpoint=False, max_wait_secs=7200, config=None, init_feed_dict=None, init_fn=None)` {#SessionManager.prepare_session}
+
+Creates a `Session`. Makes sure the model is ready to be used.
+
+Creates a `Session` on 'master'. If a `saver` object is passed in, and
+`checkpoint_dir` points to a directory containing valid checkpoint
+files, then it will try to recover the model from checkpoint. If
+no checkpoint files are available, and `wait_for_checkpoint` is
+`True`, then the process would check every `recovery_wait_secs`,
+up to `max_wait_secs`, for recovery to succeed.
+
+If the model cannot be recovered successfully then it is initialized by
+either running the provided `init_op`, or calling the provided `init_fn`.
+It is an error if the model cannot be recovered and neither an `init_op`
+or an `init_fn` are passed.
+
+This is a convenient function for the following, with a few error checks
+added:
+
+```python
+sess, initialized = self.recover_session(master)
+if not initialized:
+  if init_op:
+    sess.run(init_op, feed_dict=init_feed_dict)
+  if init_fn;
+    init_fn(sess)
+return sess
+```
+
+##### Args:
+
+
+*  <b>`master`</b>: `String` representation of the TensorFlow master to use.
+*  <b>`init_op`</b>: Optional `Operation` used to initialize the model.
+*  <b>`saver`</b>: A `Saver` object used to restore a model.
+*  <b>`checkpoint_dir`</b>: Path to the checkpoint files.
+*  <b>`wait_for_checkpoint`</b>: Whether to wait for checkpoint to become available.
+*  <b>`max_wait_secs`</b>: Maximum time to wait for checkpoints to become available.
+*  <b>`config`</b>: Optional `ConfigProto` proto used to configure the session.
+*  <b>`init_feed_dict`</b>: Optional dictionary that maps `Tensor` objects to feed
+    values.  This feed dictionary is passed to the session `run()` call when
+    running the init op.
+*  <b>`init_fn`</b>: Optional callable used to initialize the model. Called after the
+    optional `init_op` is called.  The callable must accept one argument,
+    the session being initialized.
+
+##### Returns:
+
+  A `Session` object that can be used to drive the model.
+
+##### Raises:
+
+
+*  <b>`RuntimeError`</b>: If the model cannot be initialized or recovered.
+
+
+- - -
+
+#### `tf.train.SessionManager.recover_session(master, saver=None, checkpoint_dir=None, wait_for_checkpoint=False, max_wait_secs=7200, config=None)` {#SessionManager.recover_session}
+
+Creates a `Session`, recovering if possible.
+
+Creates a new session on 'master'.  If the session is not initialized
+and can be recovered from a checkpoint, recover it.
+
+##### Args:
+
+
+*  <b>`master`</b>: `String` representation of the TensorFlow master to use.
+*  <b>`saver`</b>: A `Saver` object used to restore a model.
+*  <b>`checkpoint_dir`</b>: Path to the checkpoint files.
+*  <b>`wait_for_checkpoint`</b>: Whether to wait for checkpoint to become available.
+*  <b>`max_wait_secs`</b>: Maximum time to wait for checkpoints to become available.
+*  <b>`config`</b>: Optional `ConfigProto` proto used to configure the session.
+
+##### Returns:
+
+  A pair (sess, initialized) where 'initialized' is `True` if
+  the session could be recovered, `False` otherwise.
+
+
+- - -
+
+#### `tf.train.SessionManager.wait_for_session(master, config=None, max_wait_secs=inf)` {#SessionManager.wait_for_session}
+
+Creates a new `Session` and waits for model to be ready.
+
+Creates a new `Session` on 'master'.  Waits for the model to be
+initialized or recovered from a checkpoint.  It's expected that
+another thread or process will make the model ready, and that this
+is intended to be used by threads/processes that participate in a
+distributed training configuration where a different thread/process
+is responsible for initializing or recovering the model being trained.
+
+NB: The amount of time this method waits for the session is bounded
+by max_wait_secs. By default, this function will wait indefinitely.
+
+##### Args:
+
+
+*  <b>`master`</b>: `String` representation of the TensorFlow master to use.
+*  <b>`config`</b>: Optional ConfigProto proto used to configure the session.
+*  <b>`max_wait_secs`</b>: Maximum time to wait for the session to become available.
+
+##### Returns:
+
+  A `Session`. May be None if the operation exceeds the timeout
+  specified by config.operation_timeout_in_ms.
+
+##### Raises:
+
+  tf.DeadlineExceededError: if the session is not available after
+    max_wait_secs.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.add_queue_runner.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.add_queue_runner.md
deleted file mode 100644
index f5b9549ad8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.add_queue_runner.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.train.add_queue_runner(qr, collection='queue_runners')` {#add_queue_runner}
-
-Adds a `QueueRunner` to a collection in the graph.
-
-When building a complex model that uses many queues it is often difficult to
-gather all the queue runners that need to be run.  This convenience function
-allows you to add a queue runner to a well known collection in the graph.
-
-The companion method `start_queue_runners()` can be used to start threads for
-all the collected queue runners.
-
-##### Args:
-
-
-*  <b>`qr`</b>: A `QueueRunner`.
-*  <b>`collection`</b>: A `GraphKey` specifying the graph collection to add
-    the queue runner to.  Defaults to `GraphKeys.QUEUE_RUNNERS`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.batch_join.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.batch_join.md
new file mode 100644
index 0000000000..f6985b0a44
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.batch_join.md
@@ -0,0 +1,79 @@
+### `tf.train.batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, shared_name=None, name=None)` {#batch_join}
+
+Runs a list of tensors to fill a queue to create batches of examples.
+
+The `tensors_list` argument is a list of tuples of tensors, or a list of
+dictionaries of tensors.  Each element in the list is treated similarily
+to the `tensors` argument of `tf.train.batch()`.
+
+Enqueues a different list of tensors in different threads.
+Implemented using a queue -- a `QueueRunner` for the queue
+is added to the current `Graph`'s `QUEUE_RUNNER` collection.
+
+`len(tensors_list)` threads will be started,
+with thread `i` enqueuing the tensors from
+`tensors_list[i]`. `tensors_list[i1][j]` must match
+`tensors_list[i2][j]` in type and shape, except in the first
+dimension if `enqueue_many` is true.
+
+If `enqueue_many` is `False`, each `tensors_list[i]` is assumed
+to represent a single example. An input tensor `x` will be output as a
+tensor with shape `[batch_size] + x.shape`.
+
+If `enqueue_many` is `True`, `tensors_list[i]` is assumed to
+represent a batch of examples, where the first dimension is indexed
+by example, and all members of `tensors_list[i]` should have the
+same size in the first dimension.  The slices of any input tensor
+`x` are treated as examples, and the output tensors will have shape
+`[batch_size] + x.shape[1:]`.
+
+The `capacity` argument controls the how long the prefetching is allowed to
+grow the queues.
+
+The returned operation is a dequeue operation and will throw
+`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
+operation is feeding another input queue, its queue runner will catch
+this exception, however, if this operation is used in your main thread
+you are responsible for catching this yourself.
+
+*N.B.:* If `dynamic_pad` is `False`, you must ensure that either
+(i) the `shapes` argument is passed, or (ii) all of the tensors in
+`tensors_list` must have fully-defined shapes. `ValueError` will be
+raised if neither of these conditions holds.
+
+If `dynamic_pad` is `True`, it is sufficient that the *rank* of the
+tensors is known, but individual dimensions may have value `None`.
+In this case, for each enqueue the dimensions with value `None`
+may have a variable length; upon dequeue, the output tensors will be padded
+on the right to the maximum shape of the tensors in the current minibatch.
+For numbers, this padding takes value 0.  For strings, this padding is
+the empty string.  See `PaddingFIFOQueue` for more info.
+
+##### Args:
+
+
+*  <b>`tensors_list`</b>: A list of tuples or dictionaries of tensors to enqueue.
+*  <b>`batch_size`</b>: An integer. The new batch size pulled from the queue.
+*  <b>`capacity`</b>: An integer. The maximum number of elements in the queue.
+*  <b>`enqueue_many`</b>: Whether each tensor in `tensor_list_list` is a single
+    example.
+*  <b>`shapes`</b>: (Optional) The shapes for each example.  Defaults to the
+    inferred shapes for `tensor_list_list[i]`.
+*  <b>`dynamic_pad`</b>: Boolean.  Allow variable dimensions in input shapes.
+    The given dimensions are padded upon dequeue so that tensors within a
+    batch have the same shapes.
+*  <b>`shared_name`</b>: (Optional) If set, this queue will be shared under the given
+    name across multiple sessions.
+*  <b>`name`</b>: (Optional) A name for the operations.
+
+##### Returns:
+
+  A list or dictionary of tensors with the same number and types as
+  `tensors_list[i]`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be
+    inferred from the elements of `tensor_list_list`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.import_meta_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.import_meta_graph.md
new file mode 100644
index 0000000000..21b3465076
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.import_meta_graph.md
@@ -0,0 +1,65 @@
+### `tf.train.import_meta_graph(meta_graph_or_file)` {#import_meta_graph}
+
+Recreates a Graph saved in a `MetaGraphDef` proto.
+
+This function takes a `MetaGraphDef` protocol buffer as input. If
+the argument is a file containing a `MetaGraphDef` protocol buffer ,
+it constructs a protocol buffer from the file content. The function
+then adds all the nodes from the `graph_def` field to the
+current graph, recreates all the collections, and returns a saver
+constructed from the `saver_def` field.
+
+In combination with `export_meta_graph()`, this function can be used to
+
+* Serialize a graph along with other Python objects such as `QueueRunner`,
+  `Variable` into a `MetaGraphDef`.
+
+* Restart training from a saved graph and checkpoints.
+
+* Run inference from a saved graph and checkpoints.
+
+```Python
+...
+# Create a saver.
+saver = tf.train.Saver(...variables...)
+# Remember the training_op we want to run by adding it to a collection.
+tf.add_to_collection('train_op', train_op)
+sess = tf.Session()
+for step in xrange(1000000):
+    sess.run(train_op)
+    if step % 1000 == 0:
+        # Saves checkpoint, which by default also exports a meta_graph
+        # named 'my-model-global_step.meta'.
+        saver.save(sess, 'my-model', global_step=step)
+```
+
+Later we can continue training from this saved `meta_graph` without building
+the model from scratch.
+
+```Python
+with tf.Session() as sess:
+  new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
+  new_saver.restore(sess, 'my-save-dir/my-model-10000')
+  # tf.get_collection() returns a list. In this example we only want the
+  # first one.
+  train_op = tf.get_collection('train_op')[0]
+  for step in xrange(1000000):
+    sess.run(train_op)
+```
+
+NOTE: Restarting training from saved `meta_graph` only works if the
+device assignments have not changed.
+
+##### Args:
+
+
+*  <b>`meta_graph_or_file`</b>: `MetaGraphDef` protocol buffer or filename (including
+    the path) containing a `MetaGraphDef`.
+
+##### Returns:
+
+  A saver constructed from `saver_def` in `MetaGraphDef` or None.
+
+  A None value is returned if no variables exist in the `MetaGraphDef`
+  (i.e., there are no variables to restore).
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.shuffle_batch_join.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.shuffle_batch_join.md
new file mode 100644
index 0000000000..ab9e1be4a3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.shuffle_batch_join.md
@@ -0,0 +1,68 @@
+### `tf.train.shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, shared_name=None, name=None)` {#shuffle_batch_join}
+
+Create batches by randomly shuffling tensors.
+
+The `tensors_list` argument is a list of tuples of tensors, or a list of
+dictionaries of tensors.  Each element in the list is treated similarily
+to the `tensors` argument of `tf.train.shuffle_batch()`.
+
+This version enqueues a different list of tensors in different threads.
+It adds the following to the current `Graph`:
+
+* A shuffling queue into which tensors from `tensors_list` are enqueued.
+* A `dequeue_many` operation to create batches from the queue.
+* A `QueueRunner` to `QUEUE_RUNNER` collection, to enqueue the tensors
+  from `tensors_list`.
+
+`len(tensors_list)` threads will be started, with thread `i` enqueuing
+the tensors from `tensors_list[i]`. `tensors_list[i1][j]` must match
+`tensors_list[i2][j]` in type and shape, except in the first dimension if
+`enqueue_many` is true.
+
+If `enqueue_many` is `False`, each `tensors_list[i]` is assumed
+to represent a single example.  An input tensor with shape `[x, y, z]`
+will be output as a tensor with shape `[batch_size, x, y, z]`.
+
+If `enqueue_many` is `True`, `tensors_list[i]` is assumed to
+represent a batch of examples, where the first dimension is indexed
+by example, and all members of `tensors_list[i]` should have the
+same size in the first dimension.  If an input tensor has shape `[*, x,
+y, z]`, the output will have shape `[batch_size, x, y, z]`.
+
+The `capacity` argument controls the how long the prefetching is allowed to
+grow the queues.
+
+The returned operation is a dequeue operation and will throw
+`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
+operation is feeding another input queue, its queue runner will catch
+this exception, however, if this operation is used in your main thread
+you are responsible for catching this yourself.
+
+##### Args:
+
+
+*  <b>`tensors_list`</b>: A list of tuples or dictionaries of tensors to enqueue.
+*  <b>`batch_size`</b>: An integer. The new batch size pulled from the queue.
+*  <b>`capacity`</b>: An integer. The maximum number of elements in the queue.
+*  <b>`min_after_dequeue`</b>: Minimum number elements in the queue after a
+    dequeue, used to ensure a level of mixing of elements.
+*  <b>`seed`</b>: Seed for the random shuffling within the queue.
+*  <b>`enqueue_many`</b>: Whether each tensor in `tensor_list_list` is a single
+    example.
+*  <b>`shapes`</b>: (Optional) The shapes for each example.  Defaults to the
+    inferred shapes for `tensors_list[i]`.
+*  <b>`shared_name`</b>: (optional). If set, this queue will be shared under the given
+    name across multiple sessions.
+*  <b>`name`</b>: (Optional) A name for the operations.
+
+##### Returns:
+
+  A list or dictionary of tensors with the same number and types as
+  `tensors_list[i]`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be
+    inferred from the elements of `tensors_list`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.truediv.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.truediv.md
deleted file mode 100644
index 0ccb1b2217..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.truediv.md
+++ /dev/null
@@ -1,31 +0,0 @@
-### `tf.truediv(x, y, name=None)` {#truediv}
-
-Divides x / y elementwise, always producing floating point results.
-
-The same as `tf.div` for floating point arguments, but casts integer arguments
-to floating point before dividing so that the result is always floating point.
-This op is generated by normal `x / y` division in Python 3 and in Python 2.7
-with `from __future__ import division`.  If you want integer division that
-rounds down, use `x // y` or `tf.floordiv`.
-
-`x` and `y` must have the same numeric type.  If the inputs are floating
-point, the output will have the same type.  If the inputs are integral, the
-inputs are cast to `float32` for `int8` and `int16` and `float64` for `int32`
-and `int64` (matching the behavior of Numpy).
-
-##### Args:
-
-
-*  <b>`x`</b>: `Tensor` numerator of numeric type.
-*  <b>`y`</b>: `Tensor` denominator of numeric type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  `x / y` evaluated in floating point.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `x` and `y` have different dtypes.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.truncated_normal_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.truncated_normal_initializer.md
new file mode 100644
index 0000000000..0a335333e2
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.truncated_normal_initializer.md
@@ -0,0 +1,31 @@
+### `tf.truncated_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)` {#truncated_normal_initializer}
+
+Returns an initializer that generates a truncated normal distribution.
+
+These values are similar to values from a `random_normal_initializer`
+except that values more than two standard deviations from the mean
+are discarded and re-drawn. This is the recommended initializer for
+neural network weights and filters.
+
+##### Args:
+
+
+*  <b>`mean`</b>: a python scalar or a scalar tensor. Mean of the random values
+    to generate.
+*  <b>`stddev`</b>: a python scalar or a scalar tensor. Standard deviation of the
+    random values to generate.
+*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`dtype`</b>: The data type. Only floating point types are supported.
+
+##### Returns:
+
+  An initializer that generates tensors with a truncated normal
+  distribution.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.unique.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.unique.md
new file mode 100644
index 0000000000..0929f57b0f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.unique.md
@@ -0,0 +1,33 @@
+### `tf.unique(x, name=None)` {#unique}
+
+Finds unique elements in a 1-D tensor.
+
+This operation returns a tensor `y` containing all of the unique elements of `x`
+sorted in the same order that they occur in `x`. This operation also returns a
+tensor `idx` the same size as `x` that contains the index of each value of `x`
+in the unique output `y`. In other words:
+
+`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
+
+For example:
+
+```prettyprint
+# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
+y, idx = unique(x)
+y ==> [1, 2, 4, 7, 8]
+idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. 1-D.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of `Tensor` objects (y, idx).
+
+*  <b>`y`</b>: A `Tensor`. Has the same type as `x`. 1-D.
+*  <b>`idx`</b>: A `Tensor` of type `int32`. 1-D.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.unpack.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.unpack.md
deleted file mode 100644
index cc4884c720..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.unpack.md
+++ /dev/null
@@ -1,32 +0,0 @@
-### `tf.unpack(value, num=None, name='unpack')` {#unpack}
-
-Unpacks the outer dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
-
-Unpacks `num` tensors from `value` along the first dimension.
-If `num` is not specified (the default), it is inferred from `value`'s shape.
-If `value.shape[0]` is not known, `ValueError` is raised.
-
-The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in
-`output` has shape `value.shape[1:]`.
-
-This is the opposite of pack.  The numpy equivalent is
-
-    tf.unpack(x, n) = list(x)
-
-##### Args:
-
-
-*  <b>`value`</b>: A rank `R > 0` `Tensor` to be unpacked.
-*  <b>`num`</b>: An `int`. The first dimension of value. Automatically inferred if
-    `None` (the default).
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The list of `Tensor` objects unpacked from `value`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `num` is unspecified and cannot be inferred.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.variable_op_scope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.variable_op_scope.md
deleted file mode 100644
index e3ab6e5d2e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.variable_op_scope.md
+++ /dev/null
@@ -1,56 +0,0 @@
-### `tf.variable_op_scope(values, name_or_scope, default_name=None, initializer=None, regularizer=None, caching_device=None, partitioner=None, reuse=None)` {#variable_op_scope}
-
-Returns a context manager for defining an op that creates variables.
-
-This context manager validates that the given `values` are from the
-same graph, ensures that graph is the default graph, and pushes a
-name scope and a variable scope.
-
-If `name_or_scope` is not None, it is used as is in the variable scope. If
-`scope` is None, then `default_name` is used.  In that case, if the same name
-has been previously used in the same scope, it will made unique be appending
-`_N` to it.
-
-This is intended to be used when defining generic ops and so reuse is always
-inherited.
-
-For example, to define a new Python op called `my_op_with_vars`:
-
-```python
-def my_op_with_vars(a, b, scope=None):
-  with tf.variable_op_scope([a, b], scope, "MyOp") as scope:
-    a = tf.convert_to_tensor(a, name="a")
-    b = tf.convert_to_tensor(b, name="b")
-    c = tf.get_variable('c')
-    # Define some computation that uses `a`, `b`, and `c`.
-    return foo_op(..., name=scope)
-```
-
-##### Args:
-
-
-*  <b>`values`</b>: The list of `Tensor` arguments that are passed to the op function.
-*  <b>`name_or_scope`</b>: The name argument that is passed to the op function,
-    this name_or_scope is not uniquified in the variable scope.
-*  <b>`default_name`</b>: The default name to use if the `name_or_scope` argument is
-    `None`, this name will be uniquified. If name_or_scope is provided it
-    won't be used and therefore it is not required and can be None.
-*  <b>`initializer`</b>: The default initializer to pass to variable scope.
-*  <b>`regularizer`</b>: The default regularizer for variables within this scope.
-*  <b>`caching_device`</b>: The default caching device for variables within this scope.
-*  <b>`partitioner`</b>: The default partitioner for variables within this scope.
-*  <b>`reuse`</b>: `True` or `None`; if `True`, we go into reuse mode for this scope as
-    well as all sub-scopes; if `None`, we just inherit the parent scope reuse.
-
-
-##### Returns:
-
-  A context manager for use in defining a Python op.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: when trying to reuse within a create scope, or create within
-    a reuse scope, or if reuse is not `None` or `True`.
-*  <b>`TypeError`</b>: when the types of some arguments are not appropriate.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.variable_scope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.variable_scope.md
new file mode 100644
index 0000000000..86d4684b72
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.variable_scope.md
@@ -0,0 +1,82 @@
+### `tf.variable_scope(name_or_scope, reuse=None, initializer=None, regularizer=None, caching_device=None, partitioner=None)` {#variable_scope}
+
+Returns a context for variable scope.
+
+Variable scope allows to create new variables and to share already created
+ones while providing checks to not create or share by accident. For details,
+see the [Variable Scope How To](../../how_tos/variable_scope/index.md),
+here we present only a few basic examples.
+
+Simple example of how to create a new variable:
+
+```python
+with tf.variable_scope("foo"):
+    with tf.variable_scope("bar"):
+        v = tf.get_variable("v", [1])
+        assert v.name == "foo/bar/v:0"
+```
+
+Basic example of sharing a variable:
+
+```python
+with tf.variable_scope("foo"):
+    v = tf.get_variable("v", [1])
+with tf.variable_scope("foo", reuse=True):
+    v1 = tf.get_variable("v", [1])
+assert v1 == v
+```
+
+Sharing a variable by capturing a scope and setting reuse:
+
+```python
+with tf.variable_scope("foo") as scope:
+    v = tf.get_variable("v", [1])
+    scope.reuse_variables()
+    v1 = tf.get_variable("v", [1])
+assert v1 == v
+```
+
+To prevent accidental sharing of variables, we raise an exception when
+getting an existing variable in a non-reusing scope.
+
+```python
+with tf.variable_scope("foo"):
+    v = tf.get_variable("v", [1])
+    v1 = tf.get_variable("v", [1])
+    #  Raises ValueError("... v already exists ...").
+```
+
+Similarly, we raise an exception when trying to get a variable that
+does not exist in reuse mode.
+
+```python
+with tf.variable_scope("foo", reuse=True):
+    v = tf.get_variable("v", [1])
+    #  Raises ValueError("... v does not exists ...").
+```
+
+Note that the `reuse` flag is inherited: if we open a reusing scope,
+then all its sub-scopes become reusing as well.
+
+##### Args:
+
+
+*  <b>`name_or_scope`</b>: `string` or `VariableScope`: the scope to open.
+*  <b>`reuse`</b>: `True` or `None`; if `True`, we go into reuse mode for this scope as
+    well as all sub-scopes; if `None`, we just inherit the parent scope reuse.
+*  <b>`initializer`</b>: default initializer for variables within this scope.
+*  <b>`regularizer`</b>: default regularizer for variables within this scope.
+*  <b>`caching_device`</b>: default caching device for variables within this scope.
+*  <b>`partitioner`</b>: default partitioner for variables within this scope.
+
+##### Returns:
+
+  A scope that can be to captured and reused.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: when trying to reuse within a create scope, or create within
+    a reuse scope, or if reuse is not `None` or `True`.
+*  <b>`TypeError`</b>: when the types of some arguments are not appropriate.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.zeros_like.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.zeros_like.md
new file mode 100644
index 0000000000..9017e14287
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.zeros_like.md
@@ -0,0 +1,28 @@
+### `tf.zeros_like(tensor, dtype=None, name=None)` {#zeros_like}
+
+Creates a tensor with all elements set to zero.
+
+Given a single tensor (`tensor`), this operation returns a tensor of the
+same type and shape as `tensor` with all elements set to zero. Optionally,
+you can use `dtype` to specify a new type for the returned tensor.
+
+For example:
+
+```python
+# 'tensor' is [[1, 2, 3], [4, 5, 6]]
+tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]
+```
+
+##### Args:
+
+
+*  <b>`tensor`</b>: A `Tensor`.
+*  <b>`dtype`</b>: A type for the returned `Tensor`. Must be `float32`, `float64`,
+  `int8`, `int16`, `int32`, `int64`, `uint8`, or `complex64`.
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` with all elements set to zero.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.zeta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.zeta.md
deleted file mode 100644
index ed66237d38..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.zeta.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.zeta(x, q, name=None)` {#zeta}
-
-Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
-
-The Hurwitz zeta function is defined as:
-
-```
-\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-*  <b>`q`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.Operation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.Operation.md
new file mode 100644
index 0000000000..a9e21fb29e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.Operation.md
@@ -0,0 +1,225 @@
+Represents a graph node that performs computation on tensors.
+
+An `Operation` is a node in a TensorFlow `Graph` that takes zero or
+more `Tensor` objects as input, and produces zero or more `Tensor`
+objects as output. Objects of type `Operation` are created by
+calling a Python op constructor (such as
+[`tf.matmul()`](../../api_docs/python/math_ops.md#matmul))
+or [`Graph.create_op()`](../../api_docs/python/framework.md#Graph.create_op).
+
+For example `c = tf.matmul(a, b)` creates an `Operation` of type
+"MatMul" that takes tensors `a` and `b` as input, and produces `c`
+as output.
+
+After the graph has been launched in a session, an `Operation` can
+be executed by passing it to
+[`Session.run()`](../../api_docs/python/client.md#Session.run).
+`op.run()` is a shortcut for calling `tf.get_default_session().run(op)`.
+
+- - -
+
+#### `tf.Operation.name` {#Operation.name}
+
+The full name of this operation.
+
+
+- - -
+
+#### `tf.Operation.type` {#Operation.type}
+
+The type of the op (e.g. `"MatMul"`).
+
+
+- - -
+
+#### `tf.Operation.inputs` {#Operation.inputs}
+
+The list of `Tensor` objects representing the data inputs of this op.
+
+
+- - -
+
+#### `tf.Operation.control_inputs` {#Operation.control_inputs}
+
+The `Operation` objects on which this op has a control dependency.
+
+Before this op is executed, TensorFlow will ensure that the
+operations in `self.control_inputs` have finished executing. This
+mechanism can be used to run ops sequentially for performance
+reasons, or to ensure that the side effects of an op are observed
+in the correct order.
+
+##### Returns:
+
+  A list of `Operation` objects.
+
+
+- - -
+
+#### `tf.Operation.outputs` {#Operation.outputs}
+
+The list of `Tensor` objects representing the outputs of this op.
+
+
+- - -
+
+#### `tf.Operation.device` {#Operation.device}
+
+The name of the device to which this op has been assigned, if any.
+
+##### Returns:
+
+  The string name of the device to which this op has been
+  assigned, or an empty string if it has not been assigned to a
+  device.
+
+
+- - -
+
+#### `tf.Operation.graph` {#Operation.graph}
+
+The `Graph` that contains this operation.
+
+
+
+- - -
+
+#### `tf.Operation.run(feed_dict=None, session=None)` {#Operation.run}
+
+Runs this operation in a `Session`.
+
+Calling this method will execute all preceding operations that
+produce the inputs needed for this operation.
+
+*N.B.* Before invoking `Operation.run()`, its graph must have been
+launched in a session, and either a default session must be
+available, or `session` must be specified explicitly.
+
+##### Args:
+
+
+*  <b>`feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
+    See [`Session.run()`](../../api_docs/python/client.md#Session.run)
+    for a description of the valid feed values.
+*  <b>`session`</b>: (Optional.) The `Session` to be used to run to this operation. If
+    none, the default session will be used.
+
+
+
+- - -
+
+#### `tf.Operation.get_attr(name)` {#Operation.get_attr}
+
+Returns the value of the attr of this op with the given `name`.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name of the attr to fetch.
+
+##### Returns:
+
+  The value of the attr, as a Python object.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If this op does not have an attr with the given `name`.
+
+
+- - -
+
+#### `tf.Operation.traceback` {#Operation.traceback}
+
+Returns the call stack from when this operation was constructed.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.Operation.__init__(node_def, g, inputs=None, output_types=None, control_inputs=None, input_types=None, original_op=None, op_def=None)` {#Operation.__init__}
+
+Creates an `Operation`.
+
+NOTE: This constructor validates the name of the `Operation` (passed
+as `node_def.name`). Valid `Operation` names match the following
+regular expression:
+
+    [A-Za-z0-9.][A-Za-z0-9_.\-/]*
+
+##### Args:
+
+
+*  <b>`node_def`</b>: `graph_pb2.NodeDef`.  `NodeDef` for the `Operation`.
+    Used for attributes of `graph_pb2.NodeDef`, typically `name`,
+    `op`, and `device`.  The `input` attribute is irrelevant here
+    as it will be computed when generating the model.
+*  <b>`g`</b>: `Graph`. The parent graph.
+*  <b>`inputs`</b>: list of `Tensor` objects. The inputs to this `Operation`.
+*  <b>`output_types`</b>: list of `DType` objects.  List of the types of the
+    `Tensors` computed by this operation.  The length of this list indicates
+    the number of output endpoints of the `Operation`.
+*  <b>`control_inputs`</b>: list of operations or tensors from which to have a
+    control dependency.
+*  <b>`input_types`</b>: List of `DType` objects representing the
+    types of the tensors accepted by the `Operation`.  By default
+    uses `[x.dtype.base_dtype for x in inputs]`.  Operations that expect
+    reference-typed inputs must specify these explicitly.
+*  <b>`original_op`</b>: Optional. Used to associate the new `Operation` with an
+    existing `Operation` (for example, a replica with the op that was
+    replicated).
+*  <b>`op_def`</b>: Optional. The `op_def_pb2.OpDef` proto that describes the
+    op type that this `Operation` represents.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if control inputs are not Operations or Tensors,
+    or if `node_def` is not a `NodeDef`,
+    or if `g` is not a `Graph`,
+    or if `inputs` are not tensors,
+    or if `inputs` and `input_types` are incompatible.
+*  <b>`ValueError`</b>: if the `node_def` name is not valid.
+
+
+- - -
+
+#### `tf.Operation.colocation_groups()` {#Operation.colocation_groups}
+
+Returns the list of colocation groups of the op.
+
+
+- - -
+
+#### `tf.Operation.node_def` {#Operation.node_def}
+
+Returns a serialized `NodeDef` representation of this operation.
+
+##### Returns:
+
+  A
+  [`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
+  protocol buffer.
+
+
+- - -
+
+#### `tf.Operation.op_def` {#Operation.op_def}
+
+Returns the `OpDef` proto that represents the type of this op.
+
+##### Returns:
+
+  An
+  [`OpDef`](https://www.tensorflow.org/code/tensorflow/core/framework/op_def.proto)
+  protocol buffer.
+
+
+- - -
+
+#### `tf.Operation.values()` {#Operation.values}
+
+DEPRECATED: Use outputs.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.QueueBase.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.QueueBase.md
deleted file mode 100644
index 35373f6edc..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.QueueBase.md
+++ /dev/null
@@ -1,268 +0,0 @@
-Base class for queue implementations.
-
-A queue is a TensorFlow data structure that stores tensors across
-multiple steps, and exposes operations that enqueue and dequeue
-tensors.
-
-Each queue element is a tuple of one or more tensors, where each
-tuple component has a static dtype, and may have a static shape. The
-queue implementations support versions of enqueue and dequeue that
-handle single elements, versions that support enqueuing and
-dequeuing a batch of elements at once.
-
-See [`tf.FIFOQueue`](#FIFOQueue) and
-[`tf.RandomShuffleQueue`](#RandomShuffleQueue) for concrete
-implementations of this class, and instructions on how to create
-them.
-
-- - -
-
-#### `tf.QueueBase.enqueue(vals, name=None)` {#QueueBase.enqueue}
-
-Enqueues one element to this queue.
-
-If the queue is full when this operation executes, it will block
-until the element has been enqueued.
-
-##### Args:
-
-
-*  <b>`vals`</b>: A tensor, a list or tuple of tensors, or a dictionary containing
-    the values to enqueue.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The operation that enqueues a new tuple of tensors to the queue.
-
-
-- - -
-
-#### `tf.QueueBase.enqueue_many(vals, name=None)` {#QueueBase.enqueue_many}
-
-Enqueues zero or more elements to this queue.
-
-This operation slices each component tensor along the 0th dimension to
-make multiple queue elements. All of the tensors in `vals` must have the
-same size in the 0th dimension.
-
-If the queue is full when this operation executes, it will block
-until all of the elements have been enqueued.
-
-##### Args:
-
-
-*  <b>`vals`</b>: A tensor, a list or tuple of tensors, or a dictionary
-    from which the queue elements are taken.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The operation that enqueues a batch of tuples of tensors to the queue.
-
-
-
-- - -
-
-#### `tf.QueueBase.dequeue(name=None)` {#QueueBase.dequeue}
-
-Dequeues one element from this queue.
-
-If the queue is empty when this operation executes, it will block
-until there is an element to dequeue.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The tuple of tensors that was dequeued.
-
-
-- - -
-
-#### `tf.QueueBase.dequeue_many(n, name=None)` {#QueueBase.dequeue_many}
-
-Dequeues and concatenates `n` elements from this queue.
-
-This operation concatenates queue-element component tensors along
-the 0th dimension to make a single component tensor.  All of the
-components in the dequeued tuple will have size `n` in the 0th dimension.
-
-If the queue is closed and there are less than `n` elements left, then an
-`OutOfRange` exception is raised.
-
-##### Args:
-
-
-*  <b>`n`</b>: A scalar `Tensor` containing the number of elements to dequeue.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The tuple of concatenated tensors that was dequeued.
-
-
-
-- - -
-
-#### `tf.QueueBase.size(name=None)` {#QueueBase.size}
-
-Compute the number of elements in this queue.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A scalar tensor containing the number of elements in this queue.
-
-
-
-- - -
-
-#### `tf.QueueBase.close(cancel_pending_enqueues=False, name=None)` {#QueueBase.close}
-
-Closes this queue.
-
-This operation signals that no more elements will be enqueued in
-the given queue. Subsequent `enqueue` and `enqueue_many`
-operations will fail. Subsequent `dequeue` and `dequeue_many`
-operations will continue to succeed if sufficient elements remain
-in the queue. Subsequent `dequeue` and `dequeue_many` operations
-that would block will fail immediately.
-
-If `cancel_pending_enqueues` is `True`, all pending requests will also
-be cancelled.
-
-##### Args:
-
-
-*  <b>`cancel_pending_enqueues`</b>: (Optional.) A boolean, defaulting to
-    `False` (described above).
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The operation that closes the queue.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.QueueBase.__init__(dtypes, shapes, names, queue_ref)` {#QueueBase.__init__}
-
-Constructs a queue object from a queue reference.
-
-The two optional lists, `shapes` and `names`, must be of the same length
-as `dtypes` if provided.  The values at a given index `i` indicate the
-shape and name to use for the corresponding queue component in `dtypes`.
-
-##### Args:
-
-
-*  <b>`dtypes`</b>: A list of types.  The length of dtypes must equal the number
-    of tensors in each element.
-*  <b>`shapes`</b>: Constraints on the shapes of tensors in an element:
-    A list of shape tuples or None. This list is the same length
-    as dtypes.  If the shape of any tensors in the element are constrained,
-    all must be; shapes can be None if the shapes should not be constrained.
-*  <b>`names`</b>: Optional list of names.  If provided, the `enqueue()` and
-    `dequeue()` methods will use dictionaries with these names as keys.
-    Must be None or a list or tuple of the same length as `dtypes`.
-*  <b>`queue_ref`</b>: The queue reference, i.e. the output of the queue op.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If one of the arguments is invalid.
-
-
-- - -
-
-#### `tf.QueueBase.dequeue_up_to(n, name=None)` {#QueueBase.dequeue_up_to}
-
-Dequeues and concatenates `n` elements from this queue.
-
-**Note** This operation is not supported by all queues.  If a queue does not
-support DequeueUpTo, then an Unimplemented exception is raised.
-
-This operation concatenates queue-element component tensors along the
-0th dimension to make a single component tensor.  All of the components
-in the dequeued tuple will have size `n` in the 0th dimension.
-
-If the queue is closed and there are more than `0` but less than `n`
-elements remaining, then instead of raising an `OutOfRange` exception like
-`dequeue_many`, the remaining elements are returned immediately.
-If the queue is closed and there are `0` elements left in the queue, then
-an `OutOfRange` exception is raised just like in `dequeue_many`.
-Otherwise the behavior is identical to `dequeue_many`:
-
-##### Args:
-
-
-*  <b>`n`</b>: A scalar `Tensor` containing the number of elements to dequeue.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The tuple of concatenated tensors that was dequeued.
-
-
-- - -
-
-#### `tf.QueueBase.dtypes` {#QueueBase.dtypes}
-
-The list of dtypes for each component of a queue element.
-
-
-- - -
-
-#### `tf.QueueBase.from_list(index, queues)` {#QueueBase.from_list}
-
-Create a queue using the queue reference from `queues[index]`.
-
-##### Args:
-
-
-*  <b>`index`</b>: An integer scalar tensor that determines the input that gets
-    selected.
-*  <b>`queues`</b>: A list of `QueueBase` objects.
-
-##### Returns:
-
-  A `QueueBase` object.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: When `queues` is not a list of `QueueBase` objects,
-    or when the data types of `queues` are not all the same.
-
-
-- - -
-
-#### `tf.QueueBase.name` {#QueueBase.name}
-
-The name of the underlying queue.
-
-
-- - -
-
-#### `tf.QueueBase.names` {#QueueBase.names}
-
-The list of names for each component of a queue element.
-
-
-- - -
-
-#### `tf.QueueBase.queue_ref` {#QueueBase.queue_ref}
-
-The underlying queue reference.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.ReaderBase.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.ReaderBase.md
new file mode 100644
index 0000000000..bc9f62de5a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.ReaderBase.md
@@ -0,0 +1,156 @@
+Base class for different Reader types, that produce a record every step.
+
+Conceptually, Readers convert string 'work units' into records (key,
+value pairs).  Typically the 'work units' are filenames and the
+records are extracted from the contents of those files.  We want a
+single record produced per step, but a work unit can correspond to
+many records.
+
+Therefore we introduce some decoupling using a queue.  The queue
+contains the work units and the Reader dequeues from the queue when
+it is asked to produce a record (via Read()) but it has finished the
+last work unit.
+- - -
+
+#### `tf.ReaderBase.__init__(reader_ref, supports_serialize=False)` {#ReaderBase.__init__}
+
+Creates a new ReaderBase.
+
+##### Args:
+
+
+*  <b>`reader_ref`</b>: The operation that implements the reader.
+*  <b>`supports_serialize`</b>: True if the reader implementation can
+    serialize its state.
+
+
+- - -
+
+#### `tf.ReaderBase.num_records_produced(name=None)` {#ReaderBase.num_records_produced}
+
+Returns the number of records this reader has produced.
+
+This is the same as the number of Read executions that have
+succeeded.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.ReaderBase.num_work_units_completed(name=None)` {#ReaderBase.num_work_units_completed}
+
+Returns the number of work units this reader has finished processing.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.ReaderBase.read(queue, name=None)` {#ReaderBase.read}
+
+Returns the next record (key, value pair) produced by a reader.
+
+Will dequeue a work unit from queue if necessary (e.g. when the
+Reader needs to start reading from a new file since it has
+finished with the previous file).
+
+##### Args:
+
+
+*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
+    to a Queue, with string work items.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of Tensors (key, value).
+
+*  <b>`key`</b>: A string scalar Tensor.
+*  <b>`value`</b>: A string scalar Tensor.
+
+
+- - -
+
+#### `tf.ReaderBase.reader_ref` {#ReaderBase.reader_ref}
+
+Op that implements the reader.
+
+
+- - -
+
+#### `tf.ReaderBase.reset(name=None)` {#ReaderBase.reset}
+
+Restore a reader to its initial clean state.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.ReaderBase.restore_state(state, name=None)` {#ReaderBase.restore_state}
+
+Restore a reader to a previously saved state.
+
+Not all Readers support being restored, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`state`</b>: A string Tensor.
+    Result of a SerializeState of a Reader with matching type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.ReaderBase.serialize_state(name=None)` {#ReaderBase.serialize_state}
+
+Produce a string tensor that encodes the state of a reader.
+
+Not all Readers support being serialized, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A string Tensor.
+
+
+- - -
+
+#### `tf.ReaderBase.supports_serialize` {#ReaderBase.supports_serialize}
+
+Whether the Reader implementation can serialize its state.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.Tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.Tensor.md
deleted file mode 100644
index 73af134a7a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.Tensor.md
+++ /dev/null
@@ -1,228 +0,0 @@
-Represents a value produced by an `Operation`.
-
-A `Tensor` is a symbolic handle to one of the outputs of an
-`Operation`. It does not hold the values of that operation's output,
-but instead provides a means of computing those values in a
-TensorFlow [`Session`](../../api_docs/python/client.md#Session).
-
-This class has two primary purposes:
-
-1. A `Tensor` can be passed as an input to another `Operation`.
-   This builds a dataflow connection between operations, which
-   enables TensorFlow to execute an entire `Graph` that represents a
-   large, multi-step computation.
-
-2. After the graph has been launched in a session, the value of the
-   `Tensor` can be computed by passing it to
-   [`Session.run()`](../../api_docs/python/client.md#Session.run).
-   `t.eval()` is a shortcut for calling
-   `tf.get_default_session().run(t)`.
-
-In the following example, `c`, `d`, and `e` are symbolic `Tensor`
-objects, whereas `result` is a numpy array that stores a concrete
-value:
-
-```python
-# Build a dataflow graph.
-c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
-d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
-e = tf.matmul(c, d)
-
-# Construct a `Session` to execute the graph.
-sess = tf.Session()
-
-# Execute the graph and store the value that `e` represents in `result`.
-result = sess.run(e)
-```
-
-- - -
-
-#### `tf.Tensor.dtype` {#Tensor.dtype}
-
-The `DType` of elements in this tensor.
-
-
-- - -
-
-#### `tf.Tensor.name` {#Tensor.name}
-
-The string name of this tensor.
-
-
-- - -
-
-#### `tf.Tensor.value_index` {#Tensor.value_index}
-
-The index of this tensor in the outputs of its `Operation`.
-
-
-- - -
-
-#### `tf.Tensor.graph` {#Tensor.graph}
-
-The `Graph` that contains this tensor.
-
-
-- - -
-
-#### `tf.Tensor.op` {#Tensor.op}
-
-The `Operation` that produces this tensor as an output.
-
-
-- - -
-
-#### `tf.Tensor.consumers()` {#Tensor.consumers}
-
-Returns a list of `Operation`s that consume this tensor.
-
-##### Returns:
-
-  A list of `Operation`s.
-
-
-
-- - -
-
-#### `tf.Tensor.eval(feed_dict=None, session=None)` {#Tensor.eval}
-
-Evaluates this tensor in a `Session`.
-
-Calling this method will execute all preceding operations that
-produce the inputs needed for the operation that produces this
-tensor.
-
-*N.B.* Before invoking `Tensor.eval()`, its graph must have been
-launched in a session, and either a default session must be
-available, or `session` must be specified explicitly.
-
-##### Args:
-
-
-*  <b>`feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
-    See [`Session.run()`](../../api_docs/python/client.md#Session.run) for a
-    description of the valid feed values.
-*  <b>`session`</b>: (Optional.) The `Session` to be used to evaluate this tensor. If
-    none, the default session will be used.
-
-##### Returns:
-
-  A numpy array corresponding to the value of this tensor.
-
-
-
-- - -
-
-#### `tf.Tensor.get_shape()` {#Tensor.get_shape}
-
-Returns the `TensorShape` that represents the shape of this tensor.
-
-The shape is computed using shape inference functions that are
-registered for each `Operation` type using `tf.RegisterShape`.
-See [`TensorShape`](../../api_docs/python/framework.md#TensorShape) for more
-details of what a shape represents.
-
-The inferred shape of a tensor is used to provide shape
-information without having to launch the graph in a session. This
-can be used for debugging, and providing early error messages. For
-example:
-
-```python
-c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
-
-print(c.get_shape())
-==> TensorShape([Dimension(2), Dimension(3)])
-
-d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]])
-
-print(d.get_shape())
-==> TensorShape([Dimension(4), Dimension(2)])
-
-# Raises a ValueError, because `c` and `d` do not have compatible
-# inner dimensions.
-e = tf.matmul(c, d)
-
-f = tf.matmul(c, d, transpose_a=True, transpose_b=True)
-
-print(f.get_shape())
-==> TensorShape([Dimension(3), Dimension(4)])
-```
-
-In some cases, the inferred shape may have unknown dimensions. If
-the caller has additional information about the values of these
-dimensions, `Tensor.set_shape()` can be used to augment the
-inferred shape.
-
-##### Returns:
-
-  A `TensorShape` representing the shape of this tensor.
-
-
-- - -
-
-#### `tf.Tensor.set_shape(shape)` {#Tensor.set_shape}
-
-Updates the shape of this tensor.
-
-This method can be called multiple times, and will merge the given
-`shape` with the current shape of this tensor. It can be used to
-provide additional information about the shape of this tensor that
-cannot be inferred from the graph alone. For example, this can be used
-to provide additional information about the shapes of images:
-
-```python
-_, image_data = tf.TFRecordReader(...).read(...)
-image = tf.image.decode_png(image_data, channels=3)
-
-# The height and width dimensions of `image` are data dependent, and
-# cannot be computed without executing the op.
-print(image.get_shape())
-==> TensorShape([Dimension(None), Dimension(None), Dimension(3)])
-
-# We know that each image in this dataset is 28 x 28 pixels.
-image.set_shape([28, 28, 3])
-print(image.get_shape())
-==> TensorShape([Dimension(28), Dimension(28), Dimension(3)])
-```
-
-##### Args:
-
-
-*  <b>`shape`</b>: A `TensorShape` representing the shape of this tensor.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `shape` is not compatible with the current shape of
-    this tensor.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.Tensor.__init__(op, value_index, dtype)` {#Tensor.__init__}
-
-Creates a new `Tensor`.
-
-##### Args:
-
-
-*  <b>`op`</b>: An `Operation`. `Operation` that computes this tensor.
-*  <b>`value_index`</b>: An `int`. Index of the operation's endpoint that produces
-    this tensor.
-*  <b>`dtype`</b>: A `DType`. Type of elements stored in this tensor.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If the op is not an `Operation`.
-
-
-- - -
-
-#### `tf.Tensor.device` {#Tensor.device}
-
-The name of the device on which this tensor will be produced, or None.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.Variable.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.Variable.md
new file mode 100644
index 0000000000..b300fac583
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.Variable.md
@@ -0,0 +1,460 @@
+See the [Variables How To](../../how_tos/variables/index.md) for a high
+level overview.
+
+A variable maintains state in the graph across calls to `run()`. You add a
+variable to the graph by constructing an instance of the class `Variable`.
+
+The `Variable()` constructor requires an initial value for the variable,
+which can be a `Tensor` of any type and shape. The initial value defines the
+type and shape of the variable. After construction, the type and shape of
+the variable are fixed. The value can be changed using one of the assign
+methods.
+
+If you want to change the shape of a variable later you have to use an
+`assign` Op with `validate_shape=False`.
+
+Just like any `Tensor`, variables created with `Variable()` can be used as
+inputs for other Ops in the graph. Additionally, all the operators
+overloaded for the `Tensor` class are carried over to variables, so you can
+also add nodes to the graph by just doing arithmetic on variables.
+
+```python
+import tensorflow as tf
+
+# Create a variable.
+w = tf.Variable(<initial-value>, name=<optional-name>)
+
+# Use the variable in the graph like any Tensor.
+y = tf.matmul(w, ...another variable or tensor...)
+
+# The overloaded operators are available too.
+z = tf.sigmoid(w + y)
+
+# Assign a new value to the variable with `assign()` or a related method.
+w.assign(w + 1.0)
+w.assign_add(1.0)
+```
+
+When you launch the graph, variables have to be explicitly initialized before
+you can run Ops that use their value. You can initialize a variable by
+running its *initializer op*, restoring the variable from a save file, or
+simply running an `assign` Op that assigns a value to the variable. In fact,
+the variable *initializer op* is just an `assign` Op that assigns the
+variable's initial value to the variable itself.
+
+```python
+# Launch the graph in a session.
+with tf.Session() as sess:
+    # Run the variable initializer.
+    sess.run(w.initializer)
+    # ...you now can run ops that use the value of 'w'...
+```
+
+The most common initialization pattern is to use the convenience function
+`initialize_all_variables()` to add an Op to the graph that initializes
+all the variables. You then run that Op after launching the graph.
+
+```python
+# Add an Op to initialize all variables.
+init_op = tf.initialize_all_variables()
+
+# Launch the graph in a session.
+with tf.Session() as sess:
+    # Run the Op that initializes all variables.
+    sess.run(init_op)
+    # ...you can now run any Op that uses variable values...
+```
+
+If you need to create a variable with an initial value dependent on another
+variable, use the other variable's `initialized_value()`. This ensures that
+variables are initialized in the right order.
+
+All variables are automatically collected in the graph where they are
+created. By default, the constructor adds the new variable to the graph
+collection `GraphKeys.VARIABLES`. The convenience function
+`all_variables()` returns the contents of that collection.
+
+When building a machine learning model it is often convenient to distinguish
+betwen variables holding the trainable model parameters and other variables
+such as a `global step` variable used to count training steps. To make this
+easier, the variable constructor supports a `trainable=<bool>` parameter. If
+`True`, the new variable is also added to the graph collection
+`GraphKeys.TRAINABLE_VARIABLES`. The convenience function
+`trainable_variables()` returns the contents of this collection. The
+various `Optimizer` classes use this collection as the default list of
+variables to optimize.
+
+
+Creating a variable.
+
+- - -
+
+#### `tf.Variable.__init__(initial_value=None, trainable=True, collections=None, validate_shape=True, caching_device=None, name=None, variable_def=None, dtype=None)` {#Variable.__init__}
+
+Creates a new variable with value `initial_value`.
+
+The new variable is added to the graph collections listed in `collections`,
+which defaults to `[GraphKeys.VARIABLES]`.
+
+If `trainable` is `True` the variable is also added to the graph collection
+`GraphKeys.TRAINABLE_VARIABLES`.
+
+This constructor creates both a `variable` Op and an `assign` Op to set the
+variable to its initial value.
+
+##### Args:
+
+
+*  <b>`initial_value`</b>: A `Tensor`, or Python object convertible to a `Tensor`,
+    which is the initial value for the Variable. The initial value must have
+    a shape specified unless `validate_shape` is set to False. Can also be a
+    callable with no argument that returns the initial value when called. In
+    that case, `dtype` must be specified. (Note that initializer functions
+    from init_ops.py must first be bound to a shape before being used here.)
+*  <b>`trainable`</b>: If `True`, the default, also adds the variable to the graph
+    collection `GraphKeys.TRAINABLE_VARIABLES`. This collection is used as
+    the default list of variables to use by the `Optimizer` classes.
+*  <b>`collections`</b>: List of graph collections keys. The new variable is added to
+    these collections. Defaults to `[GraphKeys.VARIABLES]`.
+*  <b>`validate_shape`</b>: If `False`, allows the variable to be initialized with a
+    value of unknown shape. If `True`, the default, the shape of
+    `initial_value` must be known.
+*  <b>`caching_device`</b>: Optional device string describing where the Variable
+    should be cached for reading.  Defaults to the Variable's device.
+    If not `None`, caches on another device.  Typical use is to cache
+    on the device where the Ops using the Variable reside, to deduplicate
+    copying through `Switch` and other conditional statements.
+*  <b>`name`</b>: Optional name for the variable. Defaults to `'Variable'` and gets
+    uniquified automatically.
+*  <b>`variable_def`</b>: `VariableDef` protocol buffer. If not `None`, recreates
+    the Variable object with its contents. `variable_def` and the other
+    arguments are mutually exclusive.
+*  <b>`dtype`</b>: If set, initial_value will be converted to the given type.
+    If `None`, either the datatype will be kept (if `initial_value` is
+    a Tensor), or `convert_to_tensor` will decide.
+
+##### Returns:
+
+  A Variable.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If both `variable_def` and initial_value are specified.
+*  <b>`ValueError`</b>: If the initial value is not specified, or does not have a
+    shape and `validate_shape` is `True`.
+
+
+- - -
+
+#### `tf.Variable.initialized_value()` {#Variable.initialized_value}
+
+Returns the value of the initialized variable.
+
+You should use this instead of the variable itself to initialize another
+variable with a value that depends on the value of this variable.
+
+```python
+# Initialize 'v' with a random tensor.
+v = tf.Variable(tf.truncated_normal([10, 40]))
+# Use `initialized_value` to guarantee that `v` has been
+# initialized before its value is used to initialize `w`.
+# The random values are picked only once.
+w = tf.Variable(v.initialized_value() * 2.0)
+```
+
+##### Returns:
+
+  A `Tensor` holding the value of this variable after its initializer
+  has run.
+
+
+
+Changing a variable value.
+
+- - -
+
+#### `tf.Variable.assign(value, use_locking=False)` {#Variable.assign}
+
+Assigns a new value to the variable.
+
+This is essentially a shortcut for `assign(self, value)`.
+
+##### Args:
+
+
+*  <b>`value`</b>: A `Tensor`. The new value for this variable.
+*  <b>`use_locking`</b>: If `True`, use locking during the assignment.
+
+##### Returns:
+
+  A `Tensor` that will hold the new value of this variable after
+  the assignment has completed.
+
+
+- - -
+
+#### `tf.Variable.assign_add(delta, use_locking=False)` {#Variable.assign_add}
+
+Adds a value to this variable.
+
+ This is essentially a shortcut for `assign_add(self, delta)`.
+
+##### Args:
+
+
+*  <b>`delta`</b>: A `Tensor`. The value to add to this variable.
+*  <b>`use_locking`</b>: If `True`, use locking during the operation.
+
+##### Returns:
+
+  A `Tensor` that will hold the new value of this variable after
+  the addition has completed.
+
+
+- - -
+
+#### `tf.Variable.assign_sub(delta, use_locking=False)` {#Variable.assign_sub}
+
+Subtracts a value from this variable.
+
+This is essentially a shortcut for `assign_sub(self, delta)`.
+
+##### Args:
+
+
+*  <b>`delta`</b>: A `Tensor`. The value to subtract from this variable.
+*  <b>`use_locking`</b>: If `True`, use locking during the operation.
+
+##### Returns:
+
+  A `Tensor` that will hold the new value of this variable after
+  the subtraction has completed.
+
+
+- - -
+
+#### `tf.Variable.scatter_sub(sparse_delta, use_locking=False)` {#Variable.scatter_sub}
+
+Subtracts `IndexedSlices` from this variable.
+
+This is essentially a shortcut for `scatter_sub(self, sparse_delta.indices,
+sparse_delta.values)`.
+
+##### Args:
+
+
+*  <b>`sparse_delta`</b>: `IndexedSlices` to be subtracted from this variable.
+*  <b>`use_locking`</b>: If `True`, use locking during the operation.
+
+##### Returns:
+
+  A `Tensor` that will hold the new value of this variable after
+  the scattered subtraction has completed.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `sparse_delta` is not an `IndexedSlices`.
+
+
+- - -
+
+#### `tf.Variable.count_up_to(limit)` {#Variable.count_up_to}
+
+Increments this variable until it reaches `limit`.
+
+When that Op is run it tries to increment the variable by `1`. If
+incrementing the variable would bring it above `limit` then the Op raises
+the exception `OutOfRangeError`.
+
+If no error is raised, the Op outputs the value of the variable before
+the increment.
+
+This is essentially a shortcut for `count_up_to(self, limit)`.
+
+##### Args:
+
+
+*  <b>`limit`</b>: value at which incrementing the variable raises an error.
+
+##### Returns:
+
+  A `Tensor` that will hold the variable value before the increment. If no
+  other Op modifies this variable, the values produced will all be
+  distinct.
+
+
+
+- - -
+
+#### `tf.Variable.eval(session=None)` {#Variable.eval}
+
+In a session, computes and returns the value of this variable.
+
+This is not a graph construction method, it does not add ops to the graph.
+
+This convenience method requires a session where the graph containing this
+variable has been launched. If no session is passed, the default session is
+used.  See the [Session class](../../api_docs/python/client.md#Session) for
+more information on launching a graph and on sessions.
+
+```python
+v = tf.Variable([1, 2])
+init = tf.initialize_all_variables()
+
+with tf.Session() as sess:
+    sess.run(init)
+    # Usage passing the session explicitly.
+    print(v.eval(sess))
+    # Usage with the default session.  The 'with' block
+    # above makes 'sess' the default session.
+    print(v.eval())
+```
+
+##### Args:
+
+
+*  <b>`session`</b>: The session to use to evaluate this variable. If
+    none, the default session is used.
+
+##### Returns:
+
+  A numpy `ndarray` with a copy of the value of this variable.
+
+
+
+Properties.
+
+- - -
+
+#### `tf.Variable.name` {#Variable.name}
+
+The name of this variable.
+
+
+- - -
+
+#### `tf.Variable.dtype` {#Variable.dtype}
+
+The `DType` of this variable.
+
+
+- - -
+
+#### `tf.Variable.get_shape()` {#Variable.get_shape}
+
+The `TensorShape` of this variable.
+
+##### Returns:
+
+  A `TensorShape`.
+
+
+- - -
+
+#### `tf.Variable.device` {#Variable.device}
+
+The device of this variable.
+
+
+- - -
+
+#### `tf.Variable.initializer` {#Variable.initializer}
+
+The initializer operation for this variable.
+
+
+- - -
+
+#### `tf.Variable.graph` {#Variable.graph}
+
+The `Graph` of this variable.
+
+
+- - -
+
+#### `tf.Variable.op` {#Variable.op}
+
+The `Operation` of this variable.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.Variable.from_proto(variable_def)` {#Variable.from_proto}
+
+Returns a `Variable` object created from `variable_def`.
+
+
+- - -
+
+#### `tf.Variable.initial_value` {#Variable.initial_value}
+
+Returns the Tensor used as the initial value for the variable.
+
+Note that this is different from `initialized_value()` which runs
+the op that initializes the variable before returning its value.
+This method returns the tensor that is used by the op that initializes
+the variable.
+
+##### Returns:
+
+  A `Tensor`.
+
+
+- - -
+
+#### `tf.Variable.ref()` {#Variable.ref}
+
+Returns a reference to this variable.
+
+You usually do not need to call this method as all ops that need a reference
+to the variable call it automatically.
+
+Returns is a `Tensor` which holds a reference to the variable.  You can
+assign a new value to the variable by passing the tensor to an assign op.
+See [`value()`](#Variable.value) if you want to get the value of the
+variable.
+
+##### Returns:
+
+  A `Tensor` that is a reference to the variable.
+
+
+- - -
+
+#### `tf.Variable.to_proto()` {#Variable.to_proto}
+
+Converts a `Variable` to a `VariableDef` protocol buffer.
+
+##### Returns:
+
+  A `VariableDef` protocol buffer.
+
+
+- - -
+
+#### `tf.Variable.value()` {#Variable.value}
+
+Returns the last snapshot of this variable.
+
+You usually do not need to call this method as all ops that need the value
+of the variable call it automatically through a `convert_to_tensor()` call.
+
+Returns a `Tensor` which holds the value of the variable.  You can not
+assign a new value to this tensor as it is not a reference to the variable.
+See [`ref()`](#Variable.ref) if you want to get a reference to the
+variable.
+
+To avoid copies, if the consumer of the returned value is on the same device
+as the variable, this actually returns the live value of the variable, not
+a copy.  Updates to the variable are seen by the consumer.  If the consumer
+is on a different device it will get a copy of the variable.
+
+##### Returns:
+
+  A `Tensor` containing the value of the variable.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.add.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.add.md
new file mode 100644
index 0000000000..738f0337d3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.add.md
@@ -0,0 +1,17 @@
+### `tf.add(x, y, name=None)` {#add}
+
+Returns x + y element-wise.
+
+*NOTE*: Add supports broadcasting. AddN does not.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`, `string`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.add_to_collection.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.add_to_collection.md
deleted file mode 100644
index 1d8d752917..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.add_to_collection.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.add_to_collection(name, value)` {#add_to_collection}
-
-Wrapper for `Graph.add_to_collection()` using the default graph.
-
-See [`Graph.add_to_collection()`](../../api_docs/python/framework.md#Graph.add_to_collection)
-for more details.
-
-##### Args:
-
-
-*  <b>`name`</b>: The key for the collection. For example, the `GraphKeys` class
-    contains many standard names for collections.
-*  <b>`value`</b>: The value to add to the collection.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.as_dtype.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.as_dtype.md
deleted file mode 100644
index 50a048aacb..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.as_dtype.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.as_dtype(type_value)` {#as_dtype}
-
-Converts the given `type_value` to a `DType`.
-
-##### Args:
-
-
-*  <b>`type_value`</b>: A value that can be converted to a `tf.DType`
-    object. This may currently be a `tf.DType` object, a
-    [`DataType` enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto),
-    a string type name, or a `numpy.dtype`.
-
-##### Returns:
-
-  A `DType` corresponding to `type_value`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `type_value` cannot be converted to a `DType`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.assert_integer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.assert_integer.md
deleted file mode 100644
index c75ba58765..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.assert_integer.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.assert_integer(x, data=None, summarize=None, name=None)` {#assert_integer}
-
-Assert that `x` is of integer dtype.
-
-Example of adding a dependency to an operation:
-
-```python
-with tf.control_dependencies([tf.assert_integer(x)]):
-  output = tf.reduce_sum(x)
-```
-
-Example of adding dependency to the tensor being checked:
-
-```python
-x = tf.with_dependencies([tf.assert_integer(x)], x)
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: `Tensor` whose basetype is integer and is not quantized.
-*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
-    error message and first few entries of `x`.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_integer".
-
-##### Returns:
-
-  Op that raises `InvalidArgumentError` if `x == y` is False.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.assert_non_positive.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.assert_non_positive.md
deleted file mode 100644
index 83eb36a95c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.assert_non_positive.md
+++ /dev/null
@@ -1,34 +0,0 @@
-### `tf.assert_non_positive(x, data=None, summarize=None, name=None)` {#assert_non_positive}
-
-Assert the condition `x <= 0` holds element-wise.
-
-Example of adding a dependency to an operation:
-
-```python
-with tf.control_dependencies([tf.assert_non_positive(x)]):
-  output = tf.reduce_sum(x)
-```
-
-Example of adding dependency to the tensor being checked:
-
-```python
-x = tf.with_dependencies([tf.assert_non_positive(x)], x)
-```
-
-Non-positive means, for every element `x[i]` of `x`, we have `x[i] <= 0`.
-If `x` is empty this is trivially satisfied.
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
-    error message and first few entries of `x`.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).
-    Defaults to "assert_non_positive".
-
-##### Returns:
-
-  Op raising `InvalidArgumentError` unless `x` is all non-positive.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.assert_rank.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.assert_rank.md
new file mode 100644
index 0000000000..e8da009641
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.assert_rank.md
@@ -0,0 +1,36 @@
+### `tf.assert_rank(x, rank, data=None, summarize=None, name=None)` {#assert_rank}
+
+Assert `x` has rank equal to `rank`.
+
+Example of adding a dependency to an operation:
+
+```python
+with tf.control_dependencies([tf.assert_rank(x, 2)]):
+  output = tf.reduce_sum(x)
+```
+
+Example of adding dependency to the tensor being checked:
+
+```python
+x = tf.with_dependencies([tf.assert_rank(x, 2)], x)
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`rank`</b>: Scalar integer `Tensor`.
+*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
+    error message and first few entries of `x`.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_rank".
+
+##### Returns:
+
+  Op raising `InvalidArgumentError` unless `x` has specified rank.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If static checks determine `x` has wrong rank.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_cholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_cholesky.md
deleted file mode 100644
index 487680f50b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_cholesky.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.batch_cholesky(input, name=None)` {#batch_cholesky}
-
-Calculates the Cholesky decomposition of a batch of square matrices.
-
-The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-form square matrices, with the same constraints as the single matrix Cholesky
-decomposition above. The output is a tensor of the same shape as the input
-containing the Cholesky decompositions for all input submatrices `[..., :, :]`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
-    Shape is `[..., M, M]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`. Shape is `[..., M, M]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_fft2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_fft2d.md
new file mode 100644
index 0000000000..e7a2c7b943
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_fft2d.md
@@ -0,0 +1,18 @@
+### `tf.batch_fft2d(input, name=None)` {#batch_fft2d}
+
+Compute the 2-dimensional discrete Fourier Transform over the inner-most
+
+2 dimensions of `input`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`.
+  A complex64 tensor of the same shape as `input`. The inner-most 2
+  dimensions of `input` are replaced with their 2D Fourier Transform.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_matmul.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_matmul.md
deleted file mode 100644
index a4764435b8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_matmul.md
+++ /dev/null
@@ -1,41 +0,0 @@
-### `tf.batch_matmul(x, y, adj_x=None, adj_y=None, name=None)` {#batch_matmul}
-
-Multiplies slices of two tensors in batches.
-
-Multiplies all slices of `Tensor` `x` and `y` (each slice can be
-viewed as an element of a batch), and arranges the individual results
-in a single output tensor of the same batch size. Each of the
-individual slices can optionally be adjointed (to adjoint a matrix
-means to transpose and conjugate it) before multiplication by setting
-the `adj_x` or `adj_y` flag to `True`, which are by default `False`.
-
-The input tensors `x` and `y` are 3-D or higher with shape `[..., r_x, c_x]`
-and `[..., r_y, c_y]`.
-
-The output tensor is 3-D or higher with shape `[..., r_o, c_o]`, where:
-
-    r_o = c_x if adj_x else r_x
-    c_o = r_y if adj_y else c_y
-
-It is computed as:
-
-    output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `complex64`, `complex128`.
-    3-D or higher with shape `[..., r_x, c_x]`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-    3-D or higher with shape `[..., r_y, c_y]`.
-*  <b>`adj_x`</b>: An optional `bool`. Defaults to `False`.
-    If `True`, adjoint the slices of `x`. Defaults to `False`.
-*  <b>`adj_y`</b>: An optional `bool`. Defaults to `False`.
-    If `True`, adjoint the slices of `y`. Defaults to `False`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-  3-D or higher with shape `[..., r_o, c_o]`
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_matrix_determinant.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_matrix_determinant.md
new file mode 100644
index 0000000000..83f9503a4d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.batch_matrix_determinant.md
@@ -0,0 +1,19 @@
+### `tf.batch_matrix_determinant(input, name=None)` {#batch_matrix_determinant}
+
+Calculates the determinants for a batch of square matrices.
+
+The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+form square matrices. The output is a 1-D tensor containing the determinants
+for all input submatrices `[..., :, :]`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+    Shape is `[..., M, M]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`. Shape is `[...]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.case.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.case.md
new file mode 100644
index 0000000000..9314837b8e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.case.md
@@ -0,0 +1,75 @@
+### `tf.case(pred_fn_pairs, default, exclusive=False, name='case')` {#case}
+
+Create a case operation.
+
+The `pred_fn_pairs` parameter is a dict or list of pairs of size N.
+Each pair contains a boolean scalar tensor and a python callable that
+creates the tensors to be returned if the boolean evaluates to True.
+`default` is a callable generating a list of tensors. All the callables
+in `pred_fn_pairs` as well as `default` should return the same number
+and types of tensors.
+
+If `exclusive==True`, all predicates are evaluated, and a logging operation
+with an error is returned if more than one of the predicates evaluates to
+True. If `exclusive==False`, execution stops are the first predicate which
+evaluates to True, and the tensors generated by the corresponding function
+are returned immediately. If none of the predicates evaluate to True, this
+operation returns the tensors generated by `default`.
+
+Example 1:
+  Pseudocode:
+  ```
+    if (x < y) return 17;
+    else return 23;
+  ```
+
+  Expressions:
+  ```
+    f1 = lambda: tf.constant(17)
+    f2 = lambda: tf.constant(23)
+    r = case([(tf.less(x, y), f1)], default=f2)
+  ```
+
+Example 2:
+  Pseudocode:
+  ```
+    if (x < y && x > z) raise OpError("Only one predicate may evaluate true");
+    if (x < y) return 17;
+    else if (x > z) return 23;
+    else return -1;
+  ```
+
+  Expressions:
+  ```
+    x = tf.constant(0)
+    y = tf.constant(1)
+    z = tf.constant(2)
+    def f1(): return tf.constant(17)
+    def f2(): return tf.constant(23)
+    def f3(): return tf.constant(-1)
+    r = case({tf.less(x, y): f1, tf.greater(x, z): f2},
+             default=f3, exclusive=True)
+  ```
+
+##### Args:
+
+
+*  <b>`pred_fn_pairs`</b>: Dict or list of pairs of a boolean scalar tensor and a
+                 callable which returns a list of tensors.
+*  <b>`default`</b>: A callable that returns a list of tensors.
+*  <b>`exclusive`</b>: True iff more than one predicate is allowed to evaluate to True.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  The tensors returned by the first pair whose predicate evaluated to True, or
+  those returned by `default` if none does.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `pred_fn_pairs` is not a list/dictionary.
+*  <b>`TypeError`</b>: If `pred_fn_pairs` is a list but does not contain 2-tuples.
+*  <b>`TypeError`</b>: If `fns[i]` is not callable for any i, or `default` is not
+             callable.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.clip_by_value.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.clip_by_value.md
deleted file mode 100644
index 7cd7e0311e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.clip_by_value.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.clip_by_value(t, clip_value_min, clip_value_max, name=None)` {#clip_by_value}
-
-Clips tensor values to a specified min and max.
-
-Given a tensor `t`, this operation returns a tensor of the same type and
-shape as `t` with its values clipped to `clip_value_min` and `clip_value_max`.
-Any values less than `clip_value_min` are set to `clip_value_min`. Any values
-greater than `clip_value_max` are set to `clip_value_max`.
-
-##### Args:
-
-
-*  <b>`t`</b>: A `Tensor`.
-*  <b>`clip_value_min`</b>: A 0-D (scalar) `Tensor`. The minimum value to clip by.
-*  <b>`clip_value_max`</b>: A 0-D (scalar) `Tensor`. The maximum value to clip by.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A clipped `Tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.copy_graph.copy_op_to_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.copy_graph.copy_op_to_graph.md
new file mode 100644
index 0000000000..d549132fa2
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.copy_graph.copy_op_to_graph.md
@@ -0,0 +1,29 @@
+### `tf.contrib.copy_graph.copy_op_to_graph(org_instance, to_graph, variables, scope='')` {#copy_op_to_graph}
+
+Given an `Operation` 'org_instance` from one `Graph`,
+initializes and returns a copy of it from another `Graph`,
+under the specified scope (default `""`).
+
+The copying is done recursively, so any `Operation` whose output
+is required to evaluate the `org_instance`, is also copied (unless
+already done).
+
+Since `Variable` instances are copied separately, those required
+to evaluate `org_instance` must be provided as input.
+
+Args:
+org_instance: An `Operation` from some `Graph`. Could be a
+    `Placeholder` as well.
+to_graph: The `Graph` to copy `org_instance` to.
+variables: An iterable of `Variable` instances to copy `org_instance` to.
+scope: A scope for the new `Variable` (default `""`).
+
+##### Returns:
+
+    The copied `Operation` from `to_graph`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `org_instance` is not an `Operation` or `Tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.copy_graph.copy_variable_to_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.copy_graph.copy_variable_to_graph.md
deleted file mode 100644
index 85e336a29b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.copy_graph.copy_variable_to_graph.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.contrib.copy_graph.copy_variable_to_graph(org_instance, to_graph, scope='')` {#copy_variable_to_graph}
-
-Given a `Variable` instance from one `Graph`, initializes and returns
-a copy of it from another `Graph`, under the specified scope
-(default `""`).
-
-Args:
-org_instance: A `Variable` from some `Graph`.
-to_graph: The `Graph` to copy the `Variable` to.
-scope: A scope for the new `Variable` (default `""`).
-
-##### Returns:
-
-    The copied `Variable` from `to_graph`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `org_instance` is not a `Variable`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.DiscreteDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.DiscreteDistribution.md
deleted file mode 100644
index 6e78e38ebe..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.DiscreteDistribution.md
+++ /dev/null
@@ -1,139 +0,0 @@
-Base class for discrete probability distributions.
-
-`DiscreteDistribution` defines the API for the likelihood functions `pmf` and
-`log_pmf` of discrete probability distributions.
-
-Subclasses must override both `pmf` and `log_pmf` but one can call this base
-class's implementation.
-
-See `BaseDistribution` for more information on the API for probability
-distributions.
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.batch_shape(name=None)` {#DiscreteDistribution.batch_shape}
-
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-The product of the dimensions of the `batch_shape` is the number of
-independent distributions of this kind the instance represents.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `batch_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.cdf(value, name='cdf')` {#DiscreteDistribution.cdf}
-
-Cumulative distribution function.
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.dtype` {#DiscreteDistribution.dtype}
-
-dtype of samples from this distribution.
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.entropy(name=None)` {#DiscreteDistribution.entropy}
-
-Entropy of the distribution in nats.
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.event_shape(name=None)` {#DiscreteDistribution.event_shape}
-
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `event_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.get_batch_shape()` {#DiscreteDistribution.get_batch_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `batch_shape`. May be only partially defined.
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.get_event_shape()` {#DiscreteDistribution.get_event_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `event_shape`. May be only partially defined.
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.log_cdf(value, name='log_cdf')` {#DiscreteDistribution.log_cdf}
-
-Log CDF.
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.log_pmf(value, name='log_pmf')` {#DiscreteDistribution.log_pmf}
-
-Log of the probability mass function.
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.mean` {#DiscreteDistribution.mean}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.name` {#DiscreteDistribution.name}
-
-Name to prepend to all ops.
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.pmf(value, name='pmf')` {#DiscreteDistribution.pmf}
-
-Probability mass function.
-
-
-- - -
-
-#### `tf.contrib.distributions.DiscreteDistribution.sample(n, seed=None, name=None)` {#DiscreteDistribution.sample}
-
-Generate `n` samples.
-
-##### Args:
-
-
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
-
-##### Returns:
-
-
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.StudentT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.StudentT.md
deleted file mode 100644
index 816e5d5a83..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.StudentT.md
+++ /dev/null
@@ -1,245 +0,0 @@
-Student's t distribution with degree-of-freedom parameter df.
-
-#### Mathematical details
-
-The PDF of this distribution is:
-
-`f(t) = gamma((df+1)/2)/sqrt(df*pi)/gamma(df/2)*(1+t^2/df)^(-(df+1)/2)`
-
-#### Examples
-
-Examples of initialization of one or a batch of distributions.
-
-```python
-# Define a single scalar Student t distribution.
-single_dist = tf.contrib.distributions.StudentT(df=3)
-
-# Evaluate the pdf at 1, returning a scalar Tensor.
-single_dist.pdf(1.)
-
-# Define a batch of two scalar valued Student t's.
-# The first has degrees of freedom 2, mean 1, and scale 11.
-# The second 3, 2 and 22.
-multi_dist = tf.contrib.distributions.StudentT(df=[2, 3],
-                                               mu=[1, 2.],
-                                               sigma=[11, 22.])
-
-# Evaluate the pdf of the first distribution on 0, and the second on 1.5,
-# returning a length two tensor.
-multi_dist.pdf([0, 1.5])
-
-# Get 3 samples, returning a 3 x 2 tensor.
-multi_dist.sample(3)
-```
-
-Arguments are broadcast when possible.
-
-```python
-# Define a batch of two Student's t distributions.
-# Both have df 2 and mean 1, but different scales.
-dist = tf.contrib.distributions.StudentT(df=2, mu=1, sigma=[11, 22.])
-
-# Evaluate the pdf of both distributions on the same point, 3.0,
-# returning a length 2 tensor.
-dist.pdf(3.0)
-```
-- - -
-
-#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, name='StudentT')` {#StudentT.__init__}
-
-Construct Student's t distributions.
-
-The distributions have degree of freedom `df`, mean `mu`, and scale `sigma`.
-
-The parameters `df`, `mu`, and `sigma` must be shaped in a way that supports
-broadcasting (e.g. `df + mu + sigma` is a valid operation).
-
-##### Args:
-
-
-*  <b>`df`</b>: `float` or `double` tensor, the degrees of freedom of the
-    distribution(s). `df` must contain only positive values.
-*  <b>`mu`</b>: `float` or `double` tensor, the means of the distribution(s).
-*  <b>`sigma`</b>: `float` or `double` tensor, the scaling factor for the
-    distribution(s). `sigma` must contain only positive values.
-    Note that `sigma` is not the standard deviation of this distribution.
-*  <b>`name`</b>: The name to give Ops created by the initializer.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if mu and sigma are different dtypes.
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.batch_shape(name='batch_shape')` {#StudentT.batch_shape}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf')` {#StudentT.cdf}
-
-Cumulative distribution function.
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.df` {#StudentT.df}
-
-Degrees of freedom in these Student's t distribution(s).
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.dtype` {#StudentT.dtype}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.entropy(name='entropy')` {#StudentT.entropy}
-
-The entropy of Student t distribution(s).
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.event_shape(name='event_shape')` {#StudentT.event_shape}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.get_batch_shape()` {#StudentT.get_batch_shape}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.get_event_shape()` {#StudentT.get_event_shape}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.is_reparameterized` {#StudentT.is_reparameterized}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf')` {#StudentT.log_cdf}
-
-Log CDF.
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.log_pdf(x, name='log_pdf')` {#StudentT.log_pdf}
-
-Log pdf of observations in `x` under these Student's t-distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `df`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.mean` {#StudentT.mean}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.mu` {#StudentT.mu}
-
-Locations of these Student's t distribution(s).
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.name` {#StudentT.name}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.pdf(x, name='pdf')` {#StudentT.pdf}
-
-The PDF of observations in `x` under these Student's t distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `df`, `mu`, and
-    `sigma`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.sample(n, seed=None, name='sample')` {#StudentT.sample}
-
-Sample `n` observations from the Student t Distributions.
-
-##### Args:
-
-
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.sigma` {#StudentT.sigma}
-
-Scaling factors of these Student's t distribution(s).
-
-
-- - -
-
-#### `tf.contrib.distributions.StudentT.variance` {#StudentT.variance}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.ffmpeg.encode_audio.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.ffmpeg.encode_audio.md
new file mode 100644
index 0000000000..fb9d958f26
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.ffmpeg.encode_audio.md
@@ -0,0 +1,19 @@
+### `tf.contrib.ffmpeg.encode_audio(audio, file_format=None, samples_per_second=None)` {#encode_audio}
+
+Creates an op that encodes an audio file using sampled audio from a tensor.
+
+##### Args:
+
+
+*  <b>`audio`</b>: A rank 2 tensor that has time along dimension 0 and channels along
+      dimension 1. Dimension 0 is `samples_per_second * length` long in
+      seconds.
+*  <b>`file_format`</b>: The type of file to encode. "wav" is the only supported format.
+*  <b>`samples_per_second`</b>: The number of samples in the audio tensor per second of
+      audio.
+
+##### Returns:
+
+  A scalar tensor that contains the encoded audio in the specified file
+  format.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.layers.convolution2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.layers.convolution2d.md
deleted file mode 100644
index f296166c9c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.layers.convolution2d.md
+++ /dev/null
@@ -1,43 +0,0 @@
-### `tf.contrib.layers.convolution2d(*args, **kwargs)` {#convolution2d}
-
-Adds a 2D convolution followed by an optional batch_norm layer.
-
-`convolution2d` creates a variable called `weights`, representing the
-convolutional kernel, that is convolved with the `inputs` to produce a
-`Tensor` of activations. If a `normalizer_fn` is provided (such as
-`batch_norm`), it is then applied. Otherwise, if `normalizer_fn` is
-None and a `biases_initializer` is provided then a `biases` variable would be
-created and added the activations. Finally, if `activation_fn` is not `None`,
-it is applied to the activations as well.
-
-##### Args:
-
-
-*  <b>`inputs`</b>: a 4-D tensor  `[batch_size, height, width, channels]`.
-*  <b>`num_outputs`</b>: integer, the number of output filters.
-*  <b>`kernel_size`</b>: a list of length 2 `[kernel_height, kernel_width]` of
-    of the filters. Can be an int if both values are the same.
-*  <b>`stride`</b>: a list of length 2 `[stride_height, stride_width]`.
-    Can be an int if both strides are the same. Note that presently
-    both strides must have the same value.
-*  <b>`padding`</b>: one of `VALID` or `SAME`.
-*  <b>`activation_fn`</b>: activation function.
-*  <b>`normalizer_fn`</b>: normalization function to use instead of `biases`. If
-    `normalize_fn` is provided then `biases_initializer` and
-    `biases_regularizer` are ignored and `biases` are not created nor added.
-*  <b>`normalizer_params`</b>: normalization function parameters.
-*  <b>`weights_initializer`</b>: An initializer for the weights.
-*  <b>`weights_regularizer`</b>: Optional regularizer for the weights.
-*  <b>`biases_initializer`</b>: An initializer for the biases. If None skip biases.
-*  <b>`biases_regularizer`</b>: Optional regularizer for the biases.
-*  <b>`reuse`</b>: whether or not the layer and its variables should be reused. To be
-    able to reuse the layer scope must be given.
-*  <b>`variables_collections`</b>: optional list of collections for all the variables or
-    a dictionay containing a different list of collection per variable.
-*  <b>`outputs_collections`</b>: collection to add the outputs.
-*  <b>`scope`</b>: Optional scope for `variable_op_scope`.
-
-##### Returns:
-
-  a tensor representing the output of the operation.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.layers.summarize_tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.layers.summarize_tensor.md
deleted file mode 100644
index 872ba5c9d4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.layers.summarize_tensor.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.contrib.layers.summarize_tensor(tensor, tag=None)` {#summarize_tensor}
-
-Summarize a tensor using a suitable summary type.
-
-This function adds a summary op for `tensor`. The type of summary depends on
-the shape of `tensor`. For scalars, a `scalar_summary` is created, for all
-other tensors, `histogram_summary` is used.
-
-##### Args:
-
-
-*  <b>`tensor`</b>: The tensor to summarize
-*  <b>`tag`</b>: The tag to use, if None then use tensor's op's name.
-
-##### Returns:
-
-  The summary op created or None for string tensors.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.layers.variance_scaling_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.layers.variance_scaling_initializer.md
new file mode 100644
index 0000000000..c82f924432
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.layers.variance_scaling_initializer.md
@@ -0,0 +1,47 @@
+### `tf.contrib.layers.variance_scaling_initializer(factor=2.0, mode='FAN_IN', uniform=False, seed=None, dtype=tf.float32)` {#variance_scaling_initializer}
+
+Returns an initializer that generates tensors without scaling variance.
+
+When initializing a deep network, it is in principle advantageous to keep
+the scale of the input variance constant, so it does not explode or diminish
+by reaching the final layer. This initializer use the following formula:
+  if mode='FAN_IN': # Count only number of input connections.
+    n = fan_in
+  elif mode='FAN_OUT': # Count only number of output connections.
+    n = fan_out
+  elif mode='FAN_AVG': # Average number of inputs and output connections.
+    n = (fan_in + fan_out)/2.0
+
+    truncated_normal(shape, 0.0, stddev=sqrt(factor / n))
+
+To get http://arxiv.org/pdf/1502.01852v1.pdf use (Default):
+  - factor=2.0 mode='FAN_IN' uniform=False
+To get http://arxiv.org/abs/1408.5093 use:
+  - factor=1.0 mode='FAN_IN' uniform=True
+To get http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf use:
+  - factor=1.0 mode='FAN_AVG' uniform=True.
+To get xavier_initializer use either:
+  - factor=1.0 mode='FAN_AVG' uniform=True.
+  - factor=1.0 mode='FAN_AVG' uniform=False.
+
+##### Args:
+
+
+*  <b>`factor`</b>: Float.  A multiplicative factor.
+*  <b>`mode`</b>: String.  'FAN_IN', 'FAN_OUT', 'FAN_AVG'.
+*  <b>`uniform`</b>: Whether to use uniform or normal distributed random initialization.
+*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`dtype`</b>: The data type. Only floating point types are supported.
+
+##### Returns:
+
+  An initializer that generates tensors with unit variance.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
+*  <b>`TypeError`</b>: if `mode` is not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG'].
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.Estimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.Estimator.md
new file mode 100644
index 0000000000..00f12fa0a1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.Estimator.md
@@ -0,0 +1,215 @@
+Estimator class is the basic TensorFlow model trainer/evaluator.
+
+Parameters:
+  model_fn: Model function, takes features and targets tensors or dicts of
+            tensors and returns predictions and loss tensors.
+            E.g. `(features, targets) -> (predictions, loss)`.
+  model_dir: Directory to save model parameters, graph and etc.
+  classification: boolean, true if classification problem.
+  learning_rate: learning rate for the model.
+  optimizer: optimizer for the model, can be:
+             string: name of optimizer, like 'SGD', 'Adam', 'Adagrad', 'Ftl',
+               'Momentum', 'RMSProp', 'Momentum').
+               Full list in contrib/layers/optimizers.py
+             class: sub-class of Optimizer
+               (like tf.train.GradientDescentOptimizer).
+  clip_gradients: clip_norm value for call to `clip_by_global_norm`. None
+                  denotes no gradient clipping.
+  config: Configuration object.
+- - -
+
+#### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, classification=True, learning_rate=0.1, optimizer='Adagrad', clip_gradients=None, config=None)` {#Estimator.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.Estimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=32, steps=None, metrics=None, name=None)` {#Estimator.evaluate}
+
+Evaluates given model with provided evaluation data.
+
+##### Args:
+
+
+*  <b>`x`</b>: features.
+*  <b>`y`</b>: targets.
+*  <b>`input_fn`</b>: Input function. If set, x and y must be None.
+*  <b>`feed_fn`</b>: Function creating a feed dict every time it is called. Called
+    once per iteration.
+*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32. Ignored
+    if input_fn is set.
+*  <b>`steps`</b>: Number of steps to evalute for.
+*  <b>`metrics`</b>: Dict of metric ops to run. If None, the default metric functions
+    are used; if {}, no metrics are used.
+*  <b>`name`</b>: Name of the evaluation if user needs to run multiple evaluation on
+    different data sets, such as evaluate on training data vs test data.
+
+##### Returns:
+
+  Returns self.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If x or y are not None while input_fn or feed_fn is not None.
+
+
+- - -
+
+#### `tf.contrib.learn.Estimator.fit(x, y, steps, batch_size=32, monitors=None)` {#Estimator.fit}
+
+Trains a model given training data X and y.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+     iterator that returns arrays of features. The training input
+     samples for fitting the model.
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+     iterator that returns array of targets. The training target values
+     (class labels in classification, real numbers in regression).
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.Estimator.get_params(deep=True)` {#Estimator.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.Estimator.model_dir` {#Estimator.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.Estimator.partial_fit(x, y, steps=1, batch_size=32, monitors=None)` {#Estimator.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+    iterator that returns arrays of features. The training input
+    samples for fitting the model.
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+    iterator that returns array of targets. The training target values
+    (class label in classification, real numbers in regression).
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.Estimator.predict(x=None, input_fn=None, axis=None, batch_size=None)` {#Estimator.predict}
+
+Returns predictions for given features.
+
+##### Args:
+
+
+*  <b>`x`</b>: features.
+*  <b>`input_fn`</b>: Input function. If set, x must be None.
+*  <b>`axis`</b>: Axis on which to argmax (for classification).
+        Last axis is used by default.
+*  <b>`batch_size`</b>: Override default batch size.
+
+##### Returns:
+
+  Numpy array of predicted classes or regression values.
+
+
+- - -
+
+#### `tf.contrib.learn.Estimator.predict_proba(x=None, input_fn=None, batch_size=None)` {#Estimator.predict_proba}
+
+Returns prediction probabilities for given features (classification).
+
+##### Args:
+
+
+*  <b>`x`</b>: features.
+*  <b>`input_fn`</b>: Input function. If set, x and y must be None.
+*  <b>`batch_size`</b>: Override default batch size.
+
+##### Returns:
+
+  Numpy array of predicted probabilities.
+
+
+- - -
+
+#### `tf.contrib.learn.Estimator.set_params(**params)` {#Estimator.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.Estimator.train(input_fn, steps, monitors=None)` {#Estimator.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.NanLossDuringTrainingError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.NanLossDuringTrainingError.md
new file mode 100644
index 0000000000..8b13789179
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.NanLossDuringTrainingError.md
@@ -0,0 +1 @@
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowDNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowDNNRegressor.md
new file mode 100644
index 0000000000..182b81de75
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowDNNRegressor.md
@@ -0,0 +1,302 @@
+TensorFlow DNN Regressor model.
+
+Parameters:
+  hidden_units: List of hidden units per layer.
+  batch_size: Mini batch size.
+  steps: Number of steps to run over data.
+  optimizer: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
+  learning_rate: If this is constant float value, no decay function is
+    used. Instead, a customized decay function can be passed that accepts
+    global_step as parameter and returns a Tensor.
+    e.g. exponential decay function:
+    def exp_decay(global_step):
+        return tf.train.exponential_decay(
+            learning_rate=0.1, global_step,
+            decay_steps=2, decay_rate=0.001)
+  continue_training: when continue_training is True, once initialized
+    model will be continuely trained on every call of fit.
+  config: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc.
+  verbose: Controls the verbosity, possible values:
+    0: the algorithm and debug information is muted.
+    1: trainer prints the progress.
+    2: log device placement is printed.
+  dropout: When not None, the probability we will drop out a given coordinate.
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.__init__(hidden_units, n_classes=0, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, continue_training=False, config=None, verbose=1, dropout=None)` {#TensorFlowDNNRegressor.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.bias_` {#TensorFlowDNNRegressor.bias_}
+
+Returns bias of the DNN's bias layers.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowDNNRegressor.evaluate}
+
+See base class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowDNNRegressor.fit}
+
+Builds a neural network model given provided `model_fn` and training
+data X and y.
+
+Note: called first time constructs the graph and initializers
+variables. Consecutives times it will continue training the same model.
+This logic follows partial_fit() interface in scikit-learn.
+
+To restart learning, create new estimator.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class labels in classification, real numbers in regression).
+
+*  <b>`steps`</b>: int, number of steps to train.
+         If None or 0, train for `self.steps`.
+*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
+    invoke early stopping.
+*  <b>`logdir`</b>: the directory to save the log file that can be used for
+  optional visualization.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.get_params(deep=True)` {#TensorFlowDNNRegressor.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.get_tensor(name)` {#TensorFlowDNNRegressor.get_tensor}
+
+Returns tensor by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.get_tensor_value(name)` {#TensorFlowDNNRegressor.get_tensor_value}
+
+Returns value of the tensor give by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Numpy array - value of the tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.get_variable_names()` {#TensorFlowDNNRegressor.get_variable_names}
+
+Returns list of all variable names in this model.
+
+##### Returns:
+
+  List of names.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.model_dir` {#TensorFlowDNNRegressor.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.partial_fit(x, y)` {#TensorFlowDNNRegressor.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class label in classification, real numbers in regression).
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.predict(x, axis=1, batch_size=None)` {#TensorFlowDNNRegressor.predict}
+
+Predict class or regression for X.
+
+For a classification model, the predicted class for each sample in X is
+returned. For a regression model, the predicted value based on X is
+returned.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`axis`</b>: Which axis to argmax for classification.
+    By default axis 1 (next after batch) is used.
+    Use 2 for sequence predictions.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member
+    variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
+  value.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.predict_proba(x, batch_size=None)` {#TensorFlowDNNRegressor.predict_proba}
+
+Predict class probability of the input samples X.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
+  probabilities for each class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.restore(cls, path, config=None)` {#TensorFlowDNNRegressor.restore}
+
+Restores model from give path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Path to the checkpoints and other model information.
+*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
+      reconfigured.
+
+##### Returns:
+
+  Estimator, object of the subclass of TensorFlowEstimator.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.save(path)` {#TensorFlowDNNRegressor.save}
+
+Saves checkpoints and graph to given path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Folder to save model to.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.set_params(**params)` {#TensorFlowDNNRegressor.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.train(input_fn, steps, monitors=None)` {#TensorFlowDNNRegressor.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.weights_` {#TensorFlowDNNRegressor.weights_}
+
+Returns weights of the DNN weight layers.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowLinearRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowLinearRegressor.md
new file mode 100644
index 0000000000..6c793e1b90
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowLinearRegressor.md
@@ -0,0 +1,279 @@
+TensorFlow Linear Regression model.
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.__init__(n_classes=0, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowLinearRegressor.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.bias_` {#TensorFlowLinearRegressor.bias_}
+
+Returns bias of the linear regression.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowLinearRegressor.evaluate}
+
+See base class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowLinearRegressor.fit}
+
+Builds a neural network model given provided `model_fn` and training
+data X and y.
+
+Note: called first time constructs the graph and initializers
+variables. Consecutives times it will continue training the same model.
+This logic follows partial_fit() interface in scikit-learn.
+
+To restart learning, create new estimator.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class labels in classification, real numbers in regression).
+
+*  <b>`steps`</b>: int, number of steps to train.
+         If None or 0, train for `self.steps`.
+*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
+    invoke early stopping.
+*  <b>`logdir`</b>: the directory to save the log file that can be used for
+  optional visualization.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.get_params(deep=True)` {#TensorFlowLinearRegressor.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.get_tensor(name)` {#TensorFlowLinearRegressor.get_tensor}
+
+Returns tensor by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.get_tensor_value(name)` {#TensorFlowLinearRegressor.get_tensor_value}
+
+Returns value of the tensor give by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Numpy array - value of the tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.get_variable_names()` {#TensorFlowLinearRegressor.get_variable_names}
+
+Returns list of all variable names in this model.
+
+##### Returns:
+
+  List of names.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.model_dir` {#TensorFlowLinearRegressor.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.partial_fit(x, y)` {#TensorFlowLinearRegressor.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class label in classification, real numbers in regression).
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.predict(x, axis=1, batch_size=None)` {#TensorFlowLinearRegressor.predict}
+
+Predict class or regression for X.
+
+For a classification model, the predicted class for each sample in X is
+returned. For a regression model, the predicted value based on X is
+returned.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`axis`</b>: Which axis to argmax for classification.
+    By default axis 1 (next after batch) is used.
+    Use 2 for sequence predictions.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member
+    variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
+  value.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.predict_proba(x, batch_size=None)` {#TensorFlowLinearRegressor.predict_proba}
+
+Predict class probability of the input samples X.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
+  probabilities for each class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.restore(cls, path, config=None)` {#TensorFlowLinearRegressor.restore}
+
+Restores model from give path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Path to the checkpoints and other model information.
+*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
+      reconfigured.
+
+##### Returns:
+
+  Estimator, object of the subclass of TensorFlowEstimator.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.save(path)` {#TensorFlowLinearRegressor.save}
+
+Saves checkpoints and graph to given path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Folder to save model to.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.set_params(**params)` {#TensorFlowLinearRegressor.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.train(input_fn, steps, monitors=None)` {#TensorFlowLinearRegressor.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.weights_` {#TensorFlowLinearRegressor.weights_}
+
+Returns weights of the linear regression.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowRNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowRNNRegressor.md
new file mode 100644
index 0000000000..d23cd65402
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowRNNRegressor.md
@@ -0,0 +1,312 @@
+TensorFlow RNN Regressor model.
+
+Parameters:
+  rnn_size: The size for rnn cell, e.g. size of your word embeddings.
+  cell_type: The type of rnn cell, including rnn, gru, and lstm.
+  num_layers: The number of layers of the rnn model.
+  input_op_fn: Function that will transform the input tensor, such as
+    creating word embeddings, byte list, etc. This takes
+    an argument X for input and returns transformed X.
+  bidirectional: boolean, Whether this is a bidirectional rnn.
+  sequence_length: If sequence_length is provided, dynamic calculation is
+    performed. This saves computational time when unrolling past max sequence
+    length.
+  initial_state: An initial state for the RNN. This must be a tensor of
+    appropriate type and shape [batch_size x cell.state_size].
+  batch_size: Mini batch size.
+  steps: Number of steps to run over data.
+  optimizer: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
+  learning_rate: If this is constant float value, no decay function is
+    used. Instead, a customized decay function can be passed that accepts
+    global_step as parameter and returns a Tensor.
+    e.g. exponential decay function:
+    def exp_decay(global_step):
+        return tf.train.exponential_decay(
+            learning_rate=0.1, global_step,
+            decay_steps=2, decay_rate=0.001)
+  continue_training: when continue_training is True, once initialized
+    model will be continuely trained on every call of fit.
+  config: RunConfig object that controls the configurations of the
+    session, e.g. num_cores, gpu_memory_fraction, etc.
+  verbose: Controls the verbosity, possible values:
+    0: the algorithm and debug information is muted.
+    1: trainer prints the progress.
+    2: log device placement is printed.
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.__init__(rnn_size, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None, n_classes=0, batch_size=32, steps=50, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowRNNRegressor.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.bias_` {#TensorFlowRNNRegressor.bias_}
+
+Returns bias of the rnn layer.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowRNNRegressor.evaluate}
+
+See base class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowRNNRegressor.fit}
+
+Builds a neural network model given provided `model_fn` and training
+data X and y.
+
+Note: called first time constructs the graph and initializers
+variables. Consecutives times it will continue training the same model.
+This logic follows partial_fit() interface in scikit-learn.
+
+To restart learning, create new estimator.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class labels in classification, real numbers in regression).
+
+*  <b>`steps`</b>: int, number of steps to train.
+         If None or 0, train for `self.steps`.
+*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
+    invoke early stopping.
+*  <b>`logdir`</b>: the directory to save the log file that can be used for
+  optional visualization.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.get_params(deep=True)` {#TensorFlowRNNRegressor.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.get_tensor(name)` {#TensorFlowRNNRegressor.get_tensor}
+
+Returns tensor by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.get_tensor_value(name)` {#TensorFlowRNNRegressor.get_tensor_value}
+
+Returns value of the tensor give by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Numpy array - value of the tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.get_variable_names()` {#TensorFlowRNNRegressor.get_variable_names}
+
+Returns list of all variable names in this model.
+
+##### Returns:
+
+  List of names.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.model_dir` {#TensorFlowRNNRegressor.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.partial_fit(x, y)` {#TensorFlowRNNRegressor.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class label in classification, real numbers in regression).
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.predict(x, axis=1, batch_size=None)` {#TensorFlowRNNRegressor.predict}
+
+Predict class or regression for X.
+
+For a classification model, the predicted class for each sample in X is
+returned. For a regression model, the predicted value based on X is
+returned.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`axis`</b>: Which axis to argmax for classification.
+    By default axis 1 (next after batch) is used.
+    Use 2 for sequence predictions.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member
+    variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
+  value.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.predict_proba(x, batch_size=None)` {#TensorFlowRNNRegressor.predict_proba}
+
+Predict class probability of the input samples X.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
+  probabilities for each class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.restore(cls, path, config=None)` {#TensorFlowRNNRegressor.restore}
+
+Restores model from give path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Path to the checkpoints and other model information.
+*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
+      reconfigured.
+
+##### Returns:
+
+  Estimator, object of the subclass of TensorFlowEstimator.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.save(path)` {#TensorFlowRNNRegressor.save}
+
+Saves checkpoints and graph to given path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Folder to save model to.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.set_params(**params)` {#TensorFlowRNNRegressor.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.train(input_fn, steps, monitors=None)` {#TensorFlowRNNRegressor.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.weights_` {#TensorFlowRNNRegressor.weights_}
+
+Returns weights of the rnn layer.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowRegressor.md
deleted file mode 100644
index 169509f72f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.TensorFlowRegressor.md
+++ /dev/null
@@ -1,279 +0,0 @@
-TensorFlow Linear Regression model.
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.__init__(n_classes=0, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowRegressor.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.bias_` {#TensorFlowRegressor.bias_}
-
-Returns bias of the linear regression.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowRegressor.evaluate}
-
-See base class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowRegressor.fit}
-
-Builds a neural network model given provided `model_fn` and training
-data X and y.
-
-Note: called first time constructs the graph and initializers
-variables. Consecutives times it will continue training the same model.
-This logic follows partial_fit() interface in scikit-learn.
-
-To restart learning, create new estimator.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class labels in classification, real numbers in regression).
-
-*  <b>`steps`</b>: int, number of steps to train.
-         If None or 0, train for `self.steps`.
-*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
-    invoke early stopping.
-*  <b>`logdir`</b>: the directory to save the log file that can be used for
-  optional visualization.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.get_params(deep=True)` {#TensorFlowRegressor.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.get_tensor(name)` {#TensorFlowRegressor.get_tensor}
-
-Returns tensor by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.get_tensor_value(name)` {#TensorFlowRegressor.get_tensor_value}
-
-Returns value of the tensor give by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.get_variable_names()` {#TensorFlowRegressor.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.model_dir` {#TensorFlowRegressor.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.partial_fit(x, y)` {#TensorFlowRegressor.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class label in classification, real numbers in regression).
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.predict(x, axis=1, batch_size=None)` {#TensorFlowRegressor.predict}
-
-Predict class or regression for X.
-
-For a classification model, the predicted class for each sample in X is
-returned. For a regression model, the predicted value based on X is
-returned.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`axis`</b>: Which axis to argmax for classification.
-    By default axis 1 (next after batch) is used.
-    Use 2 for sequence predictions.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member
-    variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
-  value.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.predict_proba(x, batch_size=None)` {#TensorFlowRegressor.predict_proba}
-
-Predict class probability of the input samples X.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
-  probabilities for each class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.restore(cls, path, config=None)` {#TensorFlowRegressor.restore}
-
-Restores model from give path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Path to the checkpoints and other model information.
-*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
-      reconfigured.
-
-##### Returns:
-
-  Estimator, object of the subclass of TensorFlowEstimator.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.save(path)` {#TensorFlowRegressor.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.set_params(**params)` {#TensorFlowRegressor.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.train(input_fn, steps, monitors=None)` {#TensorFlowRegressor.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.weights_` {#TensorFlowRegressor.weights_}
-
-Returns weights of the linear regression.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.read_batch_features.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.read_batch_features.md
deleted file mode 100644
index 75b40f7e75..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.read_batch_features.md
+++ /dev/null
@@ -1,43 +0,0 @@
-### `tf.contrib.learn.read_batch_features(file_pattern, batch_size, features, reader, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name=None)` {#read_batch_features}
-
-Adds operations to read, queue, batch and parse `Example` protos.
-
-Given file pattern (or list of files), will setup a queue for file names,
-read `Example` proto using provided `reader`, use batch queue to create
-batches of examples of size `batch_size` and parse example given `features`
-specification.
-
-All queue runners are added to the queue runners collection, and may be
-started via `start_queue_runners`.
-
-All ops are added to the default graph.
-
-##### Args:
-
-
-*  <b>`file_pattern`</b>: List of files or pattern of file paths containing
-      `Example` records. See `tf.gfile.Glob` for pattern rules.
-*  <b>`batch_size`</b>: An int or scalar `Tensor` specifying the batch size to use.
-*  <b>`features`</b>: A `dict` mapping feature keys to `FixedLenFeature` or
-    `VarLenFeature` values.
-*  <b>`reader`</b>: A function or class that returns an object with
-    `read` method, (filename tensor) -> (example tensor).
-*  <b>`randomize_input`</b>: Whether the input should be randomized.
-*  <b>`num_epochs`</b>: Integer specifying the number of times to read through the
-    dataset. If None, cycles through the dataset forever. NOTE - If specified,
-    creates a variable that must be initialized, so call
-    tf.initialize_all_variables() as shown in the tests.
-*  <b>`queue_capacity`</b>: Capacity for input queue.
-*  <b>`reader_num_threads`</b>: The number of threads to read examples.
-*  <b>`parser_num_threads`</b>: The number of threads to parse examples.
-*  <b>`name`</b>: Name of resulting op.
-
-##### Returns:
-
-  A dict of `Tensor` or `SparseTensor` objects for each in `features`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: for invalid inputs.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.run_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.run_n.md
new file mode 100644
index 0000000000..8fa8f09cb5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.run_n.md
@@ -0,0 +1,19 @@
+### `tf.contrib.learn.run_n(output_dict, feed_dict=None, restore_checkpoint_path=None, n=1)` {#run_n}
+
+Run `output_dict` tensors `n` times, with the same `feed_dict` each run.
+
+##### Args:
+
+
+*  <b>`output_dict`</b>: A `dict` mapping string names to tensors to run. Must all be
+    from the same graph.
+*  <b>`feed_dict`</b>: `dict` of input values to feed each run.
+*  <b>`restore_checkpoint_path`</b>: A string containing the path to a checkpoint to
+    restore.
+*  <b>`n`</b>: Number of times to repeat.
+
+##### Returns:
+
+  A list of `n` `dict` objects, each containing values read from `output_dict`
+  tensors.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.auc_using_histogram.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.auc_using_histogram.md
new file mode 100644
index 0000000000..01f67e402c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.auc_using_histogram.md
@@ -0,0 +1,38 @@
+### `tf.contrib.metrics.auc_using_histogram(boolean_labels, scores, score_range, nbins=100, collections=None, check_shape=True, name=None)` {#auc_using_histogram}
+
+AUC computed by maintaining histograms.
+
+Rather than computing AUC directly, this Op maintains Variables containing
+histograms of the scores associated with `True` and `False` labels.  By
+comparing these the AUC is generated, with some discretization error.
+See: "Efficient AUC Learning Curve Calculation" by Bouckaert.
+
+This AUC Op updates in `O(batch_size + nbins)` time and works well even with
+large class imbalance.  The accuracy is limited by discretization error due
+to finite number of bins.  If scores are concentrated in a fewer bins,
+accuracy is lower.  If this is a concern, we recommend trying different
+numbers of bins and comparing results.
+
+##### Args:
+
+
+*  <b>`boolean_labels`</b>: 1-D boolean `Tensor`.  Entry is `True` if the corresponding
+    record is in class.
+*  <b>`scores`</b>: 1-D numeric `Tensor`, same shape as boolean_labels.
+*  <b>`score_range`</b>: `Tensor` of shape `[2]`, same dtype as `scores`.  The min/max
+    values of score that we expect.  Scores outside range will be clipped.
+*  <b>`nbins`</b>: Integer number of bins to use.  Accuracy strictly increases as the
+    number of bins increases.
+*  <b>`collections`</b>: List of graph collections keys. Internal histogram Variables
+    are added to these collections. Defaults to `[GraphKeys.LOCAL_VARIABLES]`.
+*  <b>`check_shape`</b>: Boolean.  If `True`, do a runtime shape check on the scores
+    and labels.
+*  <b>`name`</b>: A name for this Op.  Defaults to "auc_using_histogram".
+
+##### Returns:
+
+
+*  <b>`auc`</b>: `float32` scalar `Tensor`.  Fetching this converts internal histograms
+    to auc value.
+*  <b>`update_op`</b>: `Op`, when run, updates internal histograms.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.set_size.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.set_size.md
new file mode 100644
index 0000000000..8f58261e7d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.set_size.md
@@ -0,0 +1,22 @@
+### `tf.contrib.metrics.set_size(a, validate_indices=True)` {#set_size}
+
+Compute number of unique elements along last dimension of `a`.
+
+##### Args:
+
+
+*  <b>`a`</b>: `SparseTensor`, with indices sorted in row-major order.
+*  <b>`validate_indices`</b>: Whether to validate the order and range of sparse indices
+     in `a`.
+
+##### Returns:
+
+  For `a` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st
+  `n-1` dimensions as `a`. Each value is the number of unique elements in
+  the corresponding `[0...n-1]` dimension of `a`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `a` is an invalid types.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.streaming_auc.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.streaming_auc.md
deleted file mode 100644
index 2d444fac54..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.streaming_auc.md
+++ /dev/null
@@ -1,58 +0,0 @@
-### `tf.contrib.metrics.streaming_auc(predictions, labels, ignore_mask=None, num_thresholds=200, metrics_collections=None, updates_collections=None, name=None)` {#streaming_auc}
-
-Computes the approximate AUC via a Riemann sum.
-
-The `streaming_auc` function creates four local variables, `true_positives`,
-`true_negatives`, `false_positives` and `false_negatives` that are used to
-compute the AUC. To discretize the AUC curve, a linearly spaced set of
-thresholds is used to compute pairs of recall and precision values. The area
-under the curve is therefore computed using the height of the recall values
-by the false positive rate.
-
-This value is ultimately returned as `auc`, an idempotent
-operation the computes the area under a discretized curve of precision versus
-recall values (computed using the afformentioned variables). The
-`num_thresholds` variable controls the degree of discretization with larger
-numbers of thresholds more closely approximating the true AUC.
-
-To faciliate the estimation of the AUC over a stream of data, the function
-creates an `update_op` operation whose behavior is dependent on the value of
-`ignore_mask`. If `ignore_mask` is None, then `update_op` increments the
-`true_positives`, `true_negatives`, `false_positives` and `false_negatives`
-counts with the number of each found in the current `predictions` and `labels`
-`Tensors`. If `ignore_mask` is not `None`, then the increment is performed
-using only the elements of `predictions` and `labels` whose corresponding
-value in `ignore_mask` is `False`. In addition to performing the updates,
-`update_op` also returns the `auc`.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: A floating point `Tensor` of arbitrary shape and whose values
-    are in the range `[0, 1]`.
-*  <b>`labels`</b>: A binary `Tensor` whose shape matches `predictions`.
-*  <b>`ignore_mask`</b>: An optional, binary tensor whose size matches `predictions`.
-*  <b>`num_thresholds`</b>: The number of thresholds to use when discretizing the roc
-    curve.
-*  <b>`metrics_collections`</b>: An optional list of collections that `auc` should be
-    added to.
-*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
-    be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`auc`</b>: A scalar tensor representing the current area-under-curve.
-*  <b>`update_op`</b>: An operation that increments the `true_positives`,
-    `true_negatives`, `false_positives` and `false_negatives` variables
-    appropriately and whose value matches `auc`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the shape of `predictions` and `labels` do not match or if
-    `ignore_mask` is not `None` and its shape doesn't match `predictions` or
-    if either `metrics_collections` or `updates_collections` are not a list or
-    tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.streaming_percentage_less.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.streaming_percentage_less.md
new file mode 100644
index 0000000000..40ddae4d31
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.streaming_percentage_less.md
@@ -0,0 +1,47 @@
+### `tf.contrib.metrics.streaming_percentage_less(values, threshold, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_percentage_less}
+
+Computes the percentage of values less than the given threshold.
+
+The `streaming_percentage_less` function creates two local variables,
+`total` and `count` that are used to compute the percentage of `values` that
+fall below `threshold`. This rate is ultimately returned as `percentage`
+which is an idempotent operation that simply divides `total` by `count.
+To facilitate the estimation of the percentage of values that fall under
+`threshold` over multiple batches of data, the function creates an
+`update_op` operation whose behavior is dependent on the value of
+`ignore_mask`. If `ignore_mask` is None, then `update_op`
+increments `total` with the number of elements of `values` that are less
+than `threshold` and `count` with the number of elements in `values`. If
+`ignore_mask` is not `None`, then `update_op` increments `total` with the
+number of elements of `values` that are less than `threshold` and whose
+corresponding entries in `ignore_mask` are False, and `count` is incremented
+with the number of elements of `ignore_mask` that are False.
+
+##### Args:
+
+
+*  <b>`values`</b>: A numeric `Tensor` of arbitrary size.
+*  <b>`threshold`</b>: A scalar threshold.
+*  <b>`ignore_mask`</b>: An optional mask of the same shape as 'values' which indicates
+    which elements to ignore during metric computation.
+*  <b>`metrics_collections`</b>: An optional list of collections that the metric
+    value variable should be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that the metric update
+    ops should be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`percentage`</b>: A tensor representing the current mean, the value of `total`
+    divided by `count`.
+*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
+    appropriately.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `ignore_mask` is not None and its shape doesn't match `values
+    or if either `metrics_collections` or `updates_collections` are supplied
+    but are not a list or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.streaming_root_mean_squared_error.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.streaming_root_mean_squared_error.md
deleted file mode 100644
index 85319f44dd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.metrics.streaming_root_mean_squared_error.md
+++ /dev/null
@@ -1,48 +0,0 @@
-### `tf.contrib.metrics.streaming_root_mean_squared_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_root_mean_squared_error}
-
-Computes the root mean squared error between the labels and predictions.
-
-The `streaming_root_mean_squared_error` function creates two local variables,
-`total` and `count` that are used to compute the root mean squared error.
-This average is ultimately returned as `root_mean_squared_error`: an
-idempotent operation that takes the square root of the division of `total`
-by `count`. To facilitate the estimation of the root mean squared error over a
-stream of data, the function utilizes two operations. First, a `squared_error`
-operation computes the element-wise square of the difference between
-`predictions` and `labels`. Second, an `update_op` operation whose behavior is
-dependent on the value of `weights`. If `weights` is None, then `update_op`
-increments `total` with the reduced sum of `squared_error` and increments
-`count` with the number of elements in `squared_error`. If `weights` is not
-`None`, then `update_op` increments `total` with the reduced sum of the
-product of `weights` and `squared_error` and increments `count` with the
-reduced sum of `weights`. In addition to performing the updates, `update_op`
-also returns the `root_mean_squared_error` value.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: A `Tensor` of arbitrary shape.
-*  <b>`labels`</b>: A `Tensor` of the same shape as `predictions`.
-*  <b>`weights`</b>: An optional set of weights of the same shape as `predictions`. If
-    `weights` is not None, the function computes a weighted mean.
-*  <b>`metrics_collections`</b>: An optional list of collections that
-    `root_mean_squared_error` should be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
-    be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`root_mean_squared_error`</b>: A tensor representing the current mean, the value
-    of `total` divided by `count`.
-*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
-    appropriately and whose value matches `root_mean_squared_error`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `weights` is not `None` and its shape doesn't match
-    `predictions` or if either `metrics_collections` or `updates_collections`
-    are not a list or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.util.make_ndarray.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.util.make_ndarray.md
new file mode 100644
index 0000000000..7b2a81d48e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.util.make_ndarray.md
@@ -0,0 +1,20 @@
+### `tf.contrib.util.make_ndarray(tensor)` {#make_ndarray}
+
+Create a numpy ndarray from a tensor.
+
+Create a numpy ndarray with the same shape and data as the tensor.
+
+##### Args:
+
+
+*  <b>`tensor`</b>: A TensorProto.
+
+##### Returns:
+
+  A numpy array with the tensor contents.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if tensor has unsupported type.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.util.make_tensor_proto.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.util.make_tensor_proto.md
new file mode 100644
index 0000000000..f84a59be49
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.util.make_tensor_proto.md
@@ -0,0 +1,44 @@
+### `tf.contrib.util.make_tensor_proto(values, dtype=None, shape=None)` {#make_tensor_proto}
+
+Create a TensorProto.
+
+##### Args:
+
+
+*  <b>`values`</b>: Values to put in the TensorProto.
+*  <b>`dtype`</b>: Optional tensor_pb2 DataType value.
+*  <b>`shape`</b>: List of integers representing the dimensions of tensor.
+
+##### Returns:
+
+  A TensorProto. Depending on the type, it may contain data in the
+  "tensor_content" attribute, which is not directly useful to Python programs.
+  To access the values you should convert the proto back to a numpy ndarray
+  with tensor_util.MakeNdarray(proto).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if unsupported types are provided.
+*  <b>`ValueError`</b>: if arguments have inappropriate values.
+
+make_tensor_proto accepts "values" of a python scalar, a python list, a
+numpy ndarray, or a numpy scalar.
+
+If "values" is a python scalar or a python list, make_tensor_proto
+first convert it to numpy ndarray. If dtype is None, the
+conversion tries its best to infer the right numpy data
+type. Otherwise, the resulting numpy array has a compatible data
+type with the given dtype.
+
+In either case above, the numpy ndarray (either the caller provided
+or the auto converted) must have the compatible type with dtype.
+
+make_tensor_proto then converts the numpy array to a tensor proto.
+
+If "shape" is None, the resulting tensor proto represents the numpy
+array precisely.
+
+Otherwise, "shape" specifies the tensor's shape and the numpy array
+can not have more elements than what "shape" specifies.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.util.stripped_op_list_for_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.util.stripped_op_list_for_graph.md
deleted file mode 100644
index 23bfb28542..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.util.stripped_op_list_for_graph.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.contrib.util.stripped_op_list_for_graph(graph_def)` {#stripped_op_list_for_graph}
-
-Collect the stripped OpDefs for ops used by a graph.
-
-This function computes the `stripped_op_list` field of `MetaGraphDef` and
-similar protos.  The result can be communicated from the producer to the
-consumer, which can then use the C++ function
-`RemoveNewDefaultAttrsFromGraphDef` to improve forwards compatibility.
-
-##### Args:
-
-
-*  <b>`graph_def`</b>: A `GraphDef` proto, as from `graph.as_graph_def()`.
-
-##### Returns:
-
-  An `OpList` of ops used by the graph.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If an unregistered op is used.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.control_dependencies.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.control_dependencies.md
deleted file mode 100644
index 070f8788e5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.control_dependencies.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.control_dependencies(control_inputs)` {#control_dependencies}
-
-Wrapper for `Graph.control_dependencies()` using the default graph.
-
-See [`Graph.control_dependencies()`](../../api_docs/python/framework.md#Graph.control_dependencies)
-for more details.
-
-##### Args:
-
-
-*  <b>`control_inputs`</b>: A list of `Operation` or `Tensor` objects which
-    must be executed or computed before running the operations
-    defined in the context.  Can also be `None` to clear the control
-    dependencies.
-
-##### Returns:
-
- A context manager that specifies control dependencies for all
- operations constructed within the context.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.diag_part.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.diag_part.md
new file mode 100644
index 0000000000..249eb80e50
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.diag_part.md
@@ -0,0 +1,34 @@
+### `tf.diag_part(input, name=None)` {#diag_part}
+
+Returns the diagonal part of the tensor.
+
+This operation returns a tensor with the `diagonal` part
+of the `input`. The `diagonal` part is computed as follows:
+
+Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a
+tensor of rank `k` with dimensions `[D1,..., Dk]` where:
+
+`diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.
+
+For example:
+
+```prettyprint
+# 'input' is [[1, 0, 0, 0]
+              [0, 2, 0, 0]
+              [0, 0, 3, 0]
+              [0, 0, 0, 4]]
+
+tf.diag_part(input) ==> [1, 2, 3, 4]
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `complex64`.
+    Rank k tensor where k is 2, 4, or 6.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`. The extracted diagonal.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.digamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.digamma.md
deleted file mode 100644
index 5af2d11062..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.digamma.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.digamma(x, name=None)` {#digamma}
-
-Computes Psi, the derivative of Lgamma (the log of the absolute value of
-
-`Gamma(x)`), element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.edit_distance.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.edit_distance.md
deleted file mode 100644
index e5f6471817..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.edit_distance.md
+++ /dev/null
@@ -1,65 +0,0 @@
-### `tf.edit_distance(hypothesis, truth, normalize=True, name='edit_distance')` {#edit_distance}
-
-Computes the Levenshtein distance between sequences.
-
-This operation takes variable-length sequences (`hypothesis` and `truth`),
-each provided as a `SparseTensor`, and computes the Levenshtein distance.
-You can normalize the edit distance by length of `truth` by setting
-`normalize` to true.
-
-For example, given the following input:
-
-```python
-# 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values:
-#   (0,0) = ["a"]
-#   (1,0) = ["b"]
-hypothesis = tf.SparseTensor(
-    [[0, 0, 0],
-     [1, 0, 0]],
-    ["a", "b"]
-    (2, 1, 1))
-
-# 'truth' is a tensor of shape `[2, 2]` with variable-length values:
-#   (0,0) = []
-#   (0,1) = ["a"]
-#   (1,0) = ["b", "c"]
-#   (1,1) = ["a"]
-truth = tf.SparseTensor(
-    [[0, 1, 0],
-     [1, 0, 0],
-     [1, 0, 1],
-     [1, 1, 0]]
-    ["a", "b", "c", "a"],
-    (2, 2, 2))
-
-normalize = True
-```
-
-This operation would return the following:
-
-```python
-# 'output' is a tensor of shape `[2, 2]` with edit distances normalized
-# by 'truth' lengths.
-output ==> [[inf, 1.0],  # (0,0): no truth, (0,1): no hypothesis
-           [0.5, 1.0]]  # (1,0): addition, (1,1): no hypothesis
-```
-
-##### Args:
-
-
-*  <b>`hypothesis`</b>: A `SparseTensor` containing hypothesis sequences.
-*  <b>`truth`</b>: A `SparseTensor` containing truth sequences.
-*  <b>`normalize`</b>: A `bool`. If `True`, normalizes the Levenshtein distance by
-    length of `truth.`
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A dense `Tensor` with rank `R - 1`, where R is the rank of the
-  `SparseTensor` inputs `hypothesis` and `truth`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If either `hypothesis` or `truth` are not a `SparseTensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.equal.md
deleted file mode 100644
index 998db9189f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.equal.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.equal(x, y, name=None)` {#equal}
-
-Returns the truth value of (x == y) element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `quint8`, `qint8`, `qint32`, `string`, `bool`, `complex128`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.errors.DataLossError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.errors.DataLossError.md
new file mode 100644
index 0000000000..3193e77ae3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.errors.DataLossError.md
@@ -0,0 +1,13 @@
+Raised when unrecoverable data loss or corruption is encountered.
+
+For example, this may be raised by running a
+[`tf.WholeFileReader.read()`](../../api_docs/python/io_ops.md#WholeFileReader)
+operation, if the file is truncated while it is being read.
+
+- - -
+
+#### `tf.errors.DataLossError.__init__(node_def, op, message)` {#DataLossError.__init__}
+
+Creates a `DataLossError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.errors.NotFoundError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.errors.NotFoundError.md
new file mode 100644
index 0000000000..49fec3c55c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.errors.NotFoundError.md
@@ -0,0 +1,14 @@
+Raised when a requested entity (e.g., a file or directory) was not found.
+
+For example, running the
+[`tf.WholeFileReader.read()`](../../api_docs/python/io_ops.md#WholeFileReader)
+operation could raise `NotFoundError` if it receives the name of a file that
+does not exist.
+
+- - -
+
+#### `tf.errors.NotFoundError.__init__(node_def, op, message)` {#NotFoundError.__init__}
+
+Creates a `NotFoundError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.errors.ResourceExhaustedError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.errors.ResourceExhaustedError.md
deleted file mode 100644
index a01e255be5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.errors.ResourceExhaustedError.md
+++ /dev/null
@@ -1,12 +0,0 @@
-Some resource has been exhausted.
-
-For example, this error might be raised if a per-user quota is
-exhausted, or perhaps the entire file system is out of space.
-
-- - -
-
-#### `tf.errors.ResourceExhaustedError.__init__(node_def, op, message)` {#ResourceExhaustedError.__init__}
-
-Creates a `ResourceExhaustedError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.expand_dims.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.expand_dims.md
deleted file mode 100644
index a188cda506..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.expand_dims.md
+++ /dev/null
@@ -1,50 +0,0 @@
-### `tf.expand_dims(input, dim, name=None)` {#expand_dims}
-
-Inserts a dimension of 1 into a tensor's shape.
-
-Given a tensor `input`, this operation inserts a dimension of 1 at the
-dimension index `dim` of `input`'s shape. The dimension index `dim` starts at
-zero; if you specify a negative number for `dim` it is counted backward from
-the end.
-
-This operation is useful if you want to add a batch dimension to a single
-element. For example, if you have a single image of shape `[height, width,
-channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
-which will make the shape `[1, height, width, channels]`.
-
-Other examples:
-
-```prettyprint
-# 't' is a tensor of shape [2]
-shape(expand_dims(t, 0)) ==> [1, 2]
-shape(expand_dims(t, 1)) ==> [2, 1]
-shape(expand_dims(t, -1)) ==> [2, 1]
-
-# 't2' is a tensor of shape [2, 3, 5]
-shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
-shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
-shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
-```
-
-This operation requires that:
-
-`-1-input.dims() <= dim <= input.dims()`
-
-This operation is related to `squeeze()`, which removes dimensions of
-size 1.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`.
-*  <b>`dim`</b>: A `Tensor` of type `int32`.
-    0-D (scalar). Specifies the dimension index at which to
-    expand the shape of `input`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-  Contains the same data as `input`, but its shape has an additional
-  dimension of size 1 added.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.fft.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.fft.md
deleted file mode 100644
index 5a2c3c635d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.fft.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.fft(input, name=None)` {#fft}
-
-Compute the 1-dimensional discrete Fourier Transform.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 vector.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`. The 1D Fourier Transform of `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.fill.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.fill.md
deleted file mode 100644
index b6e51fa634..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.fill.md
+++ /dev/null
@@ -1,26 +0,0 @@
-### `tf.fill(dims, value, name=None)` {#fill}
-
-Creates a tensor filled with a scalar value.
-
-This operation creates a tensor of shape `dims` and fills it with `value`.
-
-For example:
-
-```prettyprint
-# Output tensor has shape [2, 3].
-fill([2, 3], 9) ==> [[9, 9, 9]
-                     [9, 9, 9]]
-```
-
-##### Args:
-
-
-*  <b>`dims`</b>: A `Tensor` of type `int32`.
-    1-D. Represents the shape of the output tensor.
-*  <b>`value`</b>: A `Tensor`. 0-D (scalar). Value to fill the returned tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `value`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.foldl.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.foldl.md
new file mode 100644
index 0000000000..dac4268165
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.foldl.md
@@ -0,0 +1,44 @@
+### `tf.foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#foldl}
+
+foldl on the list of tensors unpacked from `elems` on dimension 0.
+
+This foldl operator repeatedly applies the callable `fn` to a sequence
+of elements from first to last. The elements are made of the tensors
+unpacked from `elems` on dimension 0. The callable fn takes two tensors as
+arguments. The first argument is the accumulated value computed from the
+preceding invocation of fn. If `initializer` is None, `elems` must contain
+at least one element, and its first element is used as the initializer.
+
+Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
+of the result tensor is fn(initializer, values[0]).shape`.
+
+##### Args:
+
+
+*  <b>`fn`</b>: The callable to be performed.
+*  <b>`elems`</b>: A tensor to be unpacked on dimension 0.
+*  <b>`initializer`</b>: (optional) The initial value for the accumulator.
+*  <b>`parallel_iterations`</b>: (optional) The number of iterations allowed to run
+                       in parallel.
+*  <b>`back_prop`</b>: (optional) True enables back propagation.
+*  <b>`swap_memory`</b>: (optional) True enables GPU-CPU memory swapping.
+*  <b>`name`</b>: (optional) Name prefix for the returned tensors.
+
+##### Returns:
+
+  A tensor resulting from applying `fn` consecutively to the list of tensors
+  unpacked from `elems`, from first to last.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `fn` is not callable.
+
+##### Example:
+
+  ```python
+  elems = [1, 2, 3, 4, 5, 6]
+  sum = foldl(lambda a, x: a + x, elems)
+  # sum == 21
+  ```
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.foldr.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.foldr.md
new file mode 100644
index 0000000000..0a75190c04
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.foldr.md
@@ -0,0 +1,44 @@
+### `tf.foldr(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#foldr}
+
+foldr on the list of tensors unpacked from `elems` on dimension 0.
+
+This foldr operator repeatedly applies the callable `fn` to a sequence
+of elements from last to first. The elements are made of the tensors
+unpacked from `elems`. The callable fn takes two tensors as arguments.
+The first argument is the accumulated value computed from the preceding
+invocation of fn. If `initializer` is None, `elems` must contain at least
+one element, and its first element is used as the initializer.
+
+Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
+of the result tensor is `fn(initializer, values[0]).shape`.
+
+##### Args:
+
+
+*  <b>`fn`</b>: The callable to be performed.
+*  <b>`elems`</b>: A tensor that is unpacked into a sequence of tensors to apply `fn`.
+*  <b>`initializer`</b>: (optional) The initial value for the accumulator.
+*  <b>`parallel_iterations`</b>: (optional) The number of iterations allowed to run
+                       in parallel.
+*  <b>`back_prop`</b>: (optional) True enables back propagation.
+*  <b>`swap_memory`</b>: (optional) True enables GPU-CPU memory swapping.
+*  <b>`name`</b>: (optional) Name prefix for the returned tensors.
+
+##### Returns:
+
+  A tensor resulting from applying `fn` consecutively to the list of tensors
+  unpacked from `elems`, from last to first.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `fn` is not callable.
+
+##### Example:
+
+  ```python
+  elems = [1, 2, 3, 4, 5, 6]
+  sum = foldr(lambda a, x: a + x, elems)
+  # sum == 21
+  ```
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.get_collection.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.get_collection.md
deleted file mode 100644
index fc0044b490..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.get_collection.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.get_collection(key, scope=None)` {#get_collection}
-
-Wrapper for `Graph.get_collection()` using the default graph.
-
-See [`Graph.get_collection()`](../../api_docs/python/framework.md#Graph.get_collection)
-for more details.
-
-##### Args:
-
-
-*  <b>`key`</b>: The key for the collection. For example, the `GraphKeys` class
-    contains many standard names for collections.
-*  <b>`scope`</b>: (Optional.) If supplied, the resulting list is filtered to include
-    only items whose `name` attribute matches using `re.match`. Items
-    without a `name` attribute are never returned if a scope is supplied and
-    the choice or `re.match` means that a `scope` without special tokens
-    filters by prefix.
-
-##### Returns:
-
-  The list of values in the collection with the given `name`, or
-  an empty list if no value has been added to that collection. The
-  list contains the values in the order under which they were
-  collected.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.get_variable_scope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.get_variable_scope.md
new file mode 100644
index 0000000000..4a0d3bc775
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.get_variable_scope.md
@@ -0,0 +1,4 @@
+### `tf.get_variable_scope()` {#get_variable_scope}
+
+Returns the current variable scope.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.greater_equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.greater_equal.md
new file mode 100644
index 0000000000..9d68429c36
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.greater_equal.md
@@ -0,0 +1,15 @@
+### `tf.greater_equal(x, y, name=None)` {#greater_equal}
+
+Returns the truth value of (x >= y) element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.histogram_fixed_width.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.histogram_fixed_width.md
deleted file mode 100644
index 4a2997103b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.histogram_fixed_width.md
+++ /dev/null
@@ -1,38 +0,0 @@
-### `tf.histogram_fixed_width(values, value_range, nbins=100, dtype=tf.int32, name=None)` {#histogram_fixed_width}
-
-Return histogram of values.
-
-Given the tensor `values`, this operation returns a rank 1 histogram counting
-the number of entries in `values` that fell into every bin.  The bins are
-equal width and determined by the arguments `value_range` and `nbins`.
-
-##### Args:
-
-
-*  <b>`values`</b>: Numeric `Tensor`.
-*  <b>`value_range`</b>: Shape [2] `Tensor`.  new_values <= value_range[0] will be
-    mapped to hist[0], values >= value_range[1] will be mapped to hist[-1].
-    Must be same dtype as new_values.
-*  <b>`nbins`</b>: Scalar `int32 Tensor`.  Number of histogram bins.
-*  <b>`dtype`</b>: dtype for returned histogram.
-*  <b>`name`</b>: A name for this operation (defaults to 'histogram_fixed_width').
-
-##### Returns:
-
-  A 1-D `Tensor` holding histogram of values.
-
-
-*  <b>`Examples`</b>: 
-
-```python
-# Bins will be:  (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
-nbins = 5
-value_range = [0.0, 5.0]
-new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
-
-with tf.default_session() as sess:
-  hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
-  variables.initialize_all_variables().run()
-  sess.run(hist) => [2, 1, 1, 0, 2]
-```
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.igamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.igamma.md
new file mode 100644
index 0000000000..1cf6860651
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.igamma.md
@@ -0,0 +1,29 @@
+### `tf.igamma(a, x, name=None)` {#igamma}
+
+Compute the lower regularized incomplete Gamma function `Q(a, x)`.
+
+The lower regularized incomplete Gamma function is defined as:
+
+```
+P(a, x) = gamma(a, x) / Gamma(x) = 1 - Q(a, x)
+```
+where
+```
+gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt
+```
+is the lower incomplete Gamma function.
+
+Note, above `Q(a, x)` (`Igammac`) is the upper regularized complete
+Gamma function.
+
+##### Args:
+
+
+*  <b>`a`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`x`</b>: A `Tensor`. Must have the same type as `a`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `a`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.adjust_brightness.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.adjust_brightness.md
deleted file mode 100644
index 7743f0180c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.adjust_brightness.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.image.adjust_brightness(image, delta)` {#adjust_brightness}
-
-Adjust the brightness of RGB or Grayscale images.
-
-This is a convenience method that converts an RGB image to float
-representation, adjusts its brightness, and then converts it back to the
-original data type. If several adjustments are chained it is advisable to
-minimize the number of redundant conversions.
-
-The value `delta` is added to all components of the tensor `image`. Both
-`image` and `delta` are converted to `float` before adding (and `image` is
-scaled appropriately if it is in fixed-point representation). For regular
-images, `delta` should be in the range `[0,1)`, as it is added to the image in
-floating point representation, where pixel values are in the `[0,1)` range.
-
-##### Args:
-
-
-*  <b>`image`</b>: A tensor.
-*  <b>`delta`</b>: A scalar. Amount to add to the pixel values.
-
-##### Returns:
-
-  A brightness-adjusted tensor of the same shape and type as `image`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.draw_bounding_boxes.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.draw_bounding_boxes.md
deleted file mode 100644
index 0e1c6115c7..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.draw_bounding_boxes.md
+++ /dev/null
@@ -1,32 +0,0 @@
-### `tf.image.draw_bounding_boxes(images, boxes, name=None)` {#draw_bounding_boxes}
-
-Draw bounding boxes on a batch of images.
-
-Outputs a copy of `images` but draws on top of the pixels zero or more bounding
-boxes specified by the locations in `boxes`. The coordinates of the each
-bounding box in `boxes are encoded as `[y_min, x_min, y_max, x_max]`. The
-bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
-height of the underlying image.
-
-For example, if an image is 100 x 200 pixels and the bounding box is
-`[0.1, 0.5, 0.2, 0.9]`, the bottom-left and upper-right coordinates of the
-bounding box will be `(10, 40)` to `(50, 180)`.
-
-Parts of the bounding box may fall outside the image.
-
-##### Args:
-
-
-*  <b>`images`</b>: A `Tensor`. Must be one of the following types: `float32`, `half`.
-    4-D with shape `[batch, height, width, depth]`. A batch of images.
-*  <b>`boxes`</b>: A `Tensor` of type `float32`.
-    3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding
-    boxes.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `images`.
-  4-D with the same shape as `images`. The batch of input images with
-  bounding boxes drawn on the images.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.grayscale_to_rgb.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.grayscale_to_rgb.md
new file mode 100644
index 0000000000..755b66141b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.grayscale_to_rgb.md
@@ -0,0 +1,17 @@
+### `tf.image.grayscale_to_rgb(images, name=None)` {#grayscale_to_rgb}
+
+Converts one or more images from Grayscale to RGB.
+
+Outputs a tensor of the same `DType` and rank as `images`.  The size of the
+last dimension of the output is 3, containing the RGB value of the pixels.
+
+##### Args:
+
+
+*  <b>`images`</b>: The Grayscale tensor to convert. Last dimension must be size 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The converted grayscale image(s).
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.random_flip_left_right.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.random_flip_left_right.md
deleted file mode 100644
index d063895136..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.random_flip_left_right.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.image.random_flip_left_right(image, seed=None)` {#random_flip_left_right}
-
-Randomly flip an image horizontally (left to right).
-
-With a 1 in 2 chance, outputs the contents of `image` flipped along the
-second dimension, which is `width`.  Otherwise output the image as-is.
-
-##### Args:
-
-
-*  <b>`image`</b>: A 3-D tensor of shape `[height, width, channels].`
-*  <b>`seed`</b>: A Python integer. Used to create a random seed. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-
-##### Returns:
-
-  A 3-D tensor of the same type and shape as `image`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if the shape of `image` not supported.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.random_hue.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.random_hue.md
deleted file mode 100644
index 09a4ebc17f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.random_hue.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.image.random_hue(image, max_delta, seed=None)` {#random_hue}
-
-Adjust the hue of an RGB image by a random factor.
-
-Equivalent to `adjust_hue()` but uses a `delta` randomly
-picked in the interval `[-max_delta, max_delta]`.
-
-`max_delta` must be in the interval `[0, 0.5]`.
-
-##### Args:
-
-
-*  <b>`image`</b>: RGB image or images. Size of the last dimension must be 3.
-*  <b>`max_delta`</b>: float.  Maximum value for the random delta.
-*  <b>`seed`</b>: An operation-specific seed. It will be used in conjunction
-    with the graph-level seed to determine the real seeds that will be
-    used in this operation. Please see the documentation of
-    set_random_seed for its interaction with the graph-level random seed.
-
-##### Returns:
-
-  3-D float tensor of shape `[height, width, channels]`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `max_delta` is invalid.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.random_saturation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.random_saturation.md
new file mode 100644
index 0000000000..397bfc4d0b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.image.random_saturation.md
@@ -0,0 +1,27 @@
+### `tf.image.random_saturation(image, lower, upper, seed=None)` {#random_saturation}
+
+Adjust the saturation of an RGB image by a random factor.
+
+Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly
+picked in the interval `[lower, upper]`.
+
+##### Args:
+
+
+*  <b>`image`</b>: RGB image or images. Size of the last dimension must be 3.
+*  <b>`lower`</b>: float.  Lower bound for the random saturation factor.
+*  <b>`upper`</b>: float.  Upper bound for the random saturation factor.
+*  <b>`seed`</b>: An operation-specific seed. It will be used in conjunction
+    with the graph-level seed to determine the real seeds that will be
+    used in this operation. Please see the documentation of
+    set_random_seed for its interaction with the graph-level random seed.
+
+##### Returns:
+
+  Adjusted image(s), same shape and DType as `image`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `upper <= lower` or if `lower < 0`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_finite.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_finite.md
deleted file mode 100644
index db038e9919..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_finite.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.is_finite(x, name=None)` {#is_finite}
-
-Returns which elements of x are finite.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_inf.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_inf.md
new file mode 100644
index 0000000000..8955d5c9cc
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_inf.md
@@ -0,0 +1,14 @@
+### `tf.is_inf(x, name=None)` {#is_inf}
+
+Returns which elements of x are Inf.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_numeric_tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_numeric_tensor.md
deleted file mode 100644
index c2e61b856d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_numeric_tensor.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.is_numeric_tensor(tensor)` {#is_numeric_tensor}
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_strictly_increasing.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_strictly_increasing.md
deleted file mode 100644
index bdaedd519e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_strictly_increasing.md
+++ /dev/null
@@ -1,26 +0,0 @@
-### `tf.is_strictly_increasing(x, name=None)` {#is_strictly_increasing}
-
-Returns `True` if `x` is strictly increasing.
-
-Elements of `x` are compared in row-major order.  The tensor `[x[0],...]`
-is strictly increasing if for every adjacent pair we have `x[i] < x[i+1]`.
-If `x` has less than two elements, it is trivially strictly increasing.
-
-See also:  `is_non_decreasing`
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`name`</b>: A name for this operation (optional).
-    Defaults to "is_strictly_increasing"
-
-##### Returns:
-
-  Boolean `Tensor`, equal to `True` iff `x` is strictly increasing.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `x` is not a numeric tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_variable_initialized.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_variable_initialized.md
deleted file mode 100644
index d8383439ab..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.is_variable_initialized.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.is_variable_initialized(variable)` {#is_variable_initialized}
-
-Tests if a variable has been initialized.
-
-##### Args:
-
-
-*  <b>`variable`</b>: A `Variable`.
-
-##### Returns:
-
-  Returns a scalar boolean Tensor, `True` if the variable has been
-  initialized, `False` otherwise.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.lbeta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.lbeta.md
deleted file mode 100644
index e3ee18dfb3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.lbeta.md
+++ /dev/null
@@ -1,31 +0,0 @@
-### `tf.lbeta(x, name='lbeta')` {#lbeta}
-
-Computes `ln(|Beta(x)|)`, reducing along the last dimension.
-
-Given one-dimensional `z = [z_0,...,z_{K-1}]`, we define
-
-```Beta(z) = \prod_j Gamma(z_j) / Gamma(\sum_j z_j)```
-
-And for `n + 1` dimensional `x` with shape `[N1, ..., Nn, K]`, we define
-`lbeta(x)[i1, ..., in] = Log(|Beta(x[i1, ..., in, :])|)`.  In other words,
-the last dimension is treated as the `z` vector.
-
-Note that if `z = [u, v]`, then
-`Beta(z) = int_0^1 t^{u-1} (1 - t)^{v-1} dt`, which defines the traditional
-bivariate beta function.
-
-##### Args:
-
-
-*  <b>`x`</b>: A rank `n + 1` `Tensor` with type `float`, or `double`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The logarithm of `|Beta(x)|` reducing along the last dimension.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `x` is empty with rank one or less.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.listdiff.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.listdiff.md
deleted file mode 100644
index 1f04bd8d9e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.listdiff.md
+++ /dev/null
@@ -1,40 +0,0 @@
-### `tf.listdiff(x, y, name=None)` {#listdiff}
-
-Computes the difference between two lists of numbers or strings.
-
-Given a list `x` and a list `y`, this operation returns a list `out` that
-represents all values that are in `x` but not in `y`. The returned list `out`
-is sorted in the same order that the numbers appear in `x` (duplicates are
-preserved). This operation also returns a list `idx` that represents the
-position of each `out` element in `x`. In other words:
-
-`out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1]`
-
-For example, given this input:
-
-```prettyprint
-x = [1, 2, 3, 4, 5, 6]
-y = [1, 3, 5]
-```
-
-This operation would return:
-
-```prettyprint
-out ==> [2, 4, 6]
-idx ==> [1, 3, 5]
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. 1-D. Values to keep.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`. 1-D. Values to remove.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of `Tensor` objects (out, idx).
-
-*  <b>`out`</b>: A `Tensor`. Has the same type as `x`. 1-D. Values present in `x` but not in `y`.
-*  <b>`idx`</b>: A `Tensor` of type `int32`. 1-D. Positions of `x` values preserved in `out`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.load_op_library.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.load_op_library.md
deleted file mode 100644
index 0f38dfe4d5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.load_op_library.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.load_op_library(library_filename)` {#load_op_library}
-
-Loads a TensorFlow plugin, containing custom ops and kernels.
-
-Pass "library_filename" to a platform-specific mechanism for dynamically
-loading a library. The rules for determining the exact location of the
-library are platform-specific and are not documented here.
-
-##### Args:
-
-
-*  <b>`library_filename`</b>: Path to the plugin.
-    Relative or absolute filesystem path to a dynamic library file.
-
-##### Returns:
-
-  A python module containing the Python wrappers for Ops defined in
-  the plugin.
-
-##### Raises:
-
-
-*  <b>`RuntimeError`</b>: when unable to load the library or get the python wrappers.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.matrix_determinant.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.matrix_determinant.md
deleted file mode 100644
index a5cd5a7fe6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.matrix_determinant.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.matrix_determinant(input, name=None)` {#matrix_determinant}
-
-Calculates the determinant of a square matrix.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-    A tensor of shape `[M, M]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-  A scalar, equal to the determinant of the input.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.moving_average_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.moving_average_variables.md
new file mode 100644
index 0000000000..467a666e2c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.moving_average_variables.md
@@ -0,0 +1,13 @@
+### `tf.moving_average_variables()` {#moving_average_variables}
+
+Returns all variables that maintain their moving averages.
+
+If an `ExponentialMovingAverage` object is created and the `apply()`
+method is called on a list of variables, these variables will
+be added to the `GraphKeys.MOVING_AVERAGE_VARIABLES` collection.
+This convenience function returns the contents of that collection.
+
+##### Returns:
+
+  A list of Variable objects.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.avg_pool3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.avg_pool3d.md
deleted file mode 100644
index 76503e0567..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.avg_pool3d.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.nn.avg_pool3d(input, ksize, strides, padding, name=None)` {#avg_pool3d}
-
-Performs 3D average pooling on the input.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-    Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
-*  <b>`ksize`</b>: A list of `ints` that has length `>= 5`.
-    1-D tensor of length 5. The size of the window for each dimension of
-    the input tensor. Must have `ksize[0] = ksize[1] = 1`.
-*  <b>`strides`</b>: A list of `ints` that has length `>= 5`.
-    1-D tensor of length 5. The stride of the sliding window for each
-    dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
-    The type of padding algorithm to use.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-  The average pooled output tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.conv2d.md
deleted file mode 100644
index 684a3d5727..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.conv2d.md
+++ /dev/null
@@ -1,49 +0,0 @@
-### `tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)` {#conv2d}
-
-Computes a 2-D convolution given 4-D `input` and `filter` tensors.
-
-Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
-and a filter / kernel tensor of shape
-`[filter_height, filter_width, in_channels, out_channels]`, this op
-performs the following:
-
-1. Flattens the filter to a 2-D matrix with shape
-   `[filter_height * filter_width * in_channels, output_channels]`.
-2. Extracts image patches from the input tensor to form a *virtual*
-   tensor of shape `[batch, out_height, out_width,
-   filter_height * filter_width * in_channels]`.
-3. For each patch, right-multiplies the filter matrix and the image patch
-   vector.
-
-In detail, with the default NHWC format,
-
-    output[b, i, j, k] =
-        sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
-                        filter[di, dj, q, k]
-
-Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
-horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-*  <b>`filter`</b>: A `Tensor`. Must have the same type as `input`.
-*  <b>`strides`</b>: A list of `ints`.
-    1-D of length 4.  The stride of the sliding window for each dimension
-    of `input`. Must be in the same order as the dimension specified with format.
-*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
-    The type of padding algorithm to use.
-*  <b>`use_cudnn_on_gpu`</b>: An optional `bool`. Defaults to `True`.
-*  <b>`data_format`</b>: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`.
-    Specify the data format of the input and output data. With the
-    default format "NHWC", the data is stored in the order of:
-        [batch, in_height, in_width, in_channels].
-    Alternatively, the format could be "NCHW", the data storage order of:
-        [batch, in_channels, in_height, in_width].
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.conv2d_transpose.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.conv2d_transpose.md
deleted file mode 100644
index ee459ae0e6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.conv2d_transpose.md
+++ /dev/null
@@ -1,34 +0,0 @@
-### `tf.nn.conv2d_transpose(value, filter, output_shape, strides, padding='SAME', name=None)` {#conv2d_transpose}
-
-The transpose of `conv2d`.
-
-This operation is sometimes called "deconvolution" after [Deconvolutional
-Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is
-actually the transpose (gradient) of `conv2d` rather than an actual
-deconvolution.
-
-##### Args:
-
-
-*  <b>`value`</b>: A 4-D `Tensor` of type `float` and shape
-    `[batch, height, width, in_channels]`.
-*  <b>`filter`</b>: A 4-D `Tensor` with the same type as `value` and shape
-    `[height, width, output_channels, in_channels]`.  `filter`'s
-    `in_channels` dimension must match that of `value`.
-*  <b>`output_shape`</b>: A 1-D `Tensor` representing the output shape of the
-    deconvolution op.
-*  <b>`strides`</b>: A list of ints. The stride of the sliding window for each
-    dimension of the input tensor.
-*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
-*  <b>`name`</b>: Optional name for the returned tensor.
-
-##### Returns:
-
-  A `Tensor` with the same type as `value`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If input/output depth does not match `filter`'s shape, or if
-    padding is other than `'VALID'` or `'SAME'`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.embedding_lookup.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.embedding_lookup.md
deleted file mode 100644
index 588c2b393d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.embedding_lookup.md
+++ /dev/null
@@ -1,50 +0,0 @@
-### `tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None, validate_indices=True)` {#embedding_lookup}
-
-Looks up `ids` in a list of embedding tensors.
-
-This function is used to perform parallel lookups on the list of
-tensors in `params`.  It is a generalization of
-[`tf.gather()`](../../api_docs/python/array_ops.md#gather), where `params` is
-interpreted as a partition of a larger embedding tensor.
-
-If `len(params) > 1`, each element `id` of `ids` is partitioned between
-the elements of `params` according to the `partition_strategy`.
-In all strategies, if the id space does not evenly divide the number of
-partitions, each of the first `(max_id + 1) % len(params)` partitions will
-be assigned one more id.
-
-If `partition_strategy` is `"mod"`, we assign each id to partition
-`p = id % len(params)`. For instance,
-13 ids are split across 5 partitions as:
-`[[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]]`
-
-If `partition_strategy` is `"div"`, we assign ids to partitions in a
-contiguous manner. In this case, 13 ids are split across 5 partitions as:
-`[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]`
-
-The results of the lookup are concatenated into a dense
-tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`.
-
-##### Args:
-
-
-*  <b>`params`</b>: A list of tensors with the same type and which can be concatenated
-    along dimension 0. Each `Tensor` must be appropriately sized for the given
-    `partition_strategy`.
-*  <b>`ids`</b>: A `Tensor` with type `int32` or `int64` containing the ids to be looked
-    up in `params`.
-*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy, relevant
-    if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default
-    is `"mod"`.
-*  <b>`name`</b>: A name for the operation (optional).
-*  <b>`validate_indices`</b>: Whether or not to validate gather indices.
-
-##### Returns:
-
-  A `Tensor` with the same type as the tensors in `params`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `params` is empty.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.embedding_lookup_sparse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.embedding_lookup_sparse.md
deleted file mode 100644
index 03997f7813..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.embedding_lookup_sparse.md
+++ /dev/null
@@ -1,66 +0,0 @@
-### `tf.nn.embedding_lookup_sparse(params, sp_ids, sp_weights, partition_strategy='mod', name=None, combiner='mean')` {#embedding_lookup_sparse}
-
-Computes embeddings for the given ids and weights.
-
-This op assumes that there is at least one id for each row in the dense tensor
-represented by sp_ids (i.e. there are no rows with empty features), and that
-all the indices of sp_ids are in canonical row-major order.
-
-It also assumes that all id values lie in the range [0, p0), where p0
-is the sum of the size of params along dimension 0.
-
-##### Args:
-
-
-*  <b>`params`</b>: A single tensor representing the complete embedding tensor,
-    or a list of P tensors all of same shape except for the first dimension,
-    representing sharded embedding tensors.
-*  <b>`sp_ids`</b>: N x M SparseTensor of int64 ids (typically from FeatureValueToId),
-    where N is typically batch size and M is arbitrary.
-*  <b>`sp_weights`</b>: either a SparseTensor of float / double weights, or None to
-    indicate all weights should be taken to be 1. If specified, sp_weights
-    must have exactly the same shape and indices as sp_ids.
-*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy, relevant
-    if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default
-    is `"mod"`. See `tf.nn.embedding_lookup` for more details.
-*  <b>`name`</b>: Optional name for the op.
-*  <b>`combiner`</b>: A string specifying the reduction op. Currently "mean", "sqrtn"
-    and "sum" are supported.
-    "sum" computes the weighted sum of the embedding results for each row.
-    "mean" is the weighted sum divided by the total weight.
-    "sqrtn" is the weighted sum divided by the square root of the sum of the
-    squares of the weights.
-
-##### Returns:
-
-  A dense tensor representing the combined embeddings for the
-  sparse ids. For each row in the dense tensor represented by sp_ids, the op
-  looks up the embeddings for all ids in that row, multiplies them by the
-  corresponding weight, and combines these embeddings as specified.
-
-  In other words, if
-    shape(combined params) = [p0, p1, ..., pm]
-  and
-    shape(sp_ids) = shape(sp_weights) = [d0, d1, ..., dn]
-  then
-    shape(output) = [d0, d1, ..., dn-1, p1, ..., pm].
-
-  For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are
-
-    [0, 0]: id 1, weight 2.0
-    [0, 1]: id 3, weight 0.5
-    [1, 0]: id 0, weight 1.0
-    [2, 3]: id 1, weight 3.0
-
-  with combiner="mean", then the output will be a 3x20 matrix where
-    output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5)
-    output[1, :] = params[0, :] * 1.0
-    output[2, :] = params[1, :] * 3.0
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If sp_ids is not a SparseTensor, or if sp_weights is neither
-    None nor SparseTensor.
-*  <b>`ValueError`</b>: If combiner is not one of {"mean", "sqrtn", "sum"}.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.l2_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.l2_loss.md
new file mode 100644
index 0000000000..fd648ca642
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.l2_loss.md
@@ -0,0 +1,19 @@
+### `tf.nn.l2_loss(t, name=None)` {#l2_loss}
+
+L2 Loss.
+
+Computes half the L2 norm of a tensor without the `sqrt`:
+
+    output = sum(t ** 2) / 2
+
+##### Args:
+
+
+*  <b>`t`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+    Typically 2-D, but may have any dimensions.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `t`. 0-D.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.log_softmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.log_softmax.md
deleted file mode 100644
index 18e1f96590..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.log_softmax.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.nn.log_softmax(logits, name=None)` {#log_softmax}
-
-Computes log softmax activations.
-
-For each batch `i` and class `j` we have
-
-    logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
-
-##### Args:
-
-
-*  <b>`logits`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-    2-D with shape `[batch_size, num_classes]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `logits`. Same shape as `logits`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.log_uniform_candidate_sampler.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.log_uniform_candidate_sampler.md
new file mode 100644
index 0000000000..baf9f9d421
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.log_uniform_candidate_sampler.md
@@ -0,0 +1,56 @@
+### `tf.nn.log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)` {#log_uniform_candidate_sampler}
+
+Samples a set of classes using a log-uniform (Zipfian) base distribution.
+
+This operation randomly samples a tensor of sampled classes
+(`sampled_candidates`) from the range of integers `[0, range_max)`.
+
+The elements of `sampled_candidates` are drawn without replacement
+(if `unique=True`) or with replacement (if `unique=False`) from
+the base distribution.
+
+The base distribution for this operation is an approximately log-uniform
+or Zipfian distribution:
+
+`P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)`
+
+This sampler is useful when the target classes approximately follow such
+a distribution - for example, if the classes represent words in a lexicon
+sorted in decreasing order of frequency. If your classes are not ordered by
+decreasing frequency, do not use this op.
+
+In addition, this operation returns tensors `true_expected_count`
+and `sampled_expected_count` representing the number of times each
+of the target classes (`true_classes`) and the sampled
+classes (`sampled_candidates`) is expected to occur in an average
+tensor of sampled classes.  These values correspond to `Q(y|x)`
+defined in [this
+document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
+If `unique=True`, then these are post-rejection probabilities and we
+compute them approximately.
+
+##### Args:
+
+
+*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
+    num_true]`. The target classes.
+*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
+*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
+*  <b>`unique`</b>: A `bool`. Determines whether all sampled classes in a batch are
+    unique.
+*  <b>`range_max`</b>: An `int`. The number of possible classes.
+*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+
+*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
+    The sampled classes.
+*  <b>`true_expected_count`</b>: A tensor of type `float`.  Same shape as
+    `true_classes`. The expected counts under the sampling distribution
+    of each of `true_classes`.
+*  <b>`sampled_expected_count`</b>: A tensor of type `float`. Same shape as
+    `sampled_candidates`. The expected counts under the sampling distribution
+    of each of `sampled_candidates`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.max_pool3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.max_pool3d.md
deleted file mode 100644
index 471fcb532f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.max_pool3d.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.nn.max_pool3d(input, ksize, strides, padding, name=None)` {#max_pool3d}
-
-Performs 3D max pooling on the input.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-    Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
-*  <b>`ksize`</b>: A list of `ints` that has length `>= 5`.
-    1-D tensor of length 5. The size of the window for each dimension of
-    the input tensor. Must have `ksize[0] = ksize[1] = 1`.
-*  <b>`strides`</b>: A list of `ints` that has length `>= 5`.
-    1-D tensor of length 5. The stride of the sliding window for each
-    dimension of `input`. Must have `strides[0] = strides[4] = 1`.
-*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
-    The type of padding algorithm to use.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`. The max pooled output tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.zero_fraction.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.zero_fraction.md
deleted file mode 100644
index f4d126a041..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.nn.zero_fraction.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.nn.zero_fraction(value, name=None)` {#zero_fraction}
-
-Returns the fraction of zeros in `value`.
-
-If `value` is empty, the result is `nan`.
-
-This is useful in summaries to measure and report sparsity.  For example,
-
-    z = tf.Relu(...)
-    summ = tf.scalar_summary('sparsity', tf.nn.zero_fraction(z))
-
-##### Args:
-
-
-*  <b>`value`</b>: A tensor of numeric type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The fraction of zeros in `value`, with type `float32`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.ones_like.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.ones_like.md
new file mode 100644
index 0000000000..2c9b04ceca
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.ones_like.md
@@ -0,0 +1,28 @@
+### `tf.ones_like(tensor, dtype=None, name=None)` {#ones_like}
+
+Creates a tensor with all elements set to 1.
+
+Given a single tensor (`tensor`), this operation returns a tensor of the same
+type and shape as `tensor` with all elements set to 1. Optionally, you can
+specify a new type (`dtype`) for the returned tensor.
+
+For example:
+
+```python
+# 'tensor' is [[1, 2, 3], [4, 5, 6]]
+tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]
+```
+
+##### Args:
+
+
+*  <b>`tensor`</b>: A `Tensor`.
+*  <b>`dtype`</b>: A type for the returned `Tensor`. Must be `float32`, `float64`,
+  `int8`, `int16`, `int32`, `int64`, `uint8`, or `complex64`.
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` with all elements set to 1.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.placeholder_with_default.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.placeholder_with_default.md
new file mode 100644
index 0000000000..2719b876f1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.placeholder_with_default.md
@@ -0,0 +1,17 @@
+### `tf.placeholder_with_default(input, shape, name=None)` {#placeholder_with_default}
+
+A placeholder op that passes though `input` when its output is not fed.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. The default value to produce when `output` is not fed.
+*  <b>`shape`</b>: A `tf.TensorShape` or list of `ints`.
+    The (possibly partial) shape of the tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+  A placeholder tensor that defaults to `input` if it is not fed.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.python_io.TFRecordWriter.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.python_io.TFRecordWriter.md
deleted file mode 100644
index 4a67724209..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.python_io.TFRecordWriter.md
+++ /dev/null
@@ -1,41 +0,0 @@
-A class to write records to a TFRecords file.
-
-This class implements `__enter__` and `__exit__`, and can be used
-in `with` blocks like a normal file.
-
-- - -
-
-#### `tf.python_io.TFRecordWriter.__init__(path)` {#TFRecordWriter.__init__}
-
-Opens file `path` and creates a `TFRecordWriter` writing to it.
-
-##### Args:
-
-
-*  <b>`path`</b>: The path to the TFRecords file.
-
-##### Raises:
-
-
-*  <b>`IOError`</b>: If `path` cannot be opened for writing.
-
-
-- - -
-
-#### `tf.python_io.TFRecordWriter.write(record)` {#TFRecordWriter.write}
-
-Write a string record to the file.
-
-##### Args:
-
-
-*  <b>`record`</b>: str
-
-
-- - -
-
-#### `tf.python_io.TFRecordWriter.close()` {#TFRecordWriter.close}
-
-Close the file.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.random_crop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.random_crop.md
deleted file mode 100644
index d389872919..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.random_crop.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.random_crop(value, size, seed=None, name=None)` {#random_crop}
-
-Randomly crops a tensor to a given size.
-
-Slices a shape `size` portion out of `value` at a uniformly chosen offset.
-Requires `value.shape >= size`.
-
-If a dimension should not be cropped, pass the full size of that dimension.
-For example, RGB images can be cropped with
-`size = [crop_height, crop_width, 3]`.
-
-##### Args:
-
-
-*  <b>`value`</b>: Input tensor to crop.
-*  <b>`size`</b>: 1-D tensor with size the rank of `value`.
-*  <b>`seed`</b>: Python integer. Used to create a random seed. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  A cropped tensor of the same rank as `value` and shape `size`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.random_uniform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.random_uniform.md
new file mode 100644
index 0000000000..517bdd98c4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.random_uniform.md
@@ -0,0 +1,41 @@
+### `tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)` {#random_uniform}
+
+Outputs random values from a uniform distribution.
+
+The generated values follow a uniform distribution in the range
+`[minval, maxval)`. The lower bound `minval` is included in the range, while
+the upper bound `maxval` is excluded.
+
+For floats, the default range is `[0, 1)`.  For ints, at least `maxval` must
+be specified explicitly.
+
+In the integer case, the random integers are slightly biased unless
+`maxval - minval` is an exact power of two.  The bias is small for values of
+`maxval - minval` significantly smaller than the range of the output (either
+`2**32` or `2**64`).
+
+##### Args:
+
+
+*  <b>`shape`</b>: A 1-D integer Tensor or Python array. The shape of the output tensor.
+*  <b>`minval`</b>: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
+    range of random values to generate.  Defaults to 0.
+*  <b>`maxval`</b>: A 0-D Tensor or Python value of type `dtype`. The upper bound on
+    the range of random values to generate.  Defaults to 1 if `dtype` is
+    floating point.
+*  <b>`dtype`</b>: The type of the output: `float32`, `float64`, `int32`, or `int64`.
+*  <b>`seed`</b>: A Python integer. Used to create a random seed for the distribution.
+    See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tensor of the specified shape filled with random uniform values.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `dtype` is integral and `maxval` is not specified.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.range.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.range.md
new file mode 100644
index 0000000000..c33825d3be
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.range.md
@@ -0,0 +1,37 @@
+### `tf.range(start, limit=None, delta=1, name='range')` {#range}
+
+Creates a sequence of integers.
+
+Creates a sequence of integers that begins at `start` and extends by
+increments of `delta` up to but not including `limit`.
+
+Like the Python builtin `range`, `start` defaults to 0, so that
+`range(n) = range(0, n)`.
+
+For example:
+
+```
+# 'start' is 3
+# 'limit' is 18
+# 'delta' is 3
+tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
+
+# 'limit' is 5
+tf.range(limit) ==> [0, 1, 2, 3, 4]
+```
+
+##### Args:
+
+
+*  <b>`start`</b>: A 0-D (scalar) of type `int32`. First entry in sequence.
+    Defaults to 0.
+*  <b>`limit`</b>: A 0-D (scalar) of type `int32`. Upper limit of sequence,
+    exclusive.
+*  <b>`delta`</b>: A 0-D `Tensor` (scalar) of type `int32`. Optional. Default is 1.
+    Number that increments `start`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An 1-D `int32` `Tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.rank.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.rank.md
new file mode 100644
index 0000000000..8d8fdb4af4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.rank.md
@@ -0,0 +1,28 @@
+### `tf.rank(input, name=None)` {#rank}
+
+Returns the rank of a tensor.
+
+This operation returns an integer representing the rank of `input`.
+
+For example:
+
+```prettyprint
+# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
+# shape of tensor 't' is [2, 2, 3]
+rank(t) ==> 3
+```
+
+**Note**: The rank of a tensor is not the same as the rank of a matrix. The rank
+of a tensor is the number of indices required to uniquely select each element
+of the tensor. Rank is also known as "order", "degree", or "ndims."
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `int32`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reduce_join.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reduce_join.md
new file mode 100644
index 0000000000..c65c6022ba
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reduce_join.md
@@ -0,0 +1,49 @@
+### `tf.reduce_join(inputs, reduction_indices, keep_dims=None, separator=None, name=None)` {#reduce_join}
+
+Joins a string Tensor across the given dimensions.
+
+Computes the string join across dimensions in the given string Tensor of shape
+`[d_0, d_1, ..., d_n-1]`.  Returns a new Tensor created by joining the input
+strings with the given separator (default: empty string).  Negative indices are
+counted backwards from the end, with `-1` being equivalent to `n - 1`.  Passing
+an empty `reduction_indices` joins all strings in linear index order and outputs
+a scalar string.
+
+
+For example:
+```
+# tensor `a` is [["a", "b"], ["c", "d"]]
+tf.reduce_join(a, 0) ==> ["ac", "bd"]
+tf.reduce_join(a, 1) ==> ["ab", "cd"]
+tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"]
+tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"]
+tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]]
+tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]]
+tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"]
+tf.reduce_join(a, [0, 1]) ==> ["acbd"]
+tf.reduce_join(a, [1, 0]) ==> ["abcd"]
+tf.reduce_join(a, []) ==> ["abcd"]
+```
+
+##### Args:
+
+
+*  <b>`inputs`</b>: A `Tensor` of type `string`.
+    The input to be joined.  All reduced indices must have non-zero size.
+*  <b>`reduction_indices`</b>: A `Tensor` of type `int32`.
+    The dimensions to reduce over.  Dimensions are reduced in the
+    order specified.  If `reduction_indices` has higher rank than `1`, it is
+    flattened.  Omitting `reduction_indices` is equivalent to passing
+    `[n-1, n-2, ..., 0]`.  Negative indices from `-n` to `-1` are supported.
+*  <b>`keep_dims`</b>: An optional `bool`. Defaults to `False`.
+    If `True`, retain reduced dimensions with length `1`.
+*  <b>`separator`</b>: An optional `string`. Defaults to `""`.
+    The separator to use when joining.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `string`.
+  Has shape equal to that of the input with reduced dimensions removed or
+  set to `1` depending on `keep_dims`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reduce_max.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reduce_max.md
deleted file mode 100644
index f137e8091c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reduce_max.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.reduce_max(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_max}
-
-Computes the maximum of elements across dimensions of a tensor.
-
-Reduces `input_tensor` along the dimensions given in `reduction_indices`.
-Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
-entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
-are retained with length 1.
-
-If `reduction_indices` has no entries, all dimensions are reduced, and a
-tensor with a single element is returned.
-
-##### Args:
-
-
-*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
-*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
-    reduces all dimensions.
-*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The reduced tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reduce_sum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reduce_sum.md
new file mode 100644
index 0000000000..edbb1ab055
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reduce_sum.md
@@ -0,0 +1,37 @@
+### `tf.reduce_sum(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_sum}
+
+Computes the sum of elements across dimensions of a tensor.
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+For example:
+
+```python
+# 'x' is [[1, 1, 1]
+#         [1, 1, 1]]
+tf.reduce_sum(x) ==> 6
+tf.reduce_sum(x, 0) ==> [2, 2, 2]
+tf.reduce_sum(x, 1) ==> [3, 3]
+tf.reduce_sum(x, 1, keep_dims=True) ==> [[3], [3]]
+tf.reduce_sum(x, [0, 1]) ==> 6
+```
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reverse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reverse.md
deleted file mode 100644
index e316d5faae..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.reverse.md
+++ /dev/null
@@ -1,61 +0,0 @@
-### `tf.reverse(tensor, dims, name=None)` {#reverse}
-
-Reverses specific dimensions of a tensor.
-
-Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
-of `tensor`, this operation reverses each dimension i of `tensor` where
-`dims[i]` is `True`.
-
-`tensor` can have up to 8 dimensions. The number of dimensions
-of `tensor` must equal the number of elements in `dims`. In other words:
-
-`rank(tensor) = size(dims)`
-
-For example:
-
-```prettyprint
-# tensor 't' is [[[[ 0,  1,  2,  3],
-#                  [ 4,  5,  6,  7],
-#                  [ 8,  9, 10, 11]],
-#                 [[12, 13, 14, 15],
-#                  [16, 17, 18, 19],
-#                  [20, 21, 22, 23]]]]
-# tensor 't' shape is [1, 2, 3, 4]
-
-# 'dims' is [False, False, False, True]
-reverse(t, dims) ==> [[[[ 3,  2,  1,  0],
-                        [ 7,  6,  5,  4],
-                        [ 11, 10, 9, 8]],
-                       [[15, 14, 13, 12],
-                        [19, 18, 17, 16],
-                        [23, 22, 21, 20]]]]
-
-# 'dims' is [False, True, False, False]
-reverse(t, dims) ==> [[[[12, 13, 14, 15],
-                        [16, 17, 18, 19],
-                        [20, 21, 22, 23]
-                       [[ 0,  1,  2,  3],
-                        [ 4,  5,  6,  7],
-                        [ 8,  9, 10, 11]]]]
-
-# 'dims' is [False, False, True, False]
-reverse(t, dims) ==> [[[[8, 9, 10, 11],
-                        [4, 5, 6, 7],
-                        [0, 1, 2, 3]]
-                       [[20, 21, 22, 23],
-                        [16, 17, 18, 19],
-                        [12, 13, 14, 15]]]]
-```
-
-##### Args:
-
-
-*  <b>`tensor`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int32`, `bool`, `float32`, `float64`.
-    Up to 8-D.
-*  <b>`dims`</b>: A `Tensor` of type `bool`. 1-D. The dimensions to reverse.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `tensor`. The same shape as `tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.saturate_cast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.saturate_cast.md
new file mode 100644
index 0000000000..6a77c2791e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.saturate_cast.md
@@ -0,0 +1,19 @@
+### `tf.saturate_cast(value, dtype, name=None)` {#saturate_cast}
+
+Performs a safe saturating cast of `value` to `dtype`.
+
+This function casts the input to `dtype` without applying any scaling.  If
+there is a danger that values would over or underflow in the cast, this op
+applies the appropriate clamping before the cast.
+
+##### Args:
+
+
+*  <b>`value`</b>: A `Tensor`.
+*  <b>`dtype`</b>: The desired output `DType`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  `value` safely cast to `dtype`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.scatter_add.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.scatter_add.md
deleted file mode 100644
index a8f8b7a9b0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.scatter_add.md
+++ /dev/null
@@ -1,46 +0,0 @@
-### `tf.scatter_add(ref, indices, updates, use_locking=None, name=None)` {#scatter_add}
-
-Adds sparse updates to a variable reference.
-
-This operation computes
-
-    # Scalar indices
-    ref[indices, ...] += updates[...]
-
-    # Vector indices (for each i)
-    ref[indices[i], ...] += updates[i, ...]
-
-    # High rank indices (for each i, ..., j)
-    ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
-
-This operation outputs `ref` after the update is done.
-This makes it easier to chain operations that need to use the reset value.
-
-Duplicate entries are handled correctly: if multiple `indices` reference
-the same location, their contributions add.
-
-Requires `updates.shape = indices.shape + ref.shape[1:]`.
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/ScatterAdd.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`ref`</b>: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-    Should be from a `Variable` node.
-*  <b>`indices`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    A tensor of indices into the first dimension of `ref`.
-*  <b>`updates`</b>: A `Tensor`. Must have the same type as `ref`.
-    A tensor of updated values to add to `ref`.
-*  <b>`use_locking`</b>: An optional `bool`. Defaults to `False`.
-    If True, the addition will be protected by a lock;
-    otherwise the behavior is undefined, but may exhibit less contention.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  Same as `ref`.  Returned as a convenience for operations that want
-  to use the updated values after the update is done.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.size.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.size.md
deleted file mode 100644
index 67f1bc4885..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.size.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.size(input, name=None)` {#size}
-
-Returns the size of a tensor.
-
-This operation returns an integer representing the number of elements in
-`input`.
-
-For example:
-
-```prettyprint
-# 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
-size(t) ==> 12
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `int32`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_add.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_add.md
new file mode 100644
index 0000000000..4835ae70e5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_add.md
@@ -0,0 +1,55 @@
+### `tf.sparse_add(a, b, thresh=0)` {#sparse_add}
+
+Adds two tensors, at least one of each is a `SparseTensor`.
+
+If one `SparseTensor` and one `Tensor` are passed in, returns a `Tensor`.  If
+both arguments are `SparseTensor`s, this returns a `SparseTensor`.  The order
+of arguments does not matter.  Use vanilla `tf.add()` for adding two dense
+`Tensor`s.
+
+The indices of any input `SparseTensor` are assumed ordered in standard
+lexicographic order.  If this is not the case, before this step run
+`SparseReorder` to restore index ordering.
+
+If both arguments are sparse, we perform "clipping" as follows.  By default,
+if two values sum to zero at some index, the output `SparseTensor` would still
+include that particular location in its index, storing a zero in the
+corresponding value slot.  To override this, callers can specify `thresh`,
+indicating that if the sum has a magnitude strictly smaller than `thresh`, its
+corresponding value and index would then not be included.  In particular,
+`thresh == 0.0` (default) means everything is kept and actual thresholding
+happens only for a positive value.
+
+For example, suppose the logical sum of two sparse operands is (densified):
+
+    [       2]
+    [.1     0]
+    [ 6   -.2]
+
+Then,
+
+    - thresh == 0 (the default): all 5 index/value pairs will be returned.
+    - thresh == 0.11: only .1 and 0  will vanish, and the remaining three
+        index/value pairs will be returned.
+    - thresh == 0.21: .1, 0, and -.2 will vanish.
+
+##### Args:
+
+
+*  <b>`a`</b>: The first operand; `SparseTensor` or `Tensor`.
+*  <b>`b`</b>: The second operand; `SparseTensor` or `Tensor`.  At least one operand
+    must be sparse.
+*  <b>`thresh`</b>: A 0-D `Tensor`.  The magnitude threshold that determines if an
+  output value/index pair takes space.  Its dtype should match that of the
+  values if they are real; if the latter are complex64/complex128, then the
+  dtype should be float32/float64, correspondingly.
+
+##### Returns:
+
+  A `SparseTensor` or a `Tensor`, representing the sum.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If both `a` and `b` are `Tensor`s.  Use `tf.add()` instead.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_reset_shape.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_reset_shape.md
new file mode 100644
index 0000000000..d0606cdc5d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_reset_shape.md
@@ -0,0 +1,60 @@
+### `tf.sparse_reset_shape(sp_input, new_shape=None)` {#sparse_reset_shape}
+
+Resets the shape of a `SparseTensor` with indices and values unchanged.
+
+If `new_shape` is None, returns a copy of `sp_input` with its shape reset
+to the tight bounding box of `sp_input`.
+
+If `new_shape` is provided, then it must be larger or equal in all dimensions
+compared to the shape of `sp_input`. When this condition is met, the returned
+SparseTensor will have its shape reset to `new_shape` and its indices and
+values unchanged from that of `sp_input.`
+
+For example:
+
+  Consider a `sp_input` with shape [2, 3, 5]:
+
+    [0, 0, 1]: a
+    [0, 1, 0]: b
+    [0, 2, 2]: c
+    [1, 0, 3]: d
+
+  - It is an error to set `new_shape` as [3, 7] since this represents a
+    rank-2 tensor while `sp_input` is rank-3. This is either a ValueError
+    during graph construction (if both shapes are known) or an OpError during
+    run time.
+
+  - Setting `new_shape` as [2, 3, 6] will be fine as this shape is larger or
+    eqaul in every dimension compared to the original shape [2, 3, 5].
+
+  - On the other hand, setting new_shape as [2, 3, 4] is also an error: The
+    third dimension is smaller than the original shape [2, 3, 5] (and an
+    `InvalidArgumentError` will be raised).
+
+  - If `new_shape` is None, the returned SparseTensor will have a shape
+    [2, 3, 4], which is the tight bounding box of `sp_input`.
+
+##### Args:
+
+
+*  <b>`sp_input`</b>: The input `SparseTensor`.
+*  <b>`new_shape`</b>: None or a vector representing the new shape for the returned
+    `SpraseTensor`.
+
+##### Returns:
+
+  A `SparseTensor` indices and values unchanged from `input_sp`. Its shape is
+    `new_shape` if that is set. Otherwise it is  the tight bounding box of
+     `input_sp`
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
+*  <b>`ValueError`</b>: If `new_shape` represents a tensor with a different rank from
+    that of `sp_input` (if shapes are known when graph is constructed).
+*  <b>`OpError`</b>: 
+    - If `new_shape` has dimension sizes that are too small.
+    - If shapes are not known during graph construction time, and during run
+      time it is found out that the ranks do not match.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_retain.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_retain.md
deleted file mode 100644
index dcaa303627..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_retain.md
+++ /dev/null
@@ -1,33 +0,0 @@
-### `tf.sparse_retain(sp_input, to_retain)` {#sparse_retain}
-
-Retains specified non-empty values within a `SparseTensor`.
-
-For example, if `sp_input` has shape `[4, 5]` and 4 non-empty string values:
-
-    [0, 1]: a
-    [0, 3]: b
-    [2, 0]: c
-    [3, 1]: d
-
-and `to_retain = [True, False, False, True]`, then the output will
-be a `SparseTensor` of shape `[4, 5]` with 2 non-empty values:
-
-    [0, 1]: a
-    [3, 1]: d
-
-##### Args:
-
-
-*  <b>`sp_input`</b>: The input `SparseTensor` with `N` non-empty elements.
-*  <b>`to_retain`</b>: A bool vector of length `N` with `M` true values.
-
-##### Returns:
-
-  A `SparseTensor` with the same shape as the input and `M` non-empty
-  elements corresponding to the true positions in `to_retain`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_segment_sqrt_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_segment_sqrt_n.md
deleted file mode 100644
index bc665a42a8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_segment_sqrt_n.md
+++ /dev/null
@@ -1,26 +0,0 @@
-### `tf.sparse_segment_sqrt_n(data, indices, segment_ids, name=None)` {#sparse_segment_sqrt_n}
-
-Computes the sum along sparse segments of a tensor divided by the sqrt of N.
-
-N is the size of the segment being reduced.
-
-Read [the section on
-Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
-of segments.
-
-##### Args:
-
-
-*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-*  <b>`indices`</b>: A `Tensor` of type `int32`.
-    A 1-D tensor. Has same rank as `segment_ids`.
-*  <b>`segment_ids`</b>: A `Tensor` of type `int32`.
-    A 1-D tensor. Values should be sorted and can be repeated.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `data`.
-  Has same shape as data, except for dimension 0 which
-  has size `k`, the number of segments.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_softmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_softmax.md
deleted file mode 100644
index cb54fd9452..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_softmax.md
+++ /dev/null
@@ -1,51 +0,0 @@
-### `tf.sparse_softmax(sp_input, name=None)` {#sparse_softmax}
-
-Applies softmax to a batched N-D `SparseTensor`.
-
-The inputs represent an N-D SparseTensor  with logical shape `[..., B, C]`
-(where `N >= 2`), and with indices sorted in the canonical lexicographic
-order.
-
-This op is equivalent to applying the normal `tf.nn.softmax()` to each
-innermost logical submatrix with shape `[B, C]`, but with the catch that *the
-implicitly zero elements do not participate*.  Specifically, the algorithm is
-equivalent to:
-
-  (1) Applies `tf.nn.softmax()` to a densified view of each innermost
-      submatrix with shape `[B, C]`, along the size-C dimension;
-  (2) Masks out the original implicitly-zero locations;
-  (3) Renormalizes the remaining elements.
-
-Hence, the `SparseTensor` result has exactly the same non-zero indices and
-shape.
-
-Example:
-```python
-# First batch:
-# [?   e.]
-# [1.  ? ]
-# Second batch:
-# [e   ? ]
-# [e   e ]
-shape = [2, 2, 2]  # 3-D SparseTensor
-values = np.asarray([[[0., np.e], [1., 0.]], [[np.e, 0.], [np.e, np.e]]])
-indices = np.vstack(np.where(values)).astype(np.int64).T
-
-result = tf.sparse_softmax(tf.SparseTensor(indices, values, shape))
-# ...returning a 3-D SparseTensor, equivalent to:
-# [?   1.]     [1    ?]
-# [1.  ? ] and [.5  .5]
-# where ? means implicitly zero.
-```
-
-##### Args:
-
-
-*  <b>`sp_input`</b>: N-D `SparseTensor`, where `N >= 2`.
-*  <b>`name`</b>: optional name of the operation.
-
-##### Returns:
-
-
-*  <b>`output`</b>: N-D `SparseTensor` representing the results.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_tensor_dense_matmul.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_tensor_dense_matmul.md
deleted file mode 100644
index 5bb99ef029..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_tensor_dense_matmul.md
+++ /dev/null
@@ -1,163 +0,0 @@
-### `tf.sparse_tensor_dense_matmul(sp_a, b, adjoint_a=False, adjoint_b=False, name=None)` {#sparse_tensor_dense_matmul}
-
-Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
-
-No validity checking is performed on the indices of A.  However, the following
-input format is recommended for optimal behavior:
-
-if adjoint_a == false:
-  A should be sorted in lexicographically increasing order.  Use
-  sparse_reorder if you're not sure.
-if adjoint_a == true:
-  A should be sorted in order of increasing dimension 1 (i.e., "column major"
-  order instead of "row major" order).
-
-Deciding when to use sparse_tensor_dense_matmul vs. matmul(sp_a=True):
-
-There are a number of questions to ask in the decision process, including:
-
-* Will the SparseTensor A fit in memory if densified?
-* Is the column count of the product large (>> 1)?
-* Is the density of A larger than approximately 15%?
-
-If the answer to several of these questions is yes, consider
-converting the SparseTensor to a dense one and using tf.matmul with sp_a=True.
-
-This operation tends to perform well when A is more sparse, if the column size
-of the product is small (e.g. matrix-vector multiplication), if sp_a.shape
-takes on large values.
-
-Below is a rough speed comparison between sparse_tensor_dense_matmul,
-labelled 'sparse', and matmul(sp_a=True), labelled 'dense'.  For purposes of
-the comparison, the time spent converting from a SparseTensor to a dense
-Tensor is not included, so it is overly conservative with respect to
-the time ratio.
-
-Benchmark system:
-CPU: Intel Ivybridge with HyperThreading (6 cores) dL1:32KB dL2:256KB dL3:12MB
-GPU: NVidia Tesla k40c
-
-Compiled with:
--c opt --config=cuda --copt=-mavx
-
-```tensorflow/python/sparse_tensor_dense_matmul_op_test --benchmarks
-A sparse [m, k] with % nonzero values between 1% and 80%
-B dense [k, n]
-
-% nnz    n       gpu     m       k       dt(dense)       dt(sparse)      dt(sparse)/dt(dense)
-0.01     1       True    100     100     0.000221166     0.00010154      0.459112
-0.01     1       True    100     1000    0.00033858      0.000109275     0.322745
-0.01     1       True    1000    100     0.000310557     9.85661e-05     0.317385
-0.01     1       True    1000    1000    0.0008721       0.000100875     0.115669
-0.01     1       False   100     100     0.000208085     0.000107603     0.51711
-0.01     1       False   100     1000    0.000327112     9.51118e-05     0.290762
-0.01     1       False   1000    100     0.000308222     0.00010345      0.335635
-0.01     1       False   1000    1000    0.000865721     0.000101397     0.117124
-0.01     10      True    100     100     0.000218522     0.000105537     0.482958
-0.01     10      True    100     1000    0.000340882     0.000111641     0.327506
-0.01     10      True    1000    100     0.000315472     0.000117376     0.372064
-0.01     10      True    1000    1000    0.000905493     0.000123263     0.136128
-0.01     10      False   100     100     0.000221529     9.82571e-05     0.44354
-0.01     10      False   100     1000    0.000330552     0.000112615     0.340687
-0.01     10      False   1000    100     0.000341277     0.000114097     0.334324
-0.01     10      False   1000    1000    0.000819944     0.000120982     0.147549
-0.01     25      True    100     100     0.000207806     0.000105977     0.509981
-0.01     25      True    100     1000    0.000322879     0.00012921      0.400181
-0.01     25      True    1000    100     0.00038262      0.000141583     0.370035
-0.01     25      True    1000    1000    0.000865438     0.000202083     0.233504
-0.01     25      False   100     100     0.000209401     0.000104696     0.499979
-0.01     25      False   100     1000    0.000321161     0.000130737     0.407076
-0.01     25      False   1000    100     0.000377012     0.000136801     0.362856
-0.01     25      False   1000    1000    0.000861125     0.00020272      0.235413
-0.2      1       True    100     100     0.000206952     9.69219e-05     0.46833
-0.2      1       True    100     1000    0.000348674     0.000147475     0.422959
-0.2      1       True    1000    100     0.000336908     0.00010122      0.300439
-0.2      1       True    1000    1000    0.001022        0.000203274     0.198898
-0.2      1       False   100     100     0.000207532     9.5412e-05      0.459746
-0.2      1       False   100     1000    0.000356127     0.000146824     0.41228
-0.2      1       False   1000    100     0.000322664     0.000100918     0.312764
-0.2      1       False   1000    1000    0.000998987     0.000203442     0.203648
-0.2      10      True    100     100     0.000211692     0.000109903     0.519165
-0.2      10      True    100     1000    0.000372819     0.000164321     0.440753
-0.2      10      True    1000    100     0.000338651     0.000144806     0.427596
-0.2      10      True    1000    1000    0.00108312      0.000758876     0.70064
-0.2      10      False   100     100     0.000215727     0.000110502     0.512231
-0.2      10      False   100     1000    0.000375419     0.0001613       0.429653
-0.2      10      False   1000    100     0.000336999     0.000145628     0.432132
-0.2      10      False   1000    1000    0.00110502      0.000762043     0.689618
-0.2      25      True    100     100     0.000218705     0.000129913     0.594009
-0.2      25      True    100     1000    0.000394794     0.00029428      0.745402
-0.2      25      True    1000    100     0.000404483     0.0002693       0.665788
-0.2      25      True    1000    1000    0.0012002       0.00194494      1.62052
-0.2      25      False   100     100     0.000221494     0.0001306       0.589632
-0.2      25      False   100     1000    0.000396436     0.000297204     0.74969
-0.2      25      False   1000    100     0.000409346     0.000270068     0.659754
-0.2      25      False   1000    1000    0.00121051      0.00193737      1.60046
-0.5      1       True    100     100     0.000214981     9.82111e-05     0.456836
-0.5      1       True    100     1000    0.000415328     0.000223073     0.537101
-0.5      1       True    1000    100     0.000358324     0.00011269      0.314492
-0.5      1       True    1000    1000    0.00137612      0.000437401     0.317851
-0.5      1       False   100     100     0.000224196     0.000101423     0.452386
-0.5      1       False   100     1000    0.000400987     0.000223286     0.556841
-0.5      1       False   1000    100     0.000368825     0.00011224      0.304318
-0.5      1       False   1000    1000    0.00136036      0.000429369     0.31563
-0.5      10      True    100     100     0.000222125     0.000112308     0.505608
-0.5      10      True    100     1000    0.000461088     0.00032357      0.701753
-0.5      10      True    1000    100     0.000394624     0.000225497     0.571422
-0.5      10      True    1000    1000    0.00158027      0.00190898      1.20801
-0.5      10      False   100     100     0.000232083     0.000114978     0.495418
-0.5      10      False   100     1000    0.000454574     0.000324632     0.714146
-0.5      10      False   1000    100     0.000379097     0.000227768     0.600817
-0.5      10      False   1000    1000    0.00160292      0.00190168      1.18638
-0.5      25      True    100     100     0.00023429      0.000151703     0.647501
-0.5      25      True    100     1000    0.000497462     0.000598873     1.20386
-0.5      25      True    1000    100     0.000460778     0.000557038     1.20891
-0.5      25      True    1000    1000    0.00170036      0.00467336      2.74845
-0.5      25      False   100     100     0.000228981     0.000155334     0.678371
-0.5      25      False   100     1000    0.000496139     0.000620789     1.25124
-0.5      25      False   1000    100     0.00045473      0.000551528     1.21287
-0.5      25      False   1000    1000    0.00171793      0.00467152      2.71927
-0.8      1       True    100     100     0.000222037     0.000105301     0.47425
-0.8      1       True    100     1000    0.000410804     0.000329327     0.801664
-0.8      1       True    1000    100     0.000349735     0.000131225     0.375212
-0.8      1       True    1000    1000    0.00139219      0.000677065     0.48633
-0.8      1       False   100     100     0.000214079     0.000107486     0.502085
-0.8      1       False   100     1000    0.000413746     0.000323244     0.781261
-0.8      1       False   1000    100     0.000348983     0.000131983     0.378193
-0.8      1       False   1000    1000    0.00136296      0.000685325     0.50282
-0.8      10      True    100     100     0.000229159     0.00011825      0.516017
-0.8      10      True    100     1000    0.000498845     0.000532618     1.0677
-0.8      10      True    1000    100     0.000383126     0.00029935      0.781336
-0.8      10      True    1000    1000    0.00162866      0.00307312      1.88689
-0.8      10      False   100     100     0.000230783     0.000124958     0.541452
-0.8      10      False   100     1000    0.000493393     0.000550654     1.11606
-0.8      10      False   1000    100     0.000377167     0.000298581     0.791642
-0.8      10      False   1000    1000    0.00165795      0.00305103      1.84024
-0.8      25      True    100     100     0.000233496     0.000175241     0.75051
-0.8      25      True    100     1000    0.00055654      0.00102658      1.84458
-0.8      25      True    1000    100     0.000463814     0.000783267     1.68875
-0.8      25      True    1000    1000    0.00186905      0.00755344      4.04132
-0.8      25      False   100     100     0.000240243     0.000175047     0.728625
-0.8      25      False   100     1000    0.000578102     0.00104499      1.80763
-0.8      25      False   1000    100     0.000485113     0.000776849     1.60138
-0.8      25      False   1000    1000    0.00211448      0.00752736      3.55992
-```
-
-##### Args:
-
-
-*  <b>`sp_a`</b>: SparseTensor A, of rank 2.
-*  <b>`b`</b>: A dense Matrix with the same dtype as sp_a.
-*  <b>`adjoint_a`</b>: Use the adjoint of A in the matrix multiply.  If A is complex,
-    this is transpose(conj(A)).  Otherwise it's transpose(A).
-*  <b>`adjoint_b`</b>: Use the adjoint of B in the matrix multiply.  If B is complex,
-    this is transpose(conj(B)).  Otherwise it's transpose(B).
-*  <b>`name`</b>: A name prefix for the returned tensors (optional)
-
-##### Returns:
-
-  A dense matrix (pseudo-code in dense np.matrix notation):
-    A = A.H if adjoint_a else A
-    B = B.H if adjoint_b else B
-    return A*B
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_tensor_to_dense.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_tensor_to_dense.md
new file mode 100644
index 0000000000..a0c0a6ca9c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sparse_tensor_to_dense.md
@@ -0,0 +1,43 @@
+### `tf.sparse_tensor_to_dense(sp_input, default_value=0, validate_indices=True, name=None)` {#sparse_tensor_to_dense}
+
+Converts a `SparseTensor` into a dense tensor.
+
+This op is a convenience wrapper around `sparse_to_dense` for `SparseTensor`s.
+
+For example, if `sp_input` has shape `[3, 5]` and non-empty string values:
+
+    [0, 1]: a
+    [0, 3]: b
+    [2, 0]: c
+
+and `default_value` is `x`, then the output will be a dense `[3, 5]`
+string tensor with values:
+
+    [[x a x b x]
+     [x x x x x]
+     [c x x x x]]
+
+Indices must be without repeats.  This is only
+tested if validate_indices is True.
+
+##### Args:
+
+
+*  <b>`sp_input`</b>: The input `SparseTensor`.
+*  <b>`default_value`</b>: Scalar value to set for indices not specified in
+    `sp_input`.  Defaults to zero.
+*  <b>`validate_indices`</b>: A boolean value.  If `True`, indices are checked to make
+    sure they are sorted in lexicographic order and that there are no repeats.
+*  <b>`name`</b>: A name prefix for the returned tensors (optional).
+
+##### Returns:
+
+  A dense tensor with shape `sp_input.shape` and values specified by
+  the non-empty values in `sp_input`. Indices not in `sp_input` are assigned
+  `default_value`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.split.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.split.md
deleted file mode 100644
index f6cc936328..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.split.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.split(split_dim, num_split, value, name='split')` {#split}
-
-Splits a tensor into `num_split` tensors along one dimension.
-
-Splits `value` along dimension `split_dim` into `num_split` smaller tensors.
-Requires that `num_split` evenly divide `value.shape[split_dim]`.
-
-For example:
-
-```python
-# 'value' is a tensor with shape [5, 30]
-# Split 'value' into 3 tensors along dimension 1
-split0, split1, split2 = tf.split(1, 3, value)
-tf.shape(split0) ==> [5, 10]
-```
-
-##### Args:
-
-
-*  <b>`split_dim`</b>: A 0-D `int32` `Tensor`. The dimension along which to split.
-    Must be in the range `[0, rank(value))`.
-*  <b>`num_split`</b>: A Python integer. The number of ways to split.
-*  <b>`value`</b>: The `Tensor` to split.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  `num_split` `Tensor` objects resulting from splitting `value`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sqrt.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sqrt.md
new file mode 100644
index 0000000000..250817f3bf
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.sqrt.md
@@ -0,0 +1,16 @@
+### `tf.sqrt(x, name=None)` {#sqrt}
+
+Computes square root of x element-wise.
+
+I.e., \\(y = \sqrt{x} = x^{1/2}\\).
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.square.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.square.md
deleted file mode 100644
index 649c763015..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.square.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.square(x, name=None)` {#square}
-
-Computes square of x element-wise.
-
-I.e., \\(y = x * x = x^2\\).
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.ExponentialMovingAverage.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.ExponentialMovingAverage.md
new file mode 100644
index 0000000000..ea0aa48161
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.ExponentialMovingAverage.md
@@ -0,0 +1,229 @@
+Maintains moving averages of variables by employing an exponential decay.
+
+When training a model, it is often beneficial to maintain moving averages of
+the trained parameters.  Evaluations that use averaged parameters sometimes
+produce significantly better results than the final trained values.
+
+The `apply()` method adds shadow copies of trained variables and add ops that
+maintain a moving average of the trained variables in their shadow copies.
+It is used when building the training model.  The ops that maintain moving
+averages are typically run after each training step.
+The `average()` and `average_name()` methods give access to the shadow
+variables and their names.  They are useful when building an evaluation
+model, or when restoring a model from a checkpoint file.  They help use the
+moving averages in place of the last trained values for evaluations.
+
+The moving averages are computed using exponential decay.  You specify the
+decay value when creating the `ExponentialMovingAverage` object.  The shadow
+variables are initialized with the same initial values as the trained
+variables.  When you run the ops to maintain the moving averages, each
+shadow variable is updated with the formula:
+
+  `shadow_variable -= (1 - decay) * (shadow_variable - variable)`
+
+This is mathematically equivalent to the classic formula below, but the use
+of an `assign_sub` op (the `"-="` in the formula) allows concurrent lockless
+updates to the variables:
+
+  `shadow_variable = decay * shadow_variable + (1 - decay) * variable`
+
+Reasonable values for `decay` are close to 1.0, typically in the
+multiple-nines range: 0.999, 0.9999, etc.
+
+Example usage when creating a training model:
+
+```python
+# Create variables.
+var0 = tf.Variable(...)
+var1 = tf.Variable(...)
+# ... use the variables to build a training model...
+...
+# Create an op that applies the optimizer.  This is what we usually
+# would use as a training op.
+opt_op = opt.minimize(my_loss, [var0, var1])
+
+# Create an ExponentialMovingAverage object
+ema = tf.train.ExponentialMovingAverage(decay=0.9999)
+
+# Create the shadow variables, and add ops to maintain moving averages
+# of var0 and var1.
+maintain_averages_op = ema.apply([var0, var1])
+
+# Create an op that will update the moving averages after each training
+# step.  This is what we will use in place of the usual training op.
+with tf.control_dependencies([opt_op]):
+    training_op = tf.group(maintain_averages_op)
+
+...train the model by running training_op...
+```
+
+There are two ways to use the moving averages for evaluations:
+
+*  Build a model that uses the shadow variables instead of the variables.
+   For this, use the `average()` method which returns the shadow variable
+   for a given variable.
+*  Build a model normally but load the checkpoint files to evaluate by using
+   the shadow variable names.  For this use the `average_name()` method.  See
+   the [Saver class](../../api_docs/python/train.md#Saver) for more
+   information on restoring saved variables.
+
+Example of restoring the shadow variable values:
+
+```python
+# Create a Saver that loads variables from their saved shadow values.
+shadow_var0_name = ema.average_name(var0)
+shadow_var1_name = ema.average_name(var1)
+saver = tf.train.Saver({shadow_var0_name: var0, shadow_var1_name: var1})
+saver.restore(...checkpoint filename...)
+# var0 and var1 now hold the moving average values
+```
+
+- - -
+
+#### `tf.train.ExponentialMovingAverage.__init__(decay, num_updates=None, name='ExponentialMovingAverage')` {#ExponentialMovingAverage.__init__}
+
+Creates a new ExponentialMovingAverage object.
+
+The `apply()` method has to be called to create shadow variables and add
+ops to maintain moving averages.
+
+The optional `num_updates` parameter allows one to tweak the decay rate
+dynamically. .  It is typical to pass the count of training steps, usually
+kept in a variable that is incremented at each step, in which case the
+decay rate is lower at the start of training.  This makes moving averages
+move faster.  If passed, the actual decay rate used is:
+
+  `min(decay, (1 + num_updates) / (10 + num_updates))`
+
+##### Args:
+
+
+*  <b>`decay`</b>: Float.  The decay to use.
+*  <b>`num_updates`</b>: Optional count of number of updates applied to variables.
+*  <b>`name`</b>: String. Optional prefix name to use for the name of ops added in
+    `apply()`.
+
+
+- - -
+
+#### `tf.train.ExponentialMovingAverage.apply(var_list=None)` {#ExponentialMovingAverage.apply}
+
+Maintains moving averages of variables.
+
+`var_list` must be a list of `Variable` or `Tensor` objects.  This method
+creates shadow variables for all elements of `var_list`.  Shadow variables
+for `Variable` objects are initialized to the variable's initial value.
+They will be added to the `GraphKeys.MOVING_AVERAGE_VARIABLES` collection.
+For `Tensor` objects, the shadow variables are initialized to 0.
+
+shadow variables are created with `trainable=False` and added to the
+`GraphKeys.ALL_VARIABLES` collection.  They will be returned by calls to
+`tf.all_variables()`.
+
+Returns an op that updates all shadow variables as described above.
+
+Note that `apply()` can be called multiple times with different lists of
+variables.
+
+##### Args:
+
+
+*  <b>`var_list`</b>: A list of Variable or Tensor objects. The variables
+    and Tensors must be of types float32 or float64.
+
+##### Returns:
+
+  An Operation that updates the moving averages.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If the arguments are not all float32 or float64.
+*  <b>`ValueError`</b>: If the moving average of one of the variables is already
+    being computed.
+
+
+- - -
+
+#### `tf.train.ExponentialMovingAverage.average_name(var)` {#ExponentialMovingAverage.average_name}
+
+Returns the name of the `Variable` holding the average for `var`.
+
+The typical scenario for `ExponentialMovingAverage` is to compute moving
+averages of variables during training, and restore the variables from the
+computed moving averages during evaluations.
+
+To restore variables, you have to know the name of the shadow variables.
+That name and the original variable can then be passed to a `Saver()` object
+to restore the variable from the moving average value with:
+  `saver = tf.train.Saver({ema.average_name(var): var})`
+
+`average_name()` can be called whether or not `apply()` has been called.
+
+##### Args:
+
+
+*  <b>`var`</b>: A `Variable` object.
+
+##### Returns:
+
+  A string: The name of the variable that will be used or was used
+  by the `ExponentialMovingAverage class` to hold the moving average of
+  `var`.
+
+
+- - -
+
+#### `tf.train.ExponentialMovingAverage.average(var)` {#ExponentialMovingAverage.average}
+
+Returns the `Variable` holding the average of `var`.
+
+##### Args:
+
+
+*  <b>`var`</b>: A `Variable` object.
+
+##### Returns:
+
+  A `Variable` object or `None` if the moving average of `var`
+  is not maintained..
+
+
+- - -
+
+#### `tf.train.ExponentialMovingAverage.variables_to_restore(moving_avg_variables=None)` {#ExponentialMovingAverage.variables_to_restore}
+
+Returns a map of names to `Variables` to restore.
+
+If a variable has a moving average, use the moving average variable name as
+the restore name; otherwise, use the variable name.
+
+For example,
+
+```python
+  variables_to_restore = ema.variables_to_restore()
+  saver = tf.train.Saver(variables_to_restore)
+```
+
+Below is an example of such mapping:
+
+```
+  conv/batchnorm/gamma/ExponentialMovingAverage: conv/batchnorm/gamma,
+  conv_4/conv2d_params/ExponentialMovingAverage: conv_4/conv2d_params,
+  global_step: global_step
+```
+
+##### Args:
+
+
+*  <b>`moving_avg_variables`</b>: a list of variables that require to use of the
+    moving variable name to be restored. If None, it will default to
+    variables.moving_average_variables() + variables.trainable_variables()
+
+##### Returns:
+
+  A map from restore_names to variables. The restore_name can be the
+  moving_average version of the variable name if it exist, or the original
+  variable name.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.FtrlOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.FtrlOptimizer.md
new file mode 100644
index 0000000000..4fe719ee6b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.FtrlOptimizer.md
@@ -0,0 +1,32 @@
+Optimizer that implements the FTRL algorithm.
+
+See this [paper](
+https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
+
+- - -
+
+#### `tf.train.FtrlOptimizer.__init__(learning_rate, learning_rate_power=-0.5, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, use_locking=False, name='Ftrl')` {#FtrlOptimizer.__init__}
+
+Construct a new FTRL optimizer.
+
+##### Args:
+
+
+*  <b>`learning_rate`</b>: A float value or a constant float `Tensor`.
+*  <b>`learning_rate_power`</b>: A float value, must be less or equal to zero.
+*  <b>`initial_accumulator_value`</b>: The starting value for accumulators.
+    Only positive values are allowed.
+*  <b>`l1_regularization_strength`</b>: A float value, must be greater than or
+    equal to zero.
+*  <b>`l2_regularization_strength`</b>: A float value, must be greater than or
+    equal to zero.
+*  <b>`use_locking`</b>: If `True` use locks for update operations.
+*  <b>`name`</b>: Optional name prefix for the operations created when applying
+    gradients.  Defaults to "Ftrl".
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If one of the arguments is invalid.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Optimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Optimizer.md
new file mode 100644
index 0000000000..d5d8bb13dd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Optimizer.md
@@ -0,0 +1,255 @@
+Base class for optimizers.
+
+This class defines the API to add Ops to train a model.  You never use this
+class directly, but instead instantiate one of its subclasses such as
+`GradientDescentOptimizer`, `AdagradOptimizer`, or `MomentumOptimizer`.
+
+### Usage
+
+```python
+# Create an optimizer with the desired parameters.
+opt = GradientDescentOptimizer(learning_rate=0.1)
+# Add Ops to the graph to minimize a cost by updating a list of variables.
+# "cost" is a Tensor, and the list of variables contains tf.Variable
+# objects.
+opt_op = opt.minimize(cost, var_list=<list of variables>)
+```
+
+In the training program you will just have to run the returned Op.
+
+```python
+# Execute opt_op to do one step of training:
+opt_op.run()
+```
+
+### Processing gradients before applying them.
+
+Calling `minimize()` takes care of both computing the gradients and
+applying them to the variables.  If you want to process the gradients
+before applying them you can instead use the optimizer in three steps:
+
+1.  Compute the gradients with `compute_gradients()`.
+2.  Process the gradients as you wish.
+3.  Apply the processed gradients with `apply_gradients()`.
+
+Example:
+
+```python
+# Create an optimizer.
+opt = GradientDescentOptimizer(learning_rate=0.1)
+
+# Compute the gradients for a list of variables.
+grads_and_vars = opt.compute_gradients(loss, <list of variables>)
+
+# grads_and_vars is a list of tuples (gradient, variable).  Do whatever you
+# need to the 'gradient' part, for example cap them, etc.
+capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars]
+
+# Ask the optimizer to apply the capped gradients.
+opt.apply_gradients(capped_grads_and_vars)
+```
+
+- - -
+
+#### `tf.train.Optimizer.__init__(use_locking, name)` {#Optimizer.__init__}
+
+Create a new Optimizer.
+
+This must be called by the constructors of subclasses.
+
+##### Args:
+
+
+*  <b>`use_locking`</b>: Bool. If True apply use locks to prevent concurrent updates
+    to variables.
+*  <b>`name`</b>: A non-empty string.  The name to use for accumulators created
+    for the optimizer.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If name is malformed.
+
+
+
+- - -
+
+#### `tf.train.Optimizer.minimize(loss, global_step=None, var_list=None, gate_gradients=1, aggregation_method=None, colocate_gradients_with_ops=False, name=None, grad_loss=None)` {#Optimizer.minimize}
+
+Add operations to minimize `loss` by updating `var_list`.
+
+This method simply combines calls `compute_gradients()` and
+`apply_gradients()`. If you want to process the gradient before applying
+them call `compute_gradients()` and `apply_gradients()` explicitly instead
+of using this function.
+
+##### Args:
+
+
+*  <b>`loss`</b>: A `Tensor` containing the value to minimize.
+*  <b>`global_step`</b>: Optional `Variable` to increment by one after the
+    variables have been updated.
+*  <b>`var_list`</b>: Optional list of `Variable` objects to update to minimize
+    `loss`.  Defaults to the list of variables collected in the graph
+    under the key `GraphKeys.TRAINABLE_VARIABLES`.
+*  <b>`gate_gradients`</b>: How to gate the computation of gradients.  Can be
+    `GATE_NONE`, `GATE_OP`, or  `GATE_GRAPH`.
+*  <b>`aggregation_method`</b>: Specifies the method used to combine gradient terms.
+    Valid values are defined in the class `AggregationMethod`.
+*  <b>`colocate_gradients_with_ops`</b>: If True, try colocating gradients with
+    the corresponding op.
+*  <b>`name`</b>: Optional name for the returned operation.
+*  <b>`grad_loss`</b>: Optional. A `Tensor` holding the gradient computed for `loss`.
+
+##### Returns:
+
+  An Operation that updates the variables in `var_list`.  If `global_step`
+  was not `None`, that operation also increments `global_step`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If some of the variables are not `Variable` objects.
+
+
+- - -
+
+#### `tf.train.Optimizer.compute_gradients(loss, var_list=None, gate_gradients=1, aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None)` {#Optimizer.compute_gradients}
+
+Compute gradients of `loss` for the variables in `var_list`.
+
+This is the first part of `minimize()`.  It returns a list
+of (gradient, variable) pairs where "gradient" is the gradient
+for "variable".  Note that "gradient" can be a `Tensor`, an
+`IndexedSlices`, or `None` if there is no gradient for the
+given variable.
+
+##### Args:
+
+
+*  <b>`loss`</b>: A Tensor containing the value to minimize.
+*  <b>`var_list`</b>: Optional list of tf.Variable to update to minimize
+    `loss`.  Defaults to the list of variables collected in the graph
+    under the key `GraphKey.TRAINABLE_VARIABLES`.
+*  <b>`gate_gradients`</b>: How to gate the computation of gradients.  Can be
+    `GATE_NONE`, `GATE_OP`, or `GATE_GRAPH`.
+*  <b>`aggregation_method`</b>: Specifies the method used to combine gradient terms.
+    Valid values are defined in the class `AggregationMethod`.
+*  <b>`colocate_gradients_with_ops`</b>: If True, try colocating gradients with
+    the corresponding op.
+*  <b>`grad_loss`</b>: Optional. A `Tensor` holding the gradient computed for `loss`.
+
+##### Returns:
+
+  A list of (gradient, variable) pairs.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `var_list` contains anything else than `Variable` objects.
+*  <b>`ValueError`</b>: If some arguments are invalid.
+
+
+- - -
+
+#### `tf.train.Optimizer.apply_gradients(grads_and_vars, global_step=None, name=None)` {#Optimizer.apply_gradients}
+
+Apply gradients to variables.
+
+This is the second part of `minimize()`. It returns an `Operation` that
+applies gradients.
+
+##### Args:
+
+
+*  <b>`grads_and_vars`</b>: List of (gradient, variable) pairs as returned by
+    `compute_gradients()`.
+*  <b>`global_step`</b>: Optional `Variable` to increment by one after the
+    variables have been updated.
+*  <b>`name`</b>: Optional name for the returned operation.  Default to the
+    name passed to the `Optimizer` constructor.
+
+##### Returns:
+
+  An `Operation` that applies the specified gradients. If `global_step`
+  was not None, that operation also increments `global_step`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `grads_and_vars` is malformed.
+*  <b>`ValueError`</b>: If none of the variables have gradients.
+
+
+
+### Gating Gradients
+
+Both `minimize()` and `compute_gradients()` accept a `gate_gradient` argument
+that controls the degree of parallelism during the application of the
+gradients.
+
+The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
+
+<b>`GATE_NONE`</b>: Compute and apply gradients in parallel.  This provides
+the maximum parallelism in execution, at the cost of some non-reproducibility
+in the results.  For example the two gradients of `matmul` depend on the input
+values: With `GATE_NONE` one of the gradients could be applied to one of the
+inputs _before_ the other gradient is computed resulting in non-reproducible
+results.
+
+<b>`GATE_OP`</b>: For each Op, make sure all gradients are computed before
+they are used.  This prevents race conditions for Ops that generate gradients
+for multiple inputs where the gradients depend on the inputs.
+
+<b>`GATE_GRAPH`</b>: Make sure all gradients for all variables are computed
+before any one of them is used.  This provides the least parallelism but can
+be useful if you want to process all gradients before applying any of them.
+
+### Slots
+
+Some optimizer subclasses, such as `MomentumOptimizer` and `AdagradOptimizer`
+allocate and manage additional variables associated with the variables to
+train.  These are called <i>Slots</i>.  Slots have names and you can ask the
+optimizer for the names of the slots that it uses.  Once you have a slot name
+you can ask the optimizer for the variable it created to hold the slot value.
+
+This can be useful if you want to log debug a training algorithm, report stats
+about the slots, etc.
+
+- - -
+
+#### `tf.train.Optimizer.get_slot_names()` {#Optimizer.get_slot_names}
+
+Return a list of the names of slots created by the `Optimizer`.
+
+See `get_slot()`.
+
+##### Returns:
+
+  A list of strings.
+
+
+- - -
+
+#### `tf.train.Optimizer.get_slot(var, name)` {#Optimizer.get_slot}
+
+Return a slot named `name` created for `var` by the Optimizer.
+
+Some `Optimizer` subclasses use additional variables.  For example
+`Momentum` and `Adagrad` use variables to accumulate updates.  This method
+gives access to these `Variable` objects if for some reason you need them.
+
+Use `get_slot_names()` to get the list of slot names created by the
+`Optimizer`.
+
+##### Args:
+
+
+*  <b>`var`</b>: A variable passed to `minimize()` or `apply_gradients()`.
+*  <b>`name`</b>: A string.
+
+##### Returns:
+
+  The `Variable` for the slot if it was created, `None` otherwise.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.QueueRunner.from_proto.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.QueueRunner.from_proto.md
new file mode 100644
index 0000000000..2caa8f769a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.QueueRunner.from_proto.md
@@ -0,0 +1,4 @@
+#### `tf.train.QueueRunner.from_proto(queue_runner_def)` {#QueueRunner.from_proto}
+
+Returns a `QueueRunner` object created from `queue_runner_def`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Server.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Server.md
deleted file mode 100644
index 3f87ed3bf0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.Server.md
+++ /dev/null
@@ -1,113 +0,0 @@
-An in-process TensorFlow server, for use in distributed training.
-
-A `tf.train.Server` instance encapsulates a set of devices and a
-[`tf.Session`](../../api_docs/python/client.md#Session) target that
-can participate in distributed training. A server belongs to a
-cluster (specified by a [`tf.train.ClusterSpec`](#ClusterSpec)), and
-corresponds to a particular task in a named job. The server can
-communicate with any other server in the same cluster.
-
-- - -
-
-#### `tf.train.Server.__init__(server_or_cluster_def, job_name=None, task_index=None, protocol=None, start=True)` {#Server.__init__}
-
-Creates a new server with the given definition.
-
-The `job_name`, `task_index`, and `protocol` arguments are optional, and
-override any information provided in `server_or_cluster_def`.
-
-##### Args:
-
-
-*  <b>`server_or_cluster_def`</b>: A `tf.train.ServerDef` or
-    `tf.train.ClusterDef` protocol buffer, or a
-    `tf.train.ClusterSpec` object, describing the server to be
-    created and/or the cluster of which it is a member.
-*  <b>`job_name`</b>: (Optional.) Specifies the name of the job of which the server
-    is a member. Defaults to the value in `server_or_cluster_def`, if
-    specified.
-*  <b>`task_index`</b>: (Optional.) Specifies the task index of the server in its
-    job. Defaults to the value in `server_or_cluster_def`, if specified.
-    Otherwise defaults to 0 if the server's job has only one task.
-*  <b>`protocol`</b>: (Optional.) Specifies the protocol to be used by the server.
-    Acceptable values include `"grpc"`. Defaults to the value in
-    `server_or_cluster_def`, if specified. Otherwise defaults to `"grpc"`.
-*  <b>`start`</b>: (Optional.) Boolean, indicating whether to start the server
-    after creating it. Defaults to `True`.
-
-##### Raises:
-
-  tf.errors.OpError: Or one of its subclasses if an error occurs while
-    creating the TensorFlow server.
-
-
-- - -
-
-#### `tf.train.Server.create_local_server(start=True)` {#Server.create_local_server}
-
-Creates a new single-process cluster running on the local host.
-
-This method is a convenience wrapper for creating a
-`tf.train.Server` with a `tf.train.ServerDef` that specifies a
-single-process cluster containing a single task in a job called
-`"local"`.
-
-##### Args:
-
-
-*  <b>`start`</b>: (Optional.) Boolean, indicating whether to start the server after
-    creating it. Defaults to `True`.
-
-##### Returns:
-
-  A local `tf.train.Server`.
-
-
-- - -
-
-#### `tf.train.Server.target` {#Server.target}
-
-Returns the target for a `tf.Session` to connect to this server.
-
-To create a
-[`tf.Session`](../../api_docs/python/client.md#Session) that
-connects to this server, use the following snippet:
-
-```python
-server = tf.train.Server(...)
-with tf.Session(server.target):
-  # ...
-```
-
-##### Returns:
-
-  A string containing a session target for this server.
-
-
-
-- - -
-
-#### `tf.train.Server.start()` {#Server.start}
-
-Starts this server.
-
-##### Raises:
-
-  tf.errors.OpError: Or one of its subclasses if an error occurs while
-    starting the TensorFlow server.
-
-
-- - -
-
-#### `tf.train.Server.join()` {#Server.join}
-
-Blocks until the server has shut down.
-
-This method currently blocks forever.
-
-##### Raises:
-
-  tf.errors.OpError: Or one of its subclasses if an error occurs while
-    joining the TensorFlow server.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.batch.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.batch.md
deleted file mode 100644
index 96142e0719..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.batch.md
+++ /dev/null
@@ -1,68 +0,0 @@
-### `tf.train.batch(tensors, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, shared_name=None, name=None)` {#batch}
-
-Creates batches of tensors in `tensors`.
-
-The argument `tensors` can be a list or a dictionary of tensors.
-The value returned by the function will be of the same type
-as `tensors`.
-
-This function is implemented using a queue. A `QueueRunner` for the
-queue is added to the current `Graph`'s `QUEUE_RUNNER` collection.
-
-If `enqueue_many` is `False`, `tensors` is assumed to represent a single
-example.  An input tensor with shape `[x, y, z]` will be output as a tensor
-with shape `[batch_size, x, y, z]`.
-
-If `enqueue_many` is `True`, `tensors` is assumed to represent a batch of
-examples, where the first dimension is indexed by example, and all members of
-`tensor_list` should have the same size in the first dimension.  If an input
-tensor has shape `[*, x, y, z]`, the output will have shape `[batch_size, x,
-y, z]`.  The `capacity` argument controls the how long the prefetching is
-allowed to grow the queues.
-
-The returned operation is a dequeue operation and will throw
-`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
-operation is feeding another input queue, its queue runner will catch
-this exception, however, if this operation is used in your main thread
-you are responsible for catching this yourself.
-
-*N.B.:* If `dynamic_pad` is `False`, you must ensure that either
-(i) the `shapes` argument is passed, or (ii) all of the tensors in
-`tensors` must have fully-defined shapes. `ValueError` will be
-raised if neither of these conditions holds.
-
-If `dynamic_pad` is `True`, it is sufficient that the *rank* of the
-tensors is known, but individual dimensions may have shape `None`.
-In this case, for each enqueue the dimensions with value `None`
-may have a variable length; upon dequeue, the output tensors will be padded
-on the right to the maximum shape of the tensors in the current minibatch.
-For numbers, this padding takes value 0.  For strings, this padding is
-the empty string.  See `PaddingFIFOQueue` for more info.
-
-##### Args:
-
-
-*  <b>`tensors`</b>: The list or dictionary of tensors to enqueue.
-*  <b>`batch_size`</b>: The new batch size pulled from the queue.
-*  <b>`num_threads`</b>: The number of threads enqueuing `tensor_list`.
-*  <b>`capacity`</b>: An integer. The maximum number of elements in the queue.
-*  <b>`enqueue_many`</b>: Whether each tensor in `tensor_list` is a single example.
-*  <b>`shapes`</b>: (Optional) The shapes for each example.  Defaults to the
-    inferred shapes for `tensor_list`.
-*  <b>`dynamic_pad`</b>: Boolean.  Allow variable dimensions in input shapes.
-    The given dimensions are padded upon dequeue so that tensors within a
-    batch have the same shapes.
-*  <b>`shared_name`</b>: (optional). If set, this queue will be shared under the given
-    name across multiple sessions.
-*  <b>`name`</b>: (Optional) A name for the operations.
-
-##### Returns:
-
-  A list or dictionary of tensors with the same types as `tensors`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be
-    inferred from the elements of `tensors`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.replica_device_setter.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.replica_device_setter.md
new file mode 100644
index 0000000000..a5ea200562
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.train.replica_device_setter.md
@@ -0,0 +1,50 @@
+### `tf.train.replica_device_setter(ps_tasks=0, ps_device='/job:ps', worker_device='/job:worker', merge_devices=True, cluster=None, ps_ops=None)` {#replica_device_setter}
+
+Return a `device function` to use when building a Graph for replicas.
+
+Device Functions are used in `with tf.device(device_function):` statement to
+automatically assign devices to `Operation` objects as they are constructed,
+Device constraints are added from the inner-most context first, working
+outwards. The merging behavior adds constraints to fields that are yet unset
+by a more inner context. Currently the fields are (job, task, cpu/gpu).
+
+If `cluster` is `None`, and `ps_tasks` is 0, the returned function is a no-op.
+
+For example,
+
+```python
+# To build a cluster with two ps jobs on hosts ps0 and ps1, and 3 worker
+# jobs on hosts worker0, worker1 and worker2.
+cluster_spec = {
+    "ps": ["ps0:2222", "ps1:2222"],
+    "worker": ["worker0:2222", "worker1:2222", "worker2:2222"]}
+with tf.device(tf.replica_device_setter(cluster=cluster_spec)):
+  # Build your graph
+  v1 = tf.Variable(...)  # assigned to /job:ps/task:0
+  v2 = tf.Variable(...)  # assigned to /job:ps/task:1
+  v3 = tf.Variable(...)  # assigned to /job:ps/task:0
+# Run compute
+```
+
+##### Args:
+
+
+*  <b>`ps_tasks`</b>: Number of tasks in the `ps` job.
+*  <b>`ps_device`</b>: String.  Device of the `ps` job.  If empty no `ps` job is used.
+    Defaults to `ps`.
+*  <b>`worker_device`</b>: String.  Device of the `worker` job.  If empty no `worker`
+    job is used.
+*  <b>`merge_devices`</b>: `Boolean`. If `True`, merges or only sets a device if the
+    device constraint is completely unset. merges device specification rather
+    than overriding them.
+*  <b>`cluster`</b>: `ClusterDef` proto or `ClusterSpec`.
+*  <b>`ps_ops`</b>: List of `Operation` objects that need to be placed on `ps` devices.
+
+##### Returns:
+
+  A function to pass to `tf.device()`.
+
+##### Raises:
+
+  TypeError if `cluster` is not a dictionary or `ClusterDef` protocol buffer.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.tuple.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.tuple.md
deleted file mode 100644
index 503a98d625..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.tuple.md
+++ /dev/null
@@ -1,36 +0,0 @@
-### `tf.tuple(tensors, name=None, control_inputs=None)` {#tuple}
-
-Group tensors together.
-
-This creates a tuple of tensors with the same values as the `tensors`
-argument, except that the value of each tensor is only returned after the
-values of all tensors have been computed.
-
-`control_inputs` contains additional ops that have to finish before this op
-finishes, but whose outputs are not returned.
-
-This can be used as a "join" mechanism for parallel computations: all the
-argument tensors can be computed in parallel, but the values of any tensor
-returned by `tuple` are only available after all the parallel computations
-are done.
-
-See also `group` and `with_dependencies`.
-
-##### Args:
-
-
-*  <b>`tensors`</b>: A list of `Tensor`s or `IndexedSlices`, some entries can be `None`.
-*  <b>`name`</b>: (optional) A name to use as a `name_scope` for the operation.
-*  <b>`control_inputs`</b>: List of additional ops to finish before returning.
-
-##### Returns:
-
-  Same as `tensors`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `tensors` does not contain any `Tensor` or `IndexedSlices`.
-*  <b>`TypeError`</b>: If `control_inputs` is not a list of `Operation` or `Tensor`
-    objects.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.unsorted_segment_sum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.unsorted_segment_sum.md
new file mode 100644
index 0000000000..63255ce815
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.unsorted_segment_sum.md
@@ -0,0 +1,38 @@
+### `tf.unsorted_segment_sum(data, segment_ids, num_segments, name=None)` {#unsorted_segment_sum}
+
+Computes the sum along segments of a tensor.
+
+Read [the section on
+Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
+of segments.
+
+Computes a tensor such that
+\\(output_i = \sum_j data_j\\) where sum is over `j` such
+that `segment_ids[j] == i`. Unlike `SegmentSum`, `segment_ids`
+need not be sorted and need not cover all values in the full
+  range of valid values.
+
+If the sum is empty for a given segment ID `i`, `output[i] = 0`.
+
+`num_segments` should equal the number of distinct segment IDs.
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/UnsortedSegmentSum.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    A 1-D tensor whose rank is equal to the rank of `data`'s
+    first dimension.
+*  <b>`num_segments`</b>: A `Tensor` of type `int32`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `data`.
+  Has same shape as data, except for dimension 0 which
+  has size `num_segments`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.where.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.where.md
new file mode 100644
index 0000000000..eae2259721
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.where.md
@@ -0,0 +1,46 @@
+### `tf.where(input, name=None)` {#where}
+
+Returns locations of true values in a boolean tensor.
+
+This operation returns the coordinates of true elements in `input`. The
+coordinates are returned in a 2-D tensor where the first dimension (rows)
+represents the number of true elements, and the second dimension (columns)
+represents the coordinates of the true elements. Keep in mind, the shape of
+the output tensor can vary depending on how many true values there are in
+`input`. Indices are output in row-major order.
+
+For example:
+
+```prettyprint
+# 'input' tensor is [[True, False]
+#                    [True, False]]
+# 'input' has two true values, so output has two coordinates.
+# 'input' has rank of 2, so coordinates have two indices.
+where(input) ==> [[0, 0],
+                  [1, 0]]
+
+# `input` tensor is [[[True, False]
+#                     [True, False]]
+#                    [[False, True]
+#                     [False, True]]
+#                    [[False, False]
+#                     [False, True]]]
+# 'input' has 5 true values, so output has 5 coordinates.
+# 'input' has rank of 3, so coordinates have three indices.
+where(input) ==> [[0, 0, 0],
+                  [0, 1, 0],
+                  [1, 0, 1],
+                  [1, 1, 1],
+                  [2, 1, 1]]
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `bool`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `int64`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.while_loop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.while_loop.md
new file mode 100644
index 0000000000..4baea56c63
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.while_loop.md
@@ -0,0 +1,60 @@
+### `tf.while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#while_loop}
+
+Repeat `body` while the condition `cond` is true.
+
+`cond` is a callable returning a boolean scalar tensor. `body` is a callable
+returning a list of tensors of the same length and with the same types as
+`loop_vars`. `loop_vars` is a list of tensors that is passed to both `cond`
+and `body`. `cond` and `body` both take as many arguments as there are
+`loop_vars`.
+
+In addition to regular Tensors or IndexedSlices, the body may accept and
+return TensorArray objects.  The flows of the TensorArray objects will
+be appropriately forwarded between loops and during gradient calculations.
+
+While `cond` evaluates to true, `body` is executed.
+
+`while_loop` implements non-strict semantics, enabling multiple iterations
+to run in parallel. The maximum number of parallel iterations can be
+controlled by `parallel_iterations`, which gives users some control over
+memory consumption and execution order. For correct programs, `while_loop`
+should return the same result for any parallel_iterations > 0.
+
+For training, TensorFlow remembers the tensors that are produced in the
+forward inference but needed in back propagation. These tensors can be a
+main source of memory consumption and often cause OOM problems when training
+on GPUs.  When the flag swap_memory is true, we swap out these tensors from
+GPU to CPU.  This for example allows us to train RNN models with very long
+sequences and large batches.
+
+##### Args:
+
+
+*  <b>`cond`</b>: A callable that represents the termination condition of the loop.
+*  <b>`body`</b>: A callable that represents the loop body.
+*  <b>`loop_vars`</b>: The list of variable input tensors.
+*  <b>`parallel_iterations`</b>: The number of iterations allowed to run in parallel.
+*  <b>`back_prop`</b>: Whether backprop is enabled for this while loop.
+*  <b>`swap_memory`</b>: Whether GPU-CPU memory swap is enabled for this loop.
+*  <b>`name`</b>: Optional name prefix for the returned tensors.
+
+##### Returns:
+
+  The output tensors for the loop variables after the loop.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `cond` or `body` is not callable.
+*  <b>`ValueError`</b>: if `loop_var` is empty.
+
+
+*  <b>`Example`</b>: 
+
+  ```python
+  i = tf.constant(0)
+  c = lambda i: tf.less(i, 10)
+  b = lambda i: tf.add(i, 1)
+  r = tf.while_loop(c, b, [i])
+  ```
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.zeros.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.zeros.md
new file mode 100644
index 0000000000..57598a372d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.zeros.md
@@ -0,0 +1,24 @@
+### `tf.zeros(shape, dtype=tf.float32, name=None)` {#zeros}
+
+Creates a tensor with all elements set to zero.
+
+This operation returns a tensor of type `dtype` with shape `shape` and
+all elements set to zero.
+
+For example:
+
+```python
+tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
+```
+
+##### Args:
+
+
+*  <b>`shape`</b>: Either a list of integers, or a 1-D `Tensor` of type `int32`.
+*  <b>`dtype`</b>: The type of an element in the resulting `Tensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` with all elements set to zero.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.zeros_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.zeros_initializer.md
deleted file mode 100644
index 707393f8be..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.zeros_initializer.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.zeros_initializer(shape, dtype=tf.float32)` {#zeros_initializer}
-
-An adaptor for zeros() to match the Initializer spec.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.AggregationMethod.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.AggregationMethod.md
deleted file mode 100644
index ee655fbd25..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.AggregationMethod.md
+++ /dev/null
@@ -1,10 +0,0 @@
-A class listing aggregation methods used to combine gradients.
-
-Computing partial derivatives can require aggregating gradient
-contributions. This class lists the various methods that can
-be used to combine gradients in the graph:
-
-*  `ADD_N`: All of the gradient terms are summed as part of one
-   operation using the "AddN" op. It has the property that all
-   gradients must be ready before any aggregation is performed.
-*  `DEFAULT`: The system-chosen default aggregation method.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.DeviceSpec.from_string.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.DeviceSpec.from_string.md
new file mode 100644
index 0000000000..5cbba0ada6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.DeviceSpec.from_string.md
@@ -0,0 +1,18 @@
+#### `tf.DeviceSpec.from_string(spec)` {#DeviceSpec.from_string}
+
+Construct a `DeviceSpec` from a string.
+
+##### Args:
+
+
+*  <b>`spec`</b>: a string of the form
+   /job:<name>/replica:<id>/task:<id>/device:CPU:<id>
+  or
+   /job:<name>/replica:<id>/task:<id>/device:GPU:<id>
+  as cpu and gpu are mutually exclusive.
+  All entries are optional.
+
+##### Returns:
+
+  A DeviceSpec.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.FIFOQueue.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.FIFOQueue.md
deleted file mode 100644
index 129107384f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.FIFOQueue.md
+++ /dev/null
@@ -1,41 +0,0 @@
-A queue implementation that dequeues elements in first-in-first out order.
-
-See [`tf.QueueBase`](#QueueBase) for a description of the methods on
-this class.
-
-- - -
-
-#### `tf.FIFOQueue.__init__(capacity, dtypes, shapes=None, names=None, shared_name=None, name='fifo_queue')` {#FIFOQueue.__init__}
-
-Creates a queue that dequeues elements in a first-in first-out order.
-
-A `FIFOQueue` has bounded capacity; supports multiple concurrent
-producers and consumers; and provides exactly-once delivery.
-
-A `FIFOQueue` holds a list of up to `capacity` elements. Each
-element is a fixed-length tuple of tensors whose dtypes are
-described by `dtypes`, and whose shapes are optionally described
-by the `shapes` argument.
-
-If the `shapes` argument is specified, each component of a queue
-element must have the respective fixed shape. If it is
-unspecified, different queue elements may have different shapes,
-but the use of `dequeue_many` is disallowed.
-
-##### Args:
-
-
-*  <b>`capacity`</b>: An integer. The upper bound on the number of elements
-    that may be stored in this queue.
-*  <b>`dtypes`</b>: A list of `DType` objects. The length of `dtypes` must equal
-    the number of tensors in each queue element.
-*  <b>`shapes`</b>: (Optional.) A list of fully-defined `TensorShape` objects
-    with the same length as `dtypes`, or `None`.
-*  <b>`names`</b>: (Optional.) A list of string naming the components in the queue
-    with the same length as `dtypes`, or `None`.  If specified the dequeue
-    methods return a dictionary with the names as keys.
-*  <b>`shared_name`</b>: (Optional.) If non-empty, this queue will be shared under
-    the given name across multiple sessions.
-*  <b>`name`</b>: Optional name for the queue operation.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.IdentityReader.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.IdentityReader.md
new file mode 100644
index 0000000000..46ba1e9d17
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.IdentityReader.md
@@ -0,0 +1,148 @@
+A Reader that outputs the queued work as both the key and value.
+
+To use, enqueue strings in a Queue.  Read will take the front
+work string and output (work, work).
+
+See ReaderBase for supported methods.
+- - -
+
+#### `tf.IdentityReader.__init__(name=None)` {#IdentityReader.__init__}
+
+Create a IdentityReader.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+
+- - -
+
+#### `tf.IdentityReader.num_records_produced(name=None)` {#IdentityReader.num_records_produced}
+
+Returns the number of records this reader has produced.
+
+This is the same as the number of Read executions that have
+succeeded.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.IdentityReader.num_work_units_completed(name=None)` {#IdentityReader.num_work_units_completed}
+
+Returns the number of work units this reader has finished processing.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.IdentityReader.read(queue, name=None)` {#IdentityReader.read}
+
+Returns the next record (key, value pair) produced by a reader.
+
+Will dequeue a work unit from queue if necessary (e.g. when the
+Reader needs to start reading from a new file since it has
+finished with the previous file).
+
+##### Args:
+
+
+*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
+    to a Queue, with string work items.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of Tensors (key, value).
+
+*  <b>`key`</b>: A string scalar Tensor.
+*  <b>`value`</b>: A string scalar Tensor.
+
+
+- - -
+
+#### `tf.IdentityReader.reader_ref` {#IdentityReader.reader_ref}
+
+Op that implements the reader.
+
+
+- - -
+
+#### `tf.IdentityReader.reset(name=None)` {#IdentityReader.reset}
+
+Restore a reader to its initial clean state.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.IdentityReader.restore_state(state, name=None)` {#IdentityReader.restore_state}
+
+Restore a reader to a previously saved state.
+
+Not all Readers support being restored, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`state`</b>: A string Tensor.
+    Result of a SerializeState of a Reader with matching type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.IdentityReader.serialize_state(name=None)` {#IdentityReader.serialize_state}
+
+Produce a string tensor that encodes the state of a reader.
+
+Not all Readers support being serialized, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A string Tensor.
+
+
+- - -
+
+#### `tf.IdentityReader.supports_serialize` {#IdentityReader.supports_serialize}
+
+Whether the Reader implementation can serialize its state.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.NoGradient.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.NoGradient.md
new file mode 100644
index 0000000000..15c40e6828
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.NoGradient.md
@@ -0,0 +1,23 @@
+### `tf.NoGradient(op_type)` {#NoGradient}
+
+Specifies that ops of type `op_type` do not have a defined gradient.
+
+This function is only used when defining a new op type. It may be
+used for ops such as `tf.size()` that are not differentiable.  For
+example:
+
+```python
+tf.NoGradient("Size")
+```
+
+##### Args:
+
+
+*  <b>`op_type`</b>: The string type of an operation. This corresponds to the
+    `OpDef.name` field for the proto that defines the operation.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `op_type` is not a string.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.RegisterGradient.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.RegisterGradient.md
new file mode 100644
index 0000000000..736bd5b4af
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.RegisterGradient.md
@@ -0,0 +1,36 @@
+A decorator for registering the gradient function for an op type.
+
+This decorator is only used when defining a new op type. For an op
+with `m` inputs and `n` outputs, the gradient function is a function
+that takes the original `Operation` and `n` `Tensor` objects
+(representing the gradients with respect to each output of the op),
+and returns `m` `Tensor` objects (representing the partial gradients
+with respect to each input of the op).
+
+For example, assuming that operations of type `"Sub"` take two
+inputs `x` and `y`, and return a single output `x - y`, the
+following gradient function would be registered:
+
+```python
+@tf.RegisterGradient("Sub")
+def _sub_grad(unused_op, grad):
+  return grad, tf.neg(grad)
+```
+
+The decorator argument `op_type` is the string type of an
+operation. This corresponds to the `OpDef.name` field for the proto
+that defines the operation.
+
+- - -
+
+#### `tf.RegisterGradient.__init__(op_type)` {#RegisterGradient.__init__}
+
+Creates a new decorator with `op_type` as the Operation type.
+
+##### Args:
+
+
+*  <b>`op_type`</b>: The string type of an operation. This corresponds to the
+    `OpDef.name` field for the proto that defines the operation.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.Tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.Tensor.md
new file mode 100644
index 0000000000..73af134a7a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.Tensor.md
@@ -0,0 +1,228 @@
+Represents a value produced by an `Operation`.
+
+A `Tensor` is a symbolic handle to one of the outputs of an
+`Operation`. It does not hold the values of that operation's output,
+but instead provides a means of computing those values in a
+TensorFlow [`Session`](../../api_docs/python/client.md#Session).
+
+This class has two primary purposes:
+
+1. A `Tensor` can be passed as an input to another `Operation`.
+   This builds a dataflow connection between operations, which
+   enables TensorFlow to execute an entire `Graph` that represents a
+   large, multi-step computation.
+
+2. After the graph has been launched in a session, the value of the
+   `Tensor` can be computed by passing it to
+   [`Session.run()`](../../api_docs/python/client.md#Session.run).
+   `t.eval()` is a shortcut for calling
+   `tf.get_default_session().run(t)`.
+
+In the following example, `c`, `d`, and `e` are symbolic `Tensor`
+objects, whereas `result` is a numpy array that stores a concrete
+value:
+
+```python
+# Build a dataflow graph.
+c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
+d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
+e = tf.matmul(c, d)
+
+# Construct a `Session` to execute the graph.
+sess = tf.Session()
+
+# Execute the graph and store the value that `e` represents in `result`.
+result = sess.run(e)
+```
+
+- - -
+
+#### `tf.Tensor.dtype` {#Tensor.dtype}
+
+The `DType` of elements in this tensor.
+
+
+- - -
+
+#### `tf.Tensor.name` {#Tensor.name}
+
+The string name of this tensor.
+
+
+- - -
+
+#### `tf.Tensor.value_index` {#Tensor.value_index}
+
+The index of this tensor in the outputs of its `Operation`.
+
+
+- - -
+
+#### `tf.Tensor.graph` {#Tensor.graph}
+
+The `Graph` that contains this tensor.
+
+
+- - -
+
+#### `tf.Tensor.op` {#Tensor.op}
+
+The `Operation` that produces this tensor as an output.
+
+
+- - -
+
+#### `tf.Tensor.consumers()` {#Tensor.consumers}
+
+Returns a list of `Operation`s that consume this tensor.
+
+##### Returns:
+
+  A list of `Operation`s.
+
+
+
+- - -
+
+#### `tf.Tensor.eval(feed_dict=None, session=None)` {#Tensor.eval}
+
+Evaluates this tensor in a `Session`.
+
+Calling this method will execute all preceding operations that
+produce the inputs needed for the operation that produces this
+tensor.
+
+*N.B.* Before invoking `Tensor.eval()`, its graph must have been
+launched in a session, and either a default session must be
+available, or `session` must be specified explicitly.
+
+##### Args:
+
+
+*  <b>`feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
+    See [`Session.run()`](../../api_docs/python/client.md#Session.run) for a
+    description of the valid feed values.
+*  <b>`session`</b>: (Optional.) The `Session` to be used to evaluate this tensor. If
+    none, the default session will be used.
+
+##### Returns:
+
+  A numpy array corresponding to the value of this tensor.
+
+
+
+- - -
+
+#### `tf.Tensor.get_shape()` {#Tensor.get_shape}
+
+Returns the `TensorShape` that represents the shape of this tensor.
+
+The shape is computed using shape inference functions that are
+registered for each `Operation` type using `tf.RegisterShape`.
+See [`TensorShape`](../../api_docs/python/framework.md#TensorShape) for more
+details of what a shape represents.
+
+The inferred shape of a tensor is used to provide shape
+information without having to launch the graph in a session. This
+can be used for debugging, and providing early error messages. For
+example:
+
+```python
+c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+
+print(c.get_shape())
+==> TensorShape([Dimension(2), Dimension(3)])
+
+d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]])
+
+print(d.get_shape())
+==> TensorShape([Dimension(4), Dimension(2)])
+
+# Raises a ValueError, because `c` and `d` do not have compatible
+# inner dimensions.
+e = tf.matmul(c, d)
+
+f = tf.matmul(c, d, transpose_a=True, transpose_b=True)
+
+print(f.get_shape())
+==> TensorShape([Dimension(3), Dimension(4)])
+```
+
+In some cases, the inferred shape may have unknown dimensions. If
+the caller has additional information about the values of these
+dimensions, `Tensor.set_shape()` can be used to augment the
+inferred shape.
+
+##### Returns:
+
+  A `TensorShape` representing the shape of this tensor.
+
+
+- - -
+
+#### `tf.Tensor.set_shape(shape)` {#Tensor.set_shape}
+
+Updates the shape of this tensor.
+
+This method can be called multiple times, and will merge the given
+`shape` with the current shape of this tensor. It can be used to
+provide additional information about the shape of this tensor that
+cannot be inferred from the graph alone. For example, this can be used
+to provide additional information about the shapes of images:
+
+```python
+_, image_data = tf.TFRecordReader(...).read(...)
+image = tf.image.decode_png(image_data, channels=3)
+
+# The height and width dimensions of `image` are data dependent, and
+# cannot be computed without executing the op.
+print(image.get_shape())
+==> TensorShape([Dimension(None), Dimension(None), Dimension(3)])
+
+# We know that each image in this dataset is 28 x 28 pixels.
+image.set_shape([28, 28, 3])
+print(image.get_shape())
+==> TensorShape([Dimension(28), Dimension(28), Dimension(3)])
+```
+
+##### Args:
+
+
+*  <b>`shape`</b>: A `TensorShape` representing the shape of this tensor.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `shape` is not compatible with the current shape of
+    this tensor.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.Tensor.__init__(op, value_index, dtype)` {#Tensor.__init__}
+
+Creates a new `Tensor`.
+
+##### Args:
+
+
+*  <b>`op`</b>: An `Operation`. `Operation` that computes this tensor.
+*  <b>`value_index`</b>: An `int`. Index of the operation's endpoint that produces
+    this tensor.
+*  <b>`dtype`</b>: A `DType`. Type of elements stored in this tensor.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If the op is not an `Operation`.
+
+
+- - -
+
+#### `tf.Tensor.device` {#Tensor.device}
+
+The name of the device on which this tensor will be produced, or None.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.add_check_numerics_ops.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.add_check_numerics_ops.md
deleted file mode 100644
index 9e72af79db..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.add_check_numerics_ops.md
+++ /dev/null
@@ -1,13 +0,0 @@
-### `tf.add_check_numerics_ops()` {#add_check_numerics_ops}
-
-Connect a `check_numerics` to every floating point tensor.
-
-`check_numerics` operations themselves are added for each `float` or `double`
-tensor in the graph. For all ops in the graph, the `check_numerics` op for
-all of its (`float` or `double`) inputs is guaranteed to run before the
-`check_numerics` op on any of its outputs.
-
-##### Returns:
-
-  A `group` op depending on all `check_numerics` ops added.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.add_to_collection.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.add_to_collection.md
new file mode 100644
index 0000000000..1d8d752917
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.add_to_collection.md
@@ -0,0 +1,14 @@
+### `tf.add_to_collection(name, value)` {#add_to_collection}
+
+Wrapper for `Graph.add_to_collection()` using the default graph.
+
+See [`Graph.add_to_collection()`](../../api_docs/python/framework.md#Graph.add_to_collection)
+for more details.
+
+##### Args:
+
+
+*  <b>`name`</b>: The key for the collection. For example, the `GraphKeys` class
+    contains many standard names for collections.
+*  <b>`value`</b>: The value to add to the collection.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.argmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.argmax.md
deleted file mode 100644
index af0a2270a9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.argmax.md
+++ /dev/null
@@ -1,17 +0,0 @@
-### `tf.argmax(input, dimension, name=None)` {#argmax}
-
-Returns the index with the largest value across dimensions of a tensor.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-*  <b>`dimension`</b>: A `Tensor` of type `int32`.
-    int32, 0 <= dimension < rank(input).  Describes which dimension
-    of the input Tensor to reduce across. For vectors, use dimension = 0.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `int64`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.argmin.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.argmin.md
deleted file mode 100644
index 002d5ed816..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.argmin.md
+++ /dev/null
@@ -1,17 +0,0 @@
-### `tf.argmin(input, dimension, name=None)` {#argmin}
-
-Returns the index with the smallest value across dimensions of a tensor.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-*  <b>`dimension`</b>: A `Tensor` of type `int32`.
-    int32, 0 <= dimension < rank(input).  Describes which dimension
-    of the input Tensor to reduce across. For vectors, use dimension = 0.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `int64`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_integer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_integer.md
new file mode 100644
index 0000000000..c75ba58765
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_integer.md
@@ -0,0 +1,30 @@
+### `tf.assert_integer(x, data=None, summarize=None, name=None)` {#assert_integer}
+
+Assert that `x` is of integer dtype.
+
+Example of adding a dependency to an operation:
+
+```python
+with tf.control_dependencies([tf.assert_integer(x)]):
+  output = tf.reduce_sum(x)
+```
+
+Example of adding dependency to the tensor being checked:
+
+```python
+x = tf.with_dependencies([tf.assert_integer(x)], x)
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: `Tensor` whose basetype is integer and is not quantized.
+*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
+    error message and first few entries of `x`.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_integer".
+
+##### Returns:
+
+  Op that raises `InvalidArgumentError` if `x == y` is False.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_less_equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_less_equal.md
deleted file mode 100644
index d740746a61..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_less_equal.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.assert_less_equal(x, y, data=None, summarize=None, name=None)` {#assert_less_equal}
-
-Assert the condition `x <= y` holds element-wise.
-
-Example of adding a dependency to an operation:
-
-```python
-with tf.control_dependencies([tf.assert_less_equal(x, y)]):
-  output = tf.reduce_sum(x)
-```
-
-Example of adding dependency to the tensor being checked:
-
-```python
-x = tf.with_dependencies([tf.assert_less_equal(x, y)], x)
-```
-
-This condition holds if for every pair of (possibly broadcast) elements
-`x[i]`, `y[i]`, we have `x[i] <= y[i]`.
-If both `x` and `y` are empty, this is trivially satisfied.
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`y`</b>: Numeric `Tensor`, same dtype as and broadcastable to `x`.
-*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
-    error message and first few entries of `x`, `y`.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_less_equal"
-
-##### Returns:
-
-  Op that raises `InvalidArgumentError` if `x <= y` is False.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_non_negative.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_non_negative.md
new file mode 100644
index 0000000000..47f07a698a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_non_negative.md
@@ -0,0 +1,34 @@
+### `tf.assert_non_negative(x, data=None, summarize=None, name=None)` {#assert_non_negative}
+
+Assert the condition `x >= 0` holds element-wise.
+
+Example of adding a dependency to an operation:
+
+```python
+with tf.control_dependencies([tf.assert_non_negative(x)]):
+  output = tf.reduce_sum(x)
+```
+
+Example of adding dependency to the tensor being checked:
+
+```python
+x = tf.with_dependencies([tf.assert_non_negative(x)], x)
+```
+
+Non-negative means, for every element `x[i]` of `x`, we have `x[i] >= 0`.
+If `x` is empty this is trivially satisfied.
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
+    error message and first few entries of `x`.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).
+    Defaults to "assert_non_negative".
+
+##### Returns:
+
+  Op raising `InvalidArgumentError` unless `x` is all non-negative.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_proper_iterable.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_proper_iterable.md
deleted file mode 100644
index ba01073765..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_proper_iterable.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.assert_proper_iterable(values)` {#assert_proper_iterable}
-
-Static assert that values is a "proper" iterable.
-
-`Ops` that expect iterables of `Tensor` can call this to validate input.
-Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.
-
-##### Args:
-
-
-*  <b>`values`</b>: Object to be checked.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `values` is not iterable or is one of
-    `Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_type.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_type.md
deleted file mode 100644
index e98b9dc4af..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.assert_type.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.assert_type(tensor, tf_type)` {#assert_type}
-
-Asserts that the given `Tensor` is of the specified type.
-
-##### Args:
-
-
-*  <b>`tensor`</b>: A tensorflow `Tensor`.
-*  <b>`tf_type`</b>: A tensorflow type (dtypes.float32, tf.int64, dtypes.bool, etc).
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the tensors data type doesn't match tf_type.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.audio_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.audio_summary.md
deleted file mode 100644
index a592378b88..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.audio_summary.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.audio_summary(tag, tensor, sample_rate, max_outputs=3, collections=None, name=None)` {#audio_summary}
-
-Outputs a `Summary` protocol buffer with audio.
-
-The summary has up to `max_outputs` summary values containing audio. The
-audio is built from `tensor` which must be 3-D with shape `[batch_size,
-frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
-assumed to be in the range of `[-1.0, 1.0]` with a sample rate of
-`sample_rate`.
-
-The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
-build the `tag` of the summary values:
-
-*  If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
-*  If `max_outputs` is greater than 1, the summary value tags are
-   generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
-
-##### Args:
-
-
-*  <b>`tag`</b>: A scalar `Tensor` of type `string`. Used to build the `tag`
-    of the summary values.
-*  <b>`tensor`</b>: A 3-D `float32` `Tensor` of shape `[batch_size, frames, channels]`
-    or a 2-D `float32` `Tensor` of shape `[batch_size, frames]`.
-*  <b>`sample_rate`</b>: The sample rate of the signal in hertz.
-*  <b>`max_outputs`</b>: Max number of batch elements to generate audio for.
-*  <b>`collections`</b>: Optional list of ops.GraphKeys.  The collections to add the
-    summary to.  Defaults to [ops.GraphKeys.SUMMARIES]
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A scalar `Tensor` of type `string`. The serialized `Summary` protocol
-  buffer.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.batch_ifft2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.batch_ifft2d.md
deleted file mode 100644
index 4476637122..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.batch_ifft2d.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.batch_ifft2d(input, name=None)` {#batch_ifft2d}
-
-Compute the inverse 2-dimensional discrete Fourier Transform over the inner-most
-
-2 dimensions of `input`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`.
-  A complex64 tensor of the same shape as `input`. The inner-most 2
-  dimensions of `input` are replaced with their inverse 2D Fourier Transform.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.batch_matrix_inverse.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.batch_matrix_inverse.md
new file mode 100644
index 0000000000..231056a05c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.batch_matrix_inverse.md
@@ -0,0 +1,28 @@
+### `tf.batch_matrix_inverse(input, adjoint=None, name=None)` {#batch_matrix_inverse}
+
+Calculates the inverse of square invertible matrices or their adjoints
+
+(conjugate transposes).
+
+The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+form square matrices. The output is a tensor of the same shape as the input
+containing the inverse for all input submatrices `[..., :, :]`.
+
+The op uses LU decomposition with partial pivoting to compute the inverses.
+
+If a matrix is not invertible there is no guarantee what the op does. It
+may detect the condition and raise an exception or it may simply return a
+garbage result.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
+    Shape is `[..., M, M]`.
+*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`. Shape is `[..., M, M]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.batch_to_space.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.batch_to_space.md
new file mode 100644
index 0000000000..d4a66ac8e0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.batch_to_space.md
@@ -0,0 +1,37 @@
+### `tf.batch_to_space(input, crops, block_size, name=None)` {#batch_to_space}
+
+BatchToSpace for 4-D tensors of type T.
+
+Rearranges (permutes) data from batch into blocks of spatial data, followed by
+cropping. This is the reverse transformation of SpaceToBatch. More specifically,
+this op outputs a copy of the input tensor where values from the `batch`
+dimension are moved in spatial blocks to the `height` and `width` dimensions,
+followed by cropping along the `height` and `width` dimensions.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. 4-D tensor with shape
+    `[batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
+      depth]`. Note that the batch size of the input tensor must be divisible by
+    `block_size * block_size`.
+*  <b>`crops`</b>: A `Tensor` of type `int32`.
+    2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
+    how many elements to crop from the intermediate result across the spatial
+    dimensions as follows:
+
+        crops = [[crop_top, crop_bottom], [crop_left, crop_right]]
+
+*  <b>`block_size`</b>: An `int`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+  4-D with shape `[batch, height, width, depth]`, where:
+
+        height = height_pad - crop_top - crop_bottom
+        width = width_pad - crop_left - crop_right
+
+  The attr `block_size` must be greater than one. It indicates the block size.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ceil.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ceil.md
new file mode 100644
index 0000000000..34e4a7feed
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ceil.md
@@ -0,0 +1,14 @@
+### `tf.ceil(x, name=None)` {#ceil}
+
+Returns element-wise smallest integer in not less than x.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.check_numerics.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.check_numerics.md
new file mode 100644
index 0000000000..46a8f6f7db
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.check_numerics.md
@@ -0,0 +1,18 @@
+### `tf.check_numerics(tensor, message, name=None)` {#check_numerics}
+
+Checks a tensor for NaN and Inf values.
+
+When run, reports an `InvalidArgument` error if `tensor` has any values
+that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is.
+
+##### Args:
+
+
+*  <b>`tensor`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
+*  <b>`message`</b>: A `string`. Prefix of the error message.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.clip_by_average_norm.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.clip_by_average_norm.md
new file mode 100644
index 0000000000..4598e183d8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.clip_by_average_norm.md
@@ -0,0 +1,29 @@
+### `tf.clip_by_average_norm(t, clip_norm, name=None)` {#clip_by_average_norm}
+
+Clips tensor values to a maximum average L2-norm.
+
+Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
+normalizes `t` so that its average L2-norm is less than or equal to
+`clip_norm`. Specifically, if the average L2-norm is already less than or
+equal to `clip_norm`, then `t` is not modified. If the average L2-norm is
+greater than `clip_norm`, then this operation returns a tensor of the same
+type and shape as `t` with its values set to:
+
+`t * clip_norm / l2norm_avg(t)`
+
+In this case, the average L2-norm of the output tensor is `clip_norm`.
+
+This operation is typically used to clip gradients before applying them with
+an optimizer.
+
+##### Args:
+
+
+*  <b>`t`</b>: A `Tensor`.
+*  <b>`clip_norm`</b>: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A clipped `Tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.clip_by_global_norm.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.clip_by_global_norm.md
deleted file mode 100644
index a40f621bf4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.clip_by_global_norm.md
+++ /dev/null
@@ -1,50 +0,0 @@
-### `tf.clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None)` {#clip_by_global_norm}
-
-Clips values of multiple tensors by the ratio of the sum of their norms.
-
-Given a tuple or list of tensors `t_list`, and a clipping ratio `clip_norm`,
-this operation returns a list of clipped tensors `list_clipped`
-and the global norm (`global_norm`) of all tensors in `t_list`. Optionally,
-if you've already computed the global norm for `t_list`, you can specify
-the global norm with `use_norm`.
-
-To perform the clipping, the values `t_list[i]` are set to:
-
-    t_list[i] * clip_norm / max(global_norm, clip_norm)
-
-where:
-
-    global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))
-
-If `clip_norm > global_norm` then the entries in `t_list` remain as they are,
-otherwise they're all shrunk by the global ratio.
-
-Any of the entries of `t_list` that are of type `None` are ignored.
-
-This is the correct way to perform gradient clipping (for example, see
-[Pascanu et al., 2012](http://arxiv.org/abs/1211.5063)
-([pdf](http://arxiv.org/pdf/1211.5063.pdf))).
-
-However, it is slower than `clip_by_norm()` because all the parameters must be
-ready before the clipping operation can be performed.
-
-##### Args:
-
-
-*  <b>`t_list`</b>: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.
-*  <b>`clip_norm`</b>: A 0-D (scalar) `Tensor` > 0. The clipping ratio.
-*  <b>`use_norm`</b>: A 0-D (scalar) `Tensor` of type `float` (optional). The global
-    norm to use. If not provided, `global_norm()` is used to compute the norm.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-
-*  <b>`list_clipped`</b>: A list of `Tensors` of the same type as `list_t`.
-*  <b>`global_norm`</b>: A 0-D (scalar) `Tensor` representing the global norm.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `t_list` is not a sequence.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.clip_by_value.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.clip_by_value.md
new file mode 100644
index 0000000000..7cd7e0311e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.clip_by_value.md
@@ -0,0 +1,21 @@
+### `tf.clip_by_value(t, clip_value_min, clip_value_max, name=None)` {#clip_by_value}
+
+Clips tensor values to a specified min and max.
+
+Given a tensor `t`, this operation returns a tensor of the same type and
+shape as `t` with its values clipped to `clip_value_min` and `clip_value_max`.
+Any values less than `clip_value_min` are set to `clip_value_min`. Any values
+greater than `clip_value_max` are set to `clip_value_max`.
+
+##### Args:
+
+
+*  <b>`t`</b>: A `Tensor`.
+*  <b>`clip_value_min`</b>: A 0-D (scalar) `Tensor`. The minimum value to clip by.
+*  <b>`clip_value_max`</b>: A 0-D (scalar) `Tensor`. The maximum value to clip by.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A clipped `Tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.conj.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.conj.md
deleted file mode 100644
index 6df004b0cd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.conj.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.conj(input, name=None)` {#conj}
-
-Returns the complex conjugate of a complex number.
-
-Given a tensor `input` of complex numbers, this operation returns a tensor of
-complex numbers that are the complex conjugate of each element in `input`. The
-complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the
-real part and *b* is the imaginary part.
-
-The complex conjugate returned by this operation is of the form \\(a - bj\\).
-
-For example:
-
-```
-# tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
-tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j]
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.copy_graph.copy_op_to_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.copy_graph.copy_op_to_graph.md
deleted file mode 100644
index d549132fa2..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.copy_graph.copy_op_to_graph.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.contrib.copy_graph.copy_op_to_graph(org_instance, to_graph, variables, scope='')` {#copy_op_to_graph}
-
-Given an `Operation` 'org_instance` from one `Graph`,
-initializes and returns a copy of it from another `Graph`,
-under the specified scope (default `""`).
-
-The copying is done recursively, so any `Operation` whose output
-is required to evaluate the `org_instance`, is also copied (unless
-already done).
-
-Since `Variable` instances are copied separately, those required
-to evaluate `org_instance` must be provided as input.
-
-Args:
-org_instance: An `Operation` from some `Graph`. Could be a
-    `Placeholder` as well.
-to_graph: The `Graph` to copy `org_instance` to.
-variables: An iterable of `Variable` instances to copy `org_instance` to.
-scope: A scope for the new `Variable` (default `""`).
-
-##### Returns:
-
-    The copied `Operation` from `to_graph`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `org_instance` is not an `Operation` or `Tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md
new file mode 100644
index 0000000000..65b516af08
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md
@@ -0,0 +1,195 @@
+Abstract base class for probability distributions.
+
+This class, along with `ContinuousDistribution` and `DiscreteDistribution`,
+defines the API for probability distributions.
+
+Users will never instantiate a `BaseDistribution`, but will instead
+instantiate subclasses of either `ContinuousDistribution` or
+`DiscreteDistribution`.
+
+Developers of new distributions should prefer to subclass
+`ContinuousDistribution` or `DiscreteDistribution`.
+
+### API
+
+The key methods for probability distributions are defined here. The likelihood
+functions (`pdf`, `log_pdf`) and (`pmf`, `log_pmf`) are defined in
+`ContinuousDistribution` and `DiscreteDistribution`, respectively.
+
+To keep ops generated by the distribution tied together by name, subclasses
+should override `name` and use it to preprend names of ops in other methods
+(see `cdf` for an example).
+
+Subclasses that wish to support `cdf` and `log_cdf` can override `log_cdf`
+and use the base class's implementation for `cdf`.
+
+### Broadcasting, batching, and shapes
+
+All distributions support batches of independent distributions of that type.
+The batch shape is determined by broadcasting together the parameters.
+
+The shape of arguments to `__init__`, `cdf`, `log_cdf`, and the likelihood
+functions defined in `ContinuousDistribution` and `DiscreteDistribution`
+reflect this broadcasting, as does the return value of `sample`.
+
+`sample_shape = (n,) + batch_shape + event_shape`, where `sample_shape` is the
+shape of the `Tensor` returned from `sample`, `n` is the number of samples,
+`batch_shape` defines how many independent distributions there are, and
+`event_shape` defines the shape of samples from each of those independent
+distributions. Samples are independent along the `batch_shape` dimensions,
+but not necessarily so along the `event_shape` dimensions (dependending on
+the particulars of the underlying distribution).
+
+Using the `Uniform` distribution as an example:
+
+```python
+minval = 3.0
+maxval = [[4.0, 6.0],
+          [10.0, 12.0]]
+
+# Broadcasting:
+# This instance represents 4 Uniform distributions. Each has a lower bound at
+# 3.0 as the `minval` parameter was broadcasted to match `maxval`'s shape.
+u = Uniform(minval, maxval)
+
+# `event_shape` is `TensorShape([])`.
+event_shape = u.get_event_shape()
+# `event_shape_t` is a `Tensor` which will evaluate to a scalar 1.
+event_shape_t = u.event_shape
+
+# Sampling returns a sample per distribution.  `samples` has shape
+# (5, 2, 2), which is (n,) + batch_shape + event_shape, where n=5,
+# batch_shape=(2, 2), and event_shape=().
+samples = u.sample(5)
+
+# The broadcasting holds across methods. Here we use `cdf` as an example. The
+# same holds for `log_cdf` and the likelihood functions.
+
+# `cum_prob` has shape (2, 2) as the `value` argument was broadcasted to the
+# shape of the `Uniform` instance.
+cum_prob_broadcast = u.cdf(4.0)
+
+# `cum_prob`'s shape is (2, 2), one per distribution. No broadcasting
+# occurred.
+cum_prob_per_dist = u.cdf([[4.0, 5.0],
+                           [6.0, 7.0]])
+
+# INVALID as the `value` argument is not broadcastable to the distribution's
+# shape.
+cum_prob_invalid = u.cdf([4.0, 5.0, 6.0])
+```
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.batch_shape(name=None)` {#BaseDistribution.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.cdf(value, name='cdf')` {#BaseDistribution.cdf}
+
+Cumulative distribution function.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.dtype` {#BaseDistribution.dtype}
+
+dtype of samples from this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.entropy(name=None)` {#BaseDistribution.entropy}
+
+Entropy of the distribution in nats.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.event_shape(name=None)` {#BaseDistribution.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.get_batch_shape()` {#BaseDistribution.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.get_event_shape()` {#BaseDistribution.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.log_cdf(value, name='log_cdf')` {#BaseDistribution.log_cdf}
+
+Log CDF.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.mean` {#BaseDistribution.mean}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.name` {#BaseDistribution.name}
+
+Name to prepend to all ops.
+
+
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.sample(n, seed=None, name=None)` {#BaseDistribution.sample}
+
+Generate `n` samples.
+
+##### Args:
+
+
+*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
+      with values of type `self.dtype`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ContinuousDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ContinuousDistribution.md
new file mode 100644
index 0000000000..e474870cd4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.ContinuousDistribution.md
@@ -0,0 +1,153 @@
+Base class for continuous probability distributions.
+
+`ContinuousDistribution` defines the API for the likelihood functions `pdf`
+and `log_pdf` of continuous probability distributions, and a property
+`is_reparameterized` (returning `True` or `False`) which describes
+whether the samples of this distribution are calculated in a differentiable
+way from a non-parameterized distribution.  For example, the `Normal`
+distribution with parameters `mu` and `sigma` is reparameterized as
+
+```Normal(mu, sigma) = sigma * Normal(0, 1) + mu```
+
+Subclasses must override `pdf` and `log_pdf` but one can call this base
+class's implementation.  They must also override the `is_reparameterized`
+property.
+
+See `BaseDistribution` for more information on the API for probability
+distributions.
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.batch_shape(name=None)` {#ContinuousDistribution.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.cdf(value, name='cdf')` {#ContinuousDistribution.cdf}
+
+Cumulative distribution function.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.dtype` {#ContinuousDistribution.dtype}
+
+dtype of samples from this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.entropy(name=None)` {#ContinuousDistribution.entropy}
+
+Entropy of the distribution in nats.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.event_shape(name=None)` {#ContinuousDistribution.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.get_batch_shape()` {#ContinuousDistribution.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.get_event_shape()` {#ContinuousDistribution.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.is_reparameterized` {#ContinuousDistribution.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.log_cdf(value, name='log_cdf')` {#ContinuousDistribution.log_cdf}
+
+Log CDF.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.log_pdf(value, name='log_pdf')` {#ContinuousDistribution.log_pdf}
+
+Log of the probability density function.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.mean` {#ContinuousDistribution.mean}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.name` {#ContinuousDistribution.name}
+
+Name to prepend to all ops.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.pdf(value, name='pdf')` {#ContinuousDistribution.pdf}
+
+Probability density function.
+
+
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.sample(n, seed=None, name=None)` {#ContinuousDistribution.sample}
+
+Generate `n` samples.
+
+##### Args:
+
+
+*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
+      with values of type `self.dtype`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.StudentT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.StudentT.md
new file mode 100644
index 0000000000..816e5d5a83
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.StudentT.md
@@ -0,0 +1,245 @@
+Student's t distribution with degree-of-freedom parameter df.
+
+#### Mathematical details
+
+The PDF of this distribution is:
+
+`f(t) = gamma((df+1)/2)/sqrt(df*pi)/gamma(df/2)*(1+t^2/df)^(-(df+1)/2)`
+
+#### Examples
+
+Examples of initialization of one or a batch of distributions.
+
+```python
+# Define a single scalar Student t distribution.
+single_dist = tf.contrib.distributions.StudentT(df=3)
+
+# Evaluate the pdf at 1, returning a scalar Tensor.
+single_dist.pdf(1.)
+
+# Define a batch of two scalar valued Student t's.
+# The first has degrees of freedom 2, mean 1, and scale 11.
+# The second 3, 2 and 22.
+multi_dist = tf.contrib.distributions.StudentT(df=[2, 3],
+                                               mu=[1, 2.],
+                                               sigma=[11, 22.])
+
+# Evaluate the pdf of the first distribution on 0, and the second on 1.5,
+# returning a length two tensor.
+multi_dist.pdf([0, 1.5])
+
+# Get 3 samples, returning a 3 x 2 tensor.
+multi_dist.sample(3)
+```
+
+Arguments are broadcast when possible.
+
+```python
+# Define a batch of two Student's t distributions.
+# Both have df 2 and mean 1, but different scales.
+dist = tf.contrib.distributions.StudentT(df=2, mu=1, sigma=[11, 22.])
+
+# Evaluate the pdf of both distributions on the same point, 3.0,
+# returning a length 2 tensor.
+dist.pdf(3.0)
+```
+- - -
+
+#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, name='StudentT')` {#StudentT.__init__}
+
+Construct Student's t distributions.
+
+The distributions have degree of freedom `df`, mean `mu`, and scale `sigma`.
+
+The parameters `df`, `mu`, and `sigma` must be shaped in a way that supports
+broadcasting (e.g. `df + mu + sigma` is a valid operation).
+
+##### Args:
+
+
+*  <b>`df`</b>: `float` or `double` tensor, the degrees of freedom of the
+    distribution(s). `df` must contain only positive values.
+*  <b>`mu`</b>: `float` or `double` tensor, the means of the distribution(s).
+*  <b>`sigma`</b>: `float` or `double` tensor, the scaling factor for the
+    distribution(s). `sigma` must contain only positive values.
+    Note that `sigma` is not the standard deviation of this distribution.
+*  <b>`name`</b>: The name to give Ops created by the initializer.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if mu and sigma are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.batch_shape(name='batch_shape')` {#StudentT.batch_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.cdf(value, name='cdf')` {#StudentT.cdf}
+
+Cumulative distribution function.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.df` {#StudentT.df}
+
+Degrees of freedom in these Student's t distribution(s).
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.dtype` {#StudentT.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.entropy(name='entropy')` {#StudentT.entropy}
+
+The entropy of Student t distribution(s).
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.event_shape(name='event_shape')` {#StudentT.event_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.get_batch_shape()` {#StudentT.get_batch_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.get_event_shape()` {#StudentT.get_event_shape}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.is_reparameterized` {#StudentT.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf')` {#StudentT.log_cdf}
+
+Log CDF.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.log_pdf(x, name='log_pdf')` {#StudentT.log_pdf}
+
+Log pdf of observations in `x` under these Student's t-distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `df`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.mean` {#StudentT.mean}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.mu` {#StudentT.mu}
+
+Locations of these Student's t distribution(s).
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.name` {#StudentT.name}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.pdf(x, name='pdf')` {#StudentT.pdf}
+
+The PDF of observations in `x` under these Student's t distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `df`, `mu`, and
+    `sigma`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.sample(n, seed=None, name='sample')` {#StudentT.sample}
+
+Sample `n` observations from the Student t Distributions.
+
+##### Args:
+
+
+*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`seed`</b>: Python integer, the random seed.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
+      with values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.sigma` {#StudentT.sigma}
+
+Scaling factors of these Student's t distribution(s).
+
+
+- - -
+
+#### `tf.contrib.distributions.StudentT.variance` {#StudentT.variance}
+
+
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.normal_congugates_known_sigma_predictive.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.normal_congugates_known_sigma_predictive.md
deleted file mode 100644
index 89e4e5ca3c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.normal_congugates_known_sigma_predictive.md
+++ /dev/null
@@ -1,55 +0,0 @@
-### `tf.contrib.distributions.normal_congugates_known_sigma_predictive(prior, sigma, s, n)` {#normal_congugates_known_sigma_predictive}
-
-Posterior predictive Normal distribution w. conjugate prior on the mean.
-
-This model assumes that `n` observations (with sum `s`) come from a
-Normal with unknown mean `mu` (described by the Normal `prior`)
-and known variance `sigma^2`.  The "known sigma predictive"
-is the distribution of new observations, conditioned on the existing
-observations and our prior.
-
-Accepts a prior Normal distribution object, having parameters
-`mu0` and `sigma0`, as well as known `sigma` values of the predictive
-distribution(s) (also assumed Normal),
-and statistical estimates `s` (the sum(s) of the observations) and
-`n` (the number(s) of observations).
-
-Calculates the Normal distribution(s) `p(x | sigma^2)`:
-
-```
-  p(x | sigma^2) = int N(x | mu, sigma^2) N(mu | prior.mu, prior.sigma^2) dmu
-                 = N(x | prior.mu, 1/(sigma^2 + prior.sigma^2))
-```
-
-Returns the predictive posterior distribution object, with parameters
-`(mu', sigma'^2)`, where:
-
-```
-sigma_n^2 = 1/(1/sigma0^2 + n/sigma^2),
-mu' = (mu0/sigma0^2 + s/sigma^2) * sigma_n^2.
-sigma'^2 = sigma_n^2 + sigma^2,
-```
-
-Distribution parameters from `prior`, as well as `sigma`, `s`, and `n`.
-will broadcast in the case of multidimensional sets of parameters.
-
-##### Args:
-
-
-*  <b>`prior`</b>: `Normal` object of type `dtype`:
-    the prior distribution having parameters `(mu0, sigma0)`.
-*  <b>`sigma`</b>: tensor of type `dtype`, taking values `sigma > 0`.
-    The known stddev parameter(s).
-*  <b>`s`</b>: Tensor of type `dtype`.  The sum(s) of observations.
-*  <b>`n`</b>: Tensor of type `int`.  The number(s) of observations.
-
-##### Returns:
-
-  A new Normal predictive distribution object.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if dtype of `s` does not match `dtype`, or `prior` is not a
-    Normal object.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.fully_connected.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.fully_connected.md
new file mode 100644
index 0000000000..da63a14cd9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.fully_connected.md
@@ -0,0 +1,46 @@
+### `tf.contrib.layers.fully_connected(*args, **kwargs)` {#fully_connected}
+
+Adds a fully connected layer.
+
+`fully_connected` creates a variable called `weights`, representing a fully
+connected weight matrix, which is multiplied by the `inputs` to produce a
+`Tensor` of hidden units. If a `normalizer_fn` is provided (such as
+`batch_norm`), it is then applied. Otherwise, if `normalizer_fn` is
+None and a `biases_initializer` is provided then a `biases` variable would be
+created and added the hidden units. Finally, if `activation_fn` is not `None`,
+it is applied to the hidden units as well.
+
+Note: that if `inputs` have a rank greater than 2, then `inputs` is flattened
+prior to the initial matrix multiply by `weights`.
+
+##### Args:
+
+
+*  <b>`inputs`</b>: A tensor of with at least rank 2 and value for the last dimension,
+    i.e. `[batch_size, depth]`, `[None, None, None, channels]`.
+*  <b>`num_outputs`</b>: Integer, the number of output units in the layer.
+*  <b>`activation_fn`</b>: activation function.
+*  <b>`normalizer_fn`</b>: normalization function to use instead of `biases`. If
+    `normalize_fn` is provided then `biases_initializer` and
+    `biases_regularizer` are ignored and `biases` are not created nor added.
+*  <b>`normalizer_params`</b>: normalization function parameters.
+*  <b>`weights_initializer`</b>: An initializer for the weights.
+*  <b>`weights_regularizer`</b>: Optional regularizer for the weights.
+*  <b>`biases_initializer`</b>: An initializer for the biases. If None skip biases.
+*  <b>`biases_regularizer`</b>: Optional regularizer for the biases.
+*  <b>`reuse`</b>: whether or not the layer and its variables should be reused. To be
+    able to reuse the layer scope must be given.
+*  <b>`variables_collections`</b>: Optional list of collections for all the variables or
+    a dictionay containing a different list of collection per variable.
+*  <b>`outputs_collections`</b>: collection to add the outputs.
+*  <b>`scope`</b>: Optional scope for variable_op_scope.
+
+##### Returns:
+
+   the tensor variable representing the result of the series of operations.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if x has rank less than 2 or if its last dimension is not set.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.l1_regularizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.l1_regularizer.md
deleted file mode 100644
index 1aa8074980..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.l1_regularizer.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.contrib.layers.l1_regularizer(scale)` {#l1_regularizer}
-
-Returns a function that can be used to apply L1 regularization to weights.
-
-L1 regularization encourages sparsity.
-
-##### Args:
-
-
-*  <b>`scale`</b>: A scalar multiplier `Tensor`. 0.0 disables the regularizer.
-
-##### Returns:
-
-  A function with signature `l1(weights, name=None)` that apply L1
-  regularization.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If scale is outside of the range [0.0, 1.0] or if scale is not a
-  float.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.summarize_activation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.summarize_activation.md
deleted file mode 100644
index 3aed0ff43c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.summarize_activation.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.contrib.layers.summarize_activation(op)` {#summarize_activation}
-
-Summarize an activation.
-
-This applies the given activation and adds useful summaries specific to the
-activation.
-
-##### Args:
-
-
-*  <b>`op`</b>: The tensor to summarize (assumed to be a layer activation).
-
-##### Returns:
-
-  The summary op created to summarize `op`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.summarize_activations.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.summarize_activations.md
deleted file mode 100644
index dc2e7a6044..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.layers.summarize_activations.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.contrib.layers.summarize_activations(name_filter=None, summarizer=summarize_activation)` {#summarize_activations}
-
-Summarize activations, using `summarize_activation` to summarize.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.NanLossDuringTrainingError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.NanLossDuringTrainingError.md
deleted file mode 100644
index 8b13789179..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.NanLossDuringTrainingError.md
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowDNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowDNNClassifier.md
deleted file mode 100644
index 03c779259a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowDNNClassifier.md
+++ /dev/null
@@ -1,302 +0,0 @@
-TensorFlow DNN Classifier model.
-
-Parameters:
-  hidden_units: List of hidden units per layer.
-  n_classes: Number of classes in the target.
-  batch_size: Mini batch size.
-  steps: Number of steps to run over data.
-  optimizer: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
-  learning_rate: If this is constant float value, no decay function is used.
-    Instead, a customized decay function can be passed that accepts
-    global_step as parameter and returns a Tensor.
-    e.g. exponential decay function:
-    def exp_decay(global_step):
-        return tf.train.exponential_decay(
-            learning_rate=0.1, global_step,
-            decay_steps=2, decay_rate=0.001)
-  class_weight: None or list of n_classes floats. Weight associated with
-    classes for loss computation. If not given, all classes are
-    supposed to have weight one.
-  continue_training: when continue_training is True, once initialized
-    model will be continuely trained on every call of fit.
-  config: RunConfig object that controls the configurations of the
-    session, e.g. num_cores, gpu_memory_fraction, etc.
-  dropout: When not None, the probability we will drop out a given coordinate.
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.__init__(hidden_units, n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1, dropout=None)` {#TensorFlowDNNClassifier.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.bias_` {#TensorFlowDNNClassifier.bias_}
-
-Returns bias of the DNN's bias layers.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowDNNClassifier.evaluate}
-
-See base class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowDNNClassifier.fit}
-
-Builds a neural network model given provided `model_fn` and training
-data X and y.
-
-Note: called first time constructs the graph and initializers
-variables. Consecutives times it will continue training the same model.
-This logic follows partial_fit() interface in scikit-learn.
-
-To restart learning, create new estimator.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class labels in classification, real numbers in regression).
-
-*  <b>`steps`</b>: int, number of steps to train.
-         If None or 0, train for `self.steps`.
-*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
-    invoke early stopping.
-*  <b>`logdir`</b>: the directory to save the log file that can be used for
-  optional visualization.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.get_params(deep=True)` {#TensorFlowDNNClassifier.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.get_tensor(name)` {#TensorFlowDNNClassifier.get_tensor}
-
-Returns tensor by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.get_tensor_value(name)` {#TensorFlowDNNClassifier.get_tensor_value}
-
-Returns value of the tensor give by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.get_variable_names()` {#TensorFlowDNNClassifier.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.model_dir` {#TensorFlowDNNClassifier.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.partial_fit(x, y)` {#TensorFlowDNNClassifier.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class label in classification, real numbers in regression).
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.predict(x, axis=1, batch_size=None)` {#TensorFlowDNNClassifier.predict}
-
-Predict class or regression for X.
-
-For a classification model, the predicted class for each sample in X is
-returned. For a regression model, the predicted value based on X is
-returned.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`axis`</b>: Which axis to argmax for classification.
-    By default axis 1 (next after batch) is used.
-    Use 2 for sequence predictions.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member
-    variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
-  value.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.predict_proba(x, batch_size=None)` {#TensorFlowDNNClassifier.predict_proba}
-
-Predict class probability of the input samples X.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
-  probabilities for each class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.restore(cls, path, config=None)` {#TensorFlowDNNClassifier.restore}
-
-Restores model from give path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Path to the checkpoints and other model information.
-*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
-      reconfigured.
-
-##### Returns:
-
-  Estimator, object of the subclass of TensorFlowEstimator.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.save(path)` {#TensorFlowDNNClassifier.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.set_params(**params)` {#TensorFlowDNNClassifier.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.train(input_fn, steps, monitors=None)` {#TensorFlowDNNClassifier.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowDNNClassifier.weights_` {#TensorFlowDNNClassifier.weights_}
-
-Returns weights of the DNN weight layers.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowLinearClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowLinearClassifier.md
deleted file mode 100644
index 469aa72b3a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowLinearClassifier.md
+++ /dev/null
@@ -1,279 +0,0 @@
-TensorFlow Linear Classifier model.
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.__init__(n_classes, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowLinearClassifier.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.bias_` {#TensorFlowLinearClassifier.bias_}
-
-Returns weights of the linear classifier.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowLinearClassifier.evaluate}
-
-See base class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowLinearClassifier.fit}
-
-Builds a neural network model given provided `model_fn` and training
-data X and y.
-
-Note: called first time constructs the graph and initializers
-variables. Consecutives times it will continue training the same model.
-This logic follows partial_fit() interface in scikit-learn.
-
-To restart learning, create new estimator.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class labels in classification, real numbers in regression).
-
-*  <b>`steps`</b>: int, number of steps to train.
-         If None or 0, train for `self.steps`.
-*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
-    invoke early stopping.
-*  <b>`logdir`</b>: the directory to save the log file that can be used for
-  optional visualization.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.get_params(deep=True)` {#TensorFlowLinearClassifier.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.get_tensor(name)` {#TensorFlowLinearClassifier.get_tensor}
-
-Returns tensor by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.get_tensor_value(name)` {#TensorFlowLinearClassifier.get_tensor_value}
-
-Returns value of the tensor give by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.get_variable_names()` {#TensorFlowLinearClassifier.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.model_dir` {#TensorFlowLinearClassifier.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.partial_fit(x, y)` {#TensorFlowLinearClassifier.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class label in classification, real numbers in regression).
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.predict(x, axis=1, batch_size=None)` {#TensorFlowLinearClassifier.predict}
-
-Predict class or regression for X.
-
-For a classification model, the predicted class for each sample in X is
-returned. For a regression model, the predicted value based on X is
-returned.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`axis`</b>: Which axis to argmax for classification.
-    By default axis 1 (next after batch) is used.
-    Use 2 for sequence predictions.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member
-    variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
-  value.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.predict_proba(x, batch_size=None)` {#TensorFlowLinearClassifier.predict_proba}
-
-Predict class probability of the input samples X.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
-  probabilities for each class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.restore(cls, path, config=None)` {#TensorFlowLinearClassifier.restore}
-
-Restores model from give path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Path to the checkpoints and other model information.
-*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
-      reconfigured.
-
-##### Returns:
-
-  Estimator, object of the subclass of TensorFlowEstimator.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.save(path)` {#TensorFlowLinearClassifier.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.set_params(**params)` {#TensorFlowLinearClassifier.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.train(input_fn, steps, monitors=None)` {#TensorFlowLinearClassifier.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.weights_` {#TensorFlowLinearClassifier.weights_}
-
-Returns weights of the linear classifier.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.run_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.run_n.md
deleted file mode 100644
index 8fa8f09cb5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.run_n.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.contrib.learn.run_n(output_dict, feed_dict=None, restore_checkpoint_path=None, n=1)` {#run_n}
-
-Run `output_dict` tensors `n` times, with the same `feed_dict` each run.
-
-##### Args:
-
-
-*  <b>`output_dict`</b>: A `dict` mapping string names to tensors to run. Must all be
-    from the same graph.
-*  <b>`feed_dict`</b>: `dict` of input values to feed each run.
-*  <b>`restore_checkpoint_path`</b>: A string containing the path to a checkpoint to
-    restore.
-*  <b>`n`</b>: Number of times to repeat.
-
-##### Returns:
-
-  A list of `n` `dict` objects, each containing values read from `output_dict`
-  tensors.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.train.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.train.md
deleted file mode 100644
index 65057636ce..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.train.md
+++ /dev/null
@@ -1,62 +0,0 @@
-### `tf.contrib.learn.train(graph, output_dir, train_op, loss_op, global_step_tensor=None, init_op=None, init_feed_dict=None, init_fn=None, log_every_steps=10, supervisor_is_chief=True, supervisor_master='', supervisor_save_model_secs=600, supervisor_save_summaries_steps=100, feed_fn=None, max_steps=None, fail_on_nan_loss=True, monitors=None)` {#train}
-
-Train a model.
-
-Given `graph`, a directory to write outputs to (`output_dir`), and some ops,
-run a training loop. The given `train_op` performs one step of training on the
-model. The `loss_op` represents the objective function of the training. It is
-expected to increment the `global_step_tensor`, a scalar integer tensor
-counting training steps. This function uses `Supervisor` to initialize the
-graph (from a checkpoint if one is available in `output_dir`), write summaries
-defined in the graph, and write regular checkpoints as defined by
-`supervisor_save_model_secs`.
-
-Training continues until `global_step_tensor` evaluates to `max_steps`, or, if
-`fail_on_nan_loss`, until `loss_op` evaluates to `NaN`. In that case the
-program is terminated with exit code 1.
-
-##### Args:
-
-
-*  <b>`graph`</b>: A graph to train. It is expected that this graph is not in use
-    elsewhere.
-*  <b>`output_dir`</b>: A directory to write outputs to.
-*  <b>`train_op`</b>: An op that performs one training step when run.
-*  <b>`loss_op`</b>: A scalar loss tensor.
-*  <b>`global_step_tensor`</b>: A tensor representing the global step. If none is given,
-    one is extracted from the graph using the same logic as in `Supervisor`.
-*  <b>`init_op`</b>: An op that initializes the graph. If `None`, use `Supervisor`'s
-    default.
-*  <b>`init_feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
-    This feed dictionary will be used when `init_op` is evaluated.
-*  <b>`init_fn`</b>: Optional callable passed to Supervisor to initialize the model.
-*  <b>`log_every_steps`</b>: Output logs regularly. The logs contain timing data and the
-    current loss.
-*  <b>`supervisor_is_chief`</b>: Whether the current process is the chief supervisor in
-    charge of restoring the model and running standard services.
-*  <b>`supervisor_master`</b>: The master string to use when preparing the session.
-*  <b>`supervisor_save_model_secs`</b>: Save a checkpoint every
-    `supervisor_save_model_secs` seconds when training.
-*  <b>`supervisor_save_summaries_steps`</b>: Save summaries every
-    `supervisor_save_summaries_steps` seconds when training.
-*  <b>`feed_fn`</b>: A function that is called every iteration to produce a `feed_dict`
-    passed to `session.run` calls. Optional.
-*  <b>`max_steps`</b>: Train until `global_step_tensor` evaluates to this value.
-*  <b>`fail_on_nan_loss`</b>: If true, raise `NanLossDuringTrainingError` if `loss_op`
-    evaluates to `NaN`. If false, continue training as if nothing happened.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-    inside the training loop.
-
-##### Returns:
-
-  The final loss value.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `global_step_tensor` is not provided. See
-      `tf.contrib.framework.get_global_step` for how we look it up if not
-      provided explicitly.
-*  <b>`NanLossDuringTrainingError`</b>: If `fail_on_nan_loss` is `True`, and loss ever
-      evaluates to `NaN`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.confusion_matrix.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.confusion_matrix.md
deleted file mode 100644
index a57fa44318..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.confusion_matrix.md
+++ /dev/null
@@ -1,45 +0,0 @@
-### `tf.contrib.metrics.confusion_matrix(predictions, labels, num_classes=None, name=None)` {#confusion_matrix}
-
-Computes the confusion matrix from predictions and labels
-
-Calculate the Confusion Matrix for a pair of prediction and
-label 1-D int arrays.
-
-Considering a prediction array such as: `[1, 2, 3]`
-And a label array such as: `[2, 2, 3]`
-
-##### The confusion matrix returned would be the following one:
-
-    [[0, 0, 0]
-     [0, 1, 0]
-     [0, 1, 0]
-     [0, 0, 1]]
-
-Where the matrix rows represent the prediction labels and the columns
-represents the real labels. The confusion matrix is always a 2-D array
-of shape [n, n], where n is the number of valid labels for a given
-classification task. Both prediction and labels must be 1-D arrays of
-the same shape in order for this function to work.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: A 1-D array represeting the predictions for a given
-               classification.
-*  <b>`labels`</b>: A 1-D represeting the real labels for the classification task.
-*  <b>`num_classes`</b>: The possible number of labels the classification task can
-               have. If this value is not provided, it will be calculated
-               using both predictions and labels array.
-*  <b>`name`</b>: Scope name.
-
-##### Returns:
-
-  A l X l matrix represeting the confusion matrix, where l in the number of
-  possible labels in the classification task.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If both predictions and labels are not 1-D vectors and do not
-              have the same size.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.set_intersection.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.set_intersection.md
deleted file mode 100644
index bd42f3fa01..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.set_intersection.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.contrib.metrics.set_intersection(a, b, validate_indices=True)` {#set_intersection}
-
-Compute set intersection of elements in last dimension of `a` and `b`.
-
-All but the last dimension of `a` and `b` must match.
-
-##### Args:
-
-
-*  <b>`a`</b>: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
-      must be sorted in row-major order.
-*  <b>`b`</b>: `Tensor` or `SparseTensor` of the same type as `a`. Must be
-      `SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
-      sorted in row-major order.
-*  <b>`validate_indices`</b>: Whether to validate the order and range of sparse indices
-     in `a` and `b`.
-
-##### Returns:
-
-  A `SparseTensor` with the same rank as `a` and `b`, and all but the last
-  dimension the same. Elements along the last dimension contain the
-  intersections.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_percentage_less.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_percentage_less.md
deleted file mode 100644
index 40ddae4d31..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_percentage_less.md
+++ /dev/null
@@ -1,47 +0,0 @@
-### `tf.contrib.metrics.streaming_percentage_less(values, threshold, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_percentage_less}
-
-Computes the percentage of values less than the given threshold.
-
-The `streaming_percentage_less` function creates two local variables,
-`total` and `count` that are used to compute the percentage of `values` that
-fall below `threshold`. This rate is ultimately returned as `percentage`
-which is an idempotent operation that simply divides `total` by `count.
-To facilitate the estimation of the percentage of values that fall under
-`threshold` over multiple batches of data, the function creates an
-`update_op` operation whose behavior is dependent on the value of
-`ignore_mask`. If `ignore_mask` is None, then `update_op`
-increments `total` with the number of elements of `values` that are less
-than `threshold` and `count` with the number of elements in `values`. If
-`ignore_mask` is not `None`, then `update_op` increments `total` with the
-number of elements of `values` that are less than `threshold` and whose
-corresponding entries in `ignore_mask` are False, and `count` is incremented
-with the number of elements of `ignore_mask` that are False.
-
-##### Args:
-
-
-*  <b>`values`</b>: A numeric `Tensor` of arbitrary size.
-*  <b>`threshold`</b>: A scalar threshold.
-*  <b>`ignore_mask`</b>: An optional mask of the same shape as 'values' which indicates
-    which elements to ignore during metric computation.
-*  <b>`metrics_collections`</b>: An optional list of collections that the metric
-    value variable should be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that the metric update
-    ops should be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`percentage`</b>: A tensor representing the current mean, the value of `total`
-    divided by `count`.
-*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
-    appropriately.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `ignore_mask` is not None and its shape doesn't match `values
-    or if either `metrics_collections` or `updates_collections` are supplied
-    but are not a list or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_precision.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_precision.md
deleted file mode 100644
index 77ddaead32..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_precision.md
+++ /dev/null
@@ -1,50 +0,0 @@
-### `tf.contrib.metrics.streaming_precision(predictions, labels, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_precision}
-
-Computes the precision of the predictions with respect to the labels.
-
-The `streaming_precision` function creates two local variables,
-`true_positives` and `false_positives`, that are used to compute the
-precision. This value is ultimately returned as `precision`, an idempotent
-operation that simply divides `true_positives` by the sum of `true_positives`
-and `false_positives`. To facilitate the calculation of the precision over a
-stream of data, the function creates an `update_op` operation whose behavior
-is dependent on the value of `ignore_mask`. If `ignore_mask` is None, then
-`update_op` increments `true_positives` with the number of elements of
-`predictions` and `labels` that are both `True` and increments
-`false_positives` with the number of elements of `predictions` that are `True`
-whose corresponding `labels` element is `False`. If `ignore_mask` is not
-`None`, then the increments for `true_positives` and `false_positives` are
-only computed using elements of `predictions` and `labels` whose corresponding
-values in `ignore_mask` are `False`. In addition to performing the updates,
-`update_op` also returns the value of `precision`.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: The predicted values, a binary `Tensor` of arbitrary shape.
-*  <b>`labels`</b>: The ground truth values, a binary `Tensor` whose dimensions must
-    match `predictions`.
-*  <b>`ignore_mask`</b>: An optional, binary tensor whose size matches `predictions`.
-*  <b>`metrics_collections`</b>: An optional list of collections that `precision` should
-    be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
-    be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`precision`</b>: Scalar float `Tensor` with the value of `true_positives`
-    divided by the sum of `true_positives` and `false_positives`.
-*  <b>`update_op`</b>: `Operation` that increments `true_positives` and
-    `false_positives` variables appropriately and whose value matches
-    `precision`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the dimensions of `predictions` and `labels` don't match or
-    if `ignore_mask` is not `None` and its shape doesn't match `predictions`
-    or if either `metrics_collections` or `updates_collections` are not a list
-    or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_sparse_precision_at_k.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_sparse_precision_at_k.md
new file mode 100644
index 0000000000..ad24dd742a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_sparse_precision_at_k.md
@@ -0,0 +1,60 @@
+### `tf.contrib.metrics.streaming_sparse_precision_at_k(predictions, labels, k, class_id=None, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_sparse_precision_at_k}
+
+Computes precision@k of the predictions with respect to sparse labels.
+
+If `class_id` is specified, we calculate precision by considering only the
+    entries in the batch for which `class_id` is in the top-k highest
+    `predictions`, and computing the fraction of them for which `class_id` is
+    indeed a correct label.
+If `class_id` is not specified, we'll calculate precision as how often on
+    average a class among the top-k classes with the highest predicted values
+    of a batch entry is correct and can be found in the label for that entry.
+
+`streaming_sparse_precision_at_k` creates two local variables,
+`true_positive_at_<k>` and `false_positive_at_<k>`, that are used to compute
+the precision@k frequency. This frequency is ultimately returned as
+`recall_at_<k>`: an idempotent operation that simply divides
+`true_positive_at_<k>` by total (`true_positive_at_<k>` + `recall_at_<k>`). To
+facilitate the estimation of precision@k over a stream of data, the function
+utilizes three steps.
+* A `top_k` operation computes a tensor whose elements indicate the top `k`
+  predictions of the `predictions` `Tensor`.
+* Set operations are applied to `top_k` and `labels` to calculate true
+  positives and false positives.
+* An `update_op` operation increments `true_positive_at_<k>` and
+  `false_positive_at_<k>`. It also returns the recall value.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: Float `Tensor` with shape [D1, ... DN, num_classes] where
+    N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes].
+    The final dimension contains the logit values for each class. [D1, ... DN]
+    must match `labels`.
+*  <b>`labels`</b>: `int64` `Tensor` or `SparseTensor` with shape
+    [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
+    target classes for the associated prediction. Commonly, N=1 and `labels`
+    has shape [batch_size, num_labels]. [D1, ... DN] must match
+    `predictions_idx`. Values should be in range [0, num_classes], where
+    num_classes is the last dimension of `predictions`.
+*  <b>`k`</b>: Integer, k for @k metric.
+*  <b>`class_id`</b>: Integer class ID for which we want binary metrics. This should be
+    in range [0, num_classes], where num_classes is the last dimension of
+    `predictions`.
+*  <b>`ignore_mask`</b>: An optional, binary tensor whose shape is broadcastable to the
+    the first [D1, ... DN] dimensions of `predictions_idx` and `labels`.
+*  <b>`metrics_collections`</b>: An optional list of collections that values should
+    be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that updates should
+    be added to.
+*  <b>`name`</b>: Name of new update operation, and namespace for other dependant ops.
+
+##### Returns:
+
+
+*  <b>`precision`</b>: Scalar `float64` `Tensor` with the value of `true_positives`
+    divided by the sum of `true_positives` and `false_positives`.
+*  <b>`update_op`</b>: `Operation` that increments `true_positives` and
+    `false_positives` variables appropriately, and whose value matches
+    `precision`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_sparse_recall_at_k.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_sparse_recall_at_k.md
new file mode 100644
index 0000000000..d09b288089
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.metrics.streaming_sparse_recall_at_k.md
@@ -0,0 +1,59 @@
+### `tf.contrib.metrics.streaming_sparse_recall_at_k(predictions, labels, k, class_id=None, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_sparse_recall_at_k}
+
+Computes recall@k of the predictions with respect to sparse labels.
+
+If `class_id` is specified, we calculate recall by considering only the
+    entries in the batch for which `class_id` is in the label, and computing
+    the fraction of them for which `class_id` is in the top-k `predictions`.
+If `class_id` is not specified, we'll calculate recall as how often on
+    average a class among the labels of a batch entry is in the top-k
+    `predictions`.
+
+`streaming_sparse_recall_at_k` creates two local variables,
+`true_positive_at_<k>` and `false_negative_at_<k>`, that are used to compute
+the recall_at_k frequency. This frequency is ultimately returned as
+`recall_at_<k>`: an idempotent operation that simply divides
+`true_positive_at_<k>` by total (`true_positive_at_<k>` + `recall_at_<k>`). To
+facilitate the estimation of recall@k over a stream of data, the function
+utilizes three steps.
+* A `top_k` operation computes a tensor whose elements indicate the top `k`
+  predictions of the `predictions` `Tensor`.
+* Set operations are applied to `top_k` and `labels` to calculate true
+  positives and false negatives.
+* An `update_op` operation increments `true_positive_at_<k>` and
+  `false_negative_at_<k>`. It also returns the recall value.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: Float `Tensor` with shape [D1, ... DN, num_classes] where
+    N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes].
+    The final dimension contains the logit values for each class. [D1, ... DN]
+    must match `labels`.
+*  <b>`labels`</b>: `int64` `Tensor` or `SparseTensor` with shape
+    [D1, ... DN, num_labels], where N >= 1 and num_labels is the number of
+    target classes for the associated prediction. Commonly, N=1 and `labels`
+    has shape [batch_size, num_labels]. [D1, ... DN] must match `labels`.
+    Values should be in range [0, num_classes], where num_classes is the last
+    dimension of `predictions`.
+*  <b>`k`</b>: Integer, k for @k metric.
+*  <b>`class_id`</b>: Integer class ID for which we want binary metrics. This should be
+    in range [0, num_classes], where num_classes is the last dimension of
+    `predictions`.
+*  <b>`ignore_mask`</b>: An optional, binary tensor whose shape is broadcastable to the
+    the first [D1, ... DN] dimensions of `predictions_idx` and `labels`.
+*  <b>`metrics_collections`</b>: An optional list of collections that values should
+    be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that updates should
+    be added to.
+*  <b>`name`</b>: Name of new update operation, and namespace for other dependant ops.
+
+##### Returns:
+
+
+*  <b>`recall`</b>: Scalar `float64` `Tensor` with the value of `true_positives` divided
+    by the sum of `true_positives` and `false_negatives`.
+*  <b>`update_op`</b>: `Operation` that increments `true_positives` and
+    `false_negatives` variables appropriately, and whose value matches
+    `recall`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.util.make_tensor_proto.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.util.make_tensor_proto.md
deleted file mode 100644
index f84a59be49..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.util.make_tensor_proto.md
+++ /dev/null
@@ -1,44 +0,0 @@
-### `tf.contrib.util.make_tensor_proto(values, dtype=None, shape=None)` {#make_tensor_proto}
-
-Create a TensorProto.
-
-##### Args:
-
-
-*  <b>`values`</b>: Values to put in the TensorProto.
-*  <b>`dtype`</b>: Optional tensor_pb2 DataType value.
-*  <b>`shape`</b>: List of integers representing the dimensions of tensor.
-
-##### Returns:
-
-  A TensorProto. Depending on the type, it may contain data in the
-  "tensor_content" attribute, which is not directly useful to Python programs.
-  To access the values you should convert the proto back to a numpy ndarray
-  with tensor_util.MakeNdarray(proto).
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if unsupported types are provided.
-*  <b>`ValueError`</b>: if arguments have inappropriate values.
-
-make_tensor_proto accepts "values" of a python scalar, a python list, a
-numpy ndarray, or a numpy scalar.
-
-If "values" is a python scalar or a python list, make_tensor_proto
-first convert it to numpy ndarray. If dtype is None, the
-conversion tries its best to infer the right numpy data
-type. Otherwise, the resulting numpy array has a compatible data
-type with the given dtype.
-
-In either case above, the numpy ndarray (either the caller provided
-or the auto converted) must have the compatible type with dtype.
-
-make_tensor_proto then converts the numpy array to a tensor proto.
-
-If "shape" is None, the resulting tensor proto represents the numpy
-array precisely.
-
-Otherwise, "shape" specifies the tensor's shape and the numpy array
-can not have more elements than what "shape" specifies.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.control_dependencies.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.control_dependencies.md
new file mode 100644
index 0000000000..070f8788e5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.control_dependencies.md
@@ -0,0 +1,20 @@
+### `tf.control_dependencies(control_inputs)` {#control_dependencies}
+
+Wrapper for `Graph.control_dependencies()` using the default graph.
+
+See [`Graph.control_dependencies()`](../../api_docs/python/framework.md#Graph.control_dependencies)
+for more details.
+
+##### Args:
+
+
+*  <b>`control_inputs`</b>: A list of `Operation` or `Tensor` objects which
+    must be executed or computed before running the operations
+    defined in the context.  Can also be `None` to clear the control
+    dependencies.
+
+##### Returns:
+
+ A context manager that specifies control dependencies for all
+ operations constructed within the context.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.convert_to_tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.convert_to_tensor.md
deleted file mode 100644
index 29902ed467..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.convert_to_tensor.md
+++ /dev/null
@@ -1,47 +0,0 @@
-### `tf.convert_to_tensor(value, dtype=None, name=None, as_ref=False)` {#convert_to_tensor}
-
-Converts the given `value` to a `Tensor`.
-
-This function converts Python objects of various types to `Tensor`
-objects. It accepts `Tensor` objects, numpy arrays, Python lists,
-and Python scalars. For example:
-
-```python
-import numpy as np
-
-def my_func(arg):
-  arg = tf.convert_to_tensor(arg, dtype=tf.float32)
-  return tf.matmul(arg, arg) + arg
-
-# The following calls are equivalent.
-value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))
-value_2 = my_func([[1.0, 2.0], [3.0, 4.0]])
-value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32))
-```
-
-This function can be useful when composing a new operation in Python
-(such as `my_func` in the example above). All standard Python op
-constructors apply this function to each of their Tensor-valued
-inputs, which allows those ops to accept numpy arrays, Python lists,
-and scalars in addition to `Tensor` objects.
-
-##### Args:
-
-
-*  <b>`value`</b>: An object whose type has a registered `Tensor` conversion function.
-*  <b>`dtype`</b>: Optional element type for the returned tensor. If missing, the
-    type is inferred from the type of `value`.
-*  <b>`name`</b>: Optional name to use if a new `Tensor` is created.
-*  <b>`as_ref`</b>: True if we want the result as a ref tensor. Only used if a new
-    `Tensor` is created.
-
-##### Returns:
-
-  A `Tensor` based on `value`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If no conversion function is registered for `value`.
-*  <b>`RuntimeError`</b>: If a registered conversion function returns an invalid value.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.count_up_to.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.count_up_to.md
deleted file mode 100644
index da86e52f07..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.count_up_to.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.count_up_to(ref, limit, name=None)` {#count_up_to}
-
-Increments 'ref' until it reaches 'limit'.
-
-This operation outputs "ref" after the update is done.  This makes it
-easier to chain operations that need to use the updated value.
-
-##### Args:
-
-
-*  <b>`ref`</b>: A mutable `Tensor`. Must be one of the following types: `int32`, `int64`.
-    Should be from a scalar `Variable` node.
-*  <b>`limit`</b>: An `int`.
-    If incrementing ref would bring it above limit, instead generates an
-    'OutOfRange' error.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `ref`.
-  A copy of the input before increment. If nothing else modifies the
-  input, the values produced will all be distinct.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.device.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.device.md
deleted file mode 100644
index 2a5e33203d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.device.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.device(device_name_or_function)` {#device}
-
-Wrapper for `Graph.device()` using the default graph.
-
-See
-[`Graph.device()`](../../api_docs/python/framework.md#Graph.device)
-for more details.
-
-##### Args:
-
-
-*  <b>`device_name_or_function`</b>: The device name or function to use in
-    the context.
-
-##### Returns:
-
-  A context manager that specifies the default device to use for newly
-  created ops.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.diag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.diag.md
new file mode 100644
index 0000000000..94eb6a6717
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.diag.md
@@ -0,0 +1,33 @@
+### `tf.diag(diagonal, name=None)` {#diag}
+
+Returns a diagonal tensor with a given diagonal values.
+
+Given a `diagonal`, this operation returns a tensor with the `diagonal` and
+everything else padded with zeros. The diagonal is computed as follows:
+
+Assume `diagonal` has dimensions [D1,..., Dk], then the output is a tensor of
+rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where:
+
+`output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik]` and 0 everywhere else.
+
+For example:
+
+```prettyprint
+# 'diagonal' is [1, 2, 3, 4]
+tf.diag(diagonal) ==> [[1, 0, 0, 0]
+                       [0, 2, 0, 0]
+                       [0, 0, 3, 0]
+                       [0, 0, 0, 4]]
+```
+
+##### Args:
+
+
+*  <b>`diagonal`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `complex64`.
+    Rank k tensor where k is at most 3.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `diagonal`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.diag_part.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.diag_part.md
deleted file mode 100644
index 249eb80e50..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.diag_part.md
+++ /dev/null
@@ -1,34 +0,0 @@
-### `tf.diag_part(input, name=None)` {#diag_part}
-
-Returns the diagonal part of the tensor.
-
-This operation returns a tensor with the `diagonal` part
-of the `input`. The `diagonal` part is computed as follows:
-
-Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a
-tensor of rank `k` with dimensions `[D1,..., Dk]` where:
-
-`diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.
-
-For example:
-
-```prettyprint
-# 'input' is [[1, 0, 0, 0]
-              [0, 2, 0, 0]
-              [0, 0, 3, 0]
-              [0, 0, 0, 4]]
-
-tf.diag_part(input) ==> [1, 2, 3, 4]
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `complex64`.
-    Rank k tensor where k is 2, 4, or 6.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`. The extracted diagonal.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.AlreadyExistsError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.AlreadyExistsError.md
deleted file mode 100644
index 85425df298..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.AlreadyExistsError.md
+++ /dev/null
@@ -1,14 +0,0 @@
-Raised when an entity that we attempted to create already exists.
-
-For example, running an operation that saves a file
-(e.g. [`tf.train.Saver.save()`](../../api_docs/python/train.md#Saver.save))
-could potentially raise this exception if an explicit filename for an
-existing file was passed.
-
-- - -
-
-#### `tf.errors.AlreadyExistsError.__init__(node_def, op, message)` {#AlreadyExistsError.__init__}
-
-Creates an `AlreadyExistsError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.InternalError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.InternalError.md
deleted file mode 100644
index dd229d2a3d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.InternalError.md
+++ /dev/null
@@ -1,12 +0,0 @@
-Raised when the system experiences an internal error.
-
-This exception is raised when some invariant expected by the runtime
-has been broken. Catching this exception is not recommended.
-
-- - -
-
-#### `tf.errors.InternalError.__init__(node_def, op, message)` {#InternalError.__init__}
-
-Creates an `InternalError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.OutOfRangeError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.OutOfRangeError.md
new file mode 100644
index 0000000000..ef996b0a88
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.OutOfRangeError.md
@@ -0,0 +1,15 @@
+Raised when an operation iterates past the valid input range.
+
+This exception is raised in "end-of-file" conditions, such as when a
+[`queue.dequeue()`](../../api_docs/python/io_ops.md#QueueBase.dequeue)
+operation is blocked on an empty queue, and a
+[`queue.close()`](../../api_docs/python/io_ops.md#QueueBase.close)
+operation executes.
+
+- - -
+
+#### `tf.errors.OutOfRangeError.__init__(node_def, op, message)` {#OutOfRangeError.__init__}
+
+Creates an `OutOfRangeError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.UnavailableError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.UnavailableError.md
deleted file mode 100644
index e212ae94ec..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.errors.UnavailableError.md
+++ /dev/null
@@ -1,11 +0,0 @@
-Raised when the runtime is currently unavailable.
-
-This exception is not currently used.
-
-- - -
-
-#### `tf.errors.UnavailableError.__init__(node_def, op, message)` {#UnavailableError.__init__}
-
-Creates an `UnavailableError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.fft3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.fft3d.md
deleted file mode 100644
index 7214e3ae20..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.fft3d.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.fft3d(input, name=None)` {#fft3d}
-
-Compute the 3-dimensional discrete Fourier Transform.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 3-D tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`. The 3D Fourier Transform of `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.floor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.floor.md
new file mode 100644
index 0000000000..4aadcff6ef
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.floor.md
@@ -0,0 +1,14 @@
+### `tf.floor(x, name=None)` {#floor}
+
+Returns element-wise largest integer not greater than x.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.floordiv.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.floordiv.md
new file mode 100644
index 0000000000..8f824e867e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.floordiv.md
@@ -0,0 +1,32 @@
+### `tf.floordiv(x, y, name=None)` {#floordiv}
+
+Divides `x / y` elementwise, rounding down for floating point.
+
+The same as `tf.div(x,y)` for integers, but uses `tf.floor(tf.div(x,y))` for
+floating point arguments so that the result is always an integer (though
+possibly an integer represented as floating point).  This op is generated by
+`x // y` floor division in Python 3 and in Python 2.7 with
+`from __future__ import division`.
+
+Note that for efficiency, `floordiv` uses C semantics for negative numbers
+(unlike Python and Numpy).
+
+`x` and `y` must have the same type, and the result will have the same type
+as well.
+
+##### Args:
+
+
+*  <b>`x`</b>: `Tensor` numerator of real numeric type.
+*  <b>`y`</b>: `Tensor` denominator of real numeric type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  `x / y` rounded down (except possibly towards zero for negative integers).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If the inputs are complex.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.foldl.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.foldl.md
deleted file mode 100644
index dac4268165..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.foldl.md
+++ /dev/null
@@ -1,44 +0,0 @@
-### `tf.foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#foldl}
-
-foldl on the list of tensors unpacked from `elems` on dimension 0.
-
-This foldl operator repeatedly applies the callable `fn` to a sequence
-of elements from first to last. The elements are made of the tensors
-unpacked from `elems` on dimension 0. The callable fn takes two tensors as
-arguments. The first argument is the accumulated value computed from the
-preceding invocation of fn. If `initializer` is None, `elems` must contain
-at least one element, and its first element is used as the initializer.
-
-Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
-of the result tensor is fn(initializer, values[0]).shape`.
-
-##### Args:
-
-
-*  <b>`fn`</b>: The callable to be performed.
-*  <b>`elems`</b>: A tensor to be unpacked on dimension 0.
-*  <b>`initializer`</b>: (optional) The initial value for the accumulator.
-*  <b>`parallel_iterations`</b>: (optional) The number of iterations allowed to run
-                       in parallel.
-*  <b>`back_prop`</b>: (optional) True enables back propagation.
-*  <b>`swap_memory`</b>: (optional) True enables GPU-CPU memory swapping.
-*  <b>`name`</b>: (optional) Name prefix for the returned tensors.
-
-##### Returns:
-
-  A tensor resulting from applying `fn` consecutively to the list of tensors
-  unpacked from `elems`, from first to last.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `fn` is not callable.
-
-##### Example:
-
-  ```python
-  elems = [1, 2, 3, 4, 5, 6]
-  sum = foldl(lambda a, x: a + x, elems)
-  # sum == 21
-  ```
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.gather.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.gather.md
new file mode 100644
index 0000000000..f3ae59bbb6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.gather.md
@@ -0,0 +1,35 @@
+### `tf.gather(params, indices, validate_indices=None, name=None)` {#gather}
+
+Gather slices from `params` according to `indices`.
+
+`indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
+Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
+
+    # Scalar indices
+    output[:, ..., :] = params[indices, :, ... :]
+
+    # Vector indices
+    output[i, :, ..., :] = params[indices[i], :, ... :]
+
+    # Higher rank indices
+    output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
+
+If `indices` is a permutation and `len(indices) == params.shape[0]` then
+this operation will permute `params` accordingly.
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/Gather.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`params`</b>: A `Tensor`.
+*  <b>`indices`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+*  <b>`validate_indices`</b>: An optional `bool`. Defaults to `True`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `params`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.get_default_session.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.get_default_session.md
deleted file mode 100644
index c564366e8b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.get_default_session.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.get_default_session()` {#get_default_session}
-
-Returns the default session for the current thread.
-
-The returned `Session` will be the innermost session on which a
-`Session` or `Session.as_default()` context has been entered.
-
-NOTE: The default session is a property of the current thread. If you
-create a new thread, and wish to use the default session in that
-thread, you must explicitly add a `with sess.as_default():` in that
-thread's function.
-
-##### Returns:
-
-  The default `Session` being used in the current thread.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.get_session_handle.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.get_session_handle.md
deleted file mode 100644
index 3fdd2b0ae9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.get_session_handle.md
+++ /dev/null
@@ -1,38 +0,0 @@
-### `tf.get_session_handle(data, name=None)` {#get_session_handle}
-
-Return the handle of `data`.
-
-This is EXPERIMENTAL and subject to change.
-
-Keep `data` "in-place" in the runtime and create a handle that can be
-used to retrieve `data` in a subsequent run().
-
-Combined with `get_session_tensor`, we can keep a tensor produced in
-one run call in place, and use it as the input in a future run call.
-Below is a simple example:
-
-```python
-c = tf.mul(a, b)
-h = tf.get_session_handle(c)
-h = sess.run(h)
-
-p, a = tf.get_session_tensor(tf.float32)
-b = tf.mul(a, 10)
-c = sess.run(b, feed_dict={p: h.handle})
-```
-
-##### Args:
-
-
-*  <b>`data`</b>: A tensor to be stored in the session.
-*  <b>`name`</b>: Optional name prefix for the return tensor.
-
-##### Returns:
-
-  A scalar string tensor representing a unique handle for `data`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `data` is not a Tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.get_variable_scope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.get_variable_scope.md
deleted file mode 100644
index 4a0d3bc775..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.get_variable_scope.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.get_variable_scope()` {#get_variable_scope}
-
-Returns the current variable scope.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.greater_equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.greater_equal.md
deleted file mode 100644
index 9d68429c36..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.greater_equal.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.greater_equal(x, y, name=None)` {#greater_equal}
-
-Returns the truth value of (x >= y) element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.histogram_fixed_width.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.histogram_fixed_width.md
new file mode 100644
index 0000000000..4a2997103b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.histogram_fixed_width.md
@@ -0,0 +1,38 @@
+### `tf.histogram_fixed_width(values, value_range, nbins=100, dtype=tf.int32, name=None)` {#histogram_fixed_width}
+
+Return histogram of values.
+
+Given the tensor `values`, this operation returns a rank 1 histogram counting
+the number of entries in `values` that fell into every bin.  The bins are
+equal width and determined by the arguments `value_range` and `nbins`.
+
+##### Args:
+
+
+*  <b>`values`</b>: Numeric `Tensor`.
+*  <b>`value_range`</b>: Shape [2] `Tensor`.  new_values <= value_range[0] will be
+    mapped to hist[0], values >= value_range[1] will be mapped to hist[-1].
+    Must be same dtype as new_values.
+*  <b>`nbins`</b>: Scalar `int32 Tensor`.  Number of histogram bins.
+*  <b>`dtype`</b>: dtype for returned histogram.
+*  <b>`name`</b>: A name for this operation (defaults to 'histogram_fixed_width').
+
+##### Returns:
+
+  A 1-D `Tensor` holding histogram of values.
+
+
+*  <b>`Examples`</b>: 
+
+```python
+# Bins will be:  (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
+nbins = 5
+value_range = [0.0, 5.0]
+new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
+
+with tf.default_session() as sess:
+  hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
+  variables.initialize_all_variables().run()
+  sess.run(hist) => [2, 1, 1, 0, 2]
+```
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ifft.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ifft.md
new file mode 100644
index 0000000000..26582404f6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ifft.md
@@ -0,0 +1,15 @@
+### `tf.ifft(input, name=None)` {#ifft}
+
+Compute the inverse 1-dimensional discrete Fourier Transform.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 vector.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`.
+  The inverse 1D Fourier Transform of `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.imag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.imag.md
deleted file mode 100644
index 1dfcadbb95..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.imag.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.imag(input, name=None)` {#imag}
-
-Returns the imaginary part of a complex number.
-
-Given a tensor `input` of complex numbers, this operation returns a tensor of
-type `float` or `double` that is the imaginary part of each element in
-`input`. All elements in `input` must be complex numbers of the form \(a +
-bj\), where *a* is the real part and *b* is the imaginary part returned by
-this operation.
-
-For example:
-
-```
-# tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
-tf.imag(input) ==> [4.75, 5.75]
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `float` or `double`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.central_crop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.central_crop.md
new file mode 100644
index 0000000000..4e6b6115f8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.central_crop.md
@@ -0,0 +1,30 @@
+### `tf.image.central_crop(image, central_fraction)` {#central_crop}
+
+Crop the central region of the image.
+
+Remove the outer parts of an image but retain the central region of the image
+along each dimension. If we specify central_fraction = 0.5, this function
+returns the region marked with "X" in the below diagram.
+
+     --------
+    |        |
+    |  XXXX  |
+    |  XXXX  |
+    |        |   where "X" is the central 50% of the image.
+     --------
+
+##### Args:
+
+
+*  <b>`image`</b>: 3-D float Tensor of shape [height, width, depth]
+*  <b>`central_fraction`</b>: float (0, 1], fraction of size to crop
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if central_crop_fraction is not within (0, 1].
+
+##### Returns:
+
+  3-D float Tensor
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.crop_to_bounding_box.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.crop_to_bounding_box.md
deleted file mode 100644
index 4724ff5eb9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.crop_to_bounding_box.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.image.crop_to_bounding_box(image, offset_height, offset_width, target_height, target_width)` {#crop_to_bounding_box}
-
-Crops an image to a specified bounding box.
-
-This op cuts a rectangular part out of `image`. The top-left corner of the
-returned image is at `offset_height, offset_width` in `image`, and its
-lower-right corner is at
-`offset_height + target_height, offset_width + target_width`.
-
-##### Args:
-
-
-*  <b>`image`</b>: 3-D tensor with shape `[height, width, channels]`
-*  <b>`offset_height`</b>: Vertical coordinate of the top-left corner of the result in
-                 the input.
-*  <b>`offset_width`</b>: Horizontal coordinate of the top-left corner of the result in
-                the input.
-*  <b>`target_height`</b>: Height of the result.
-*  <b>`target_width`</b>: Width of the result.
-
-##### Returns:
-
-  3-D tensor of image with shape `[target_height, target_width, channels]`
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the shape of `image` is incompatible with the `offset_*` or
-  `target_*` arguments
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.decode_png.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.decode_png.md
new file mode 100644
index 0000000000..4332af7704
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.decode_png.md
@@ -0,0 +1,30 @@
+### `tf.image.decode_png(contents, channels=None, dtype=None, name=None)` {#decode_png}
+
+Decode a PNG-encoded image to a uint8 or uint16 tensor.
+
+The attr `channels` indicates the desired number of color channels for the
+decoded image.
+
+Accepted values are:
+
+*   0: Use the number of channels in the PNG-encoded image.
+*   1: output a grayscale image.
+*   3: output an RGB image.
+*   4: output an RGBA image.
+
+If needed, the PNG-encoded image is transformed to match the requested number
+of color channels.
+
+##### Args:
+
+
+*  <b>`contents`</b>: A `Tensor` of type `string`. 0-D.  The PNG-encoded image.
+*  <b>`channels`</b>: An optional `int`. Defaults to `0`.
+    Number of color channels for the decoded image.
+*  <b>`dtype`</b>: An optional `tf.DType` from: `tf.uint8, tf.uint16`. Defaults to `tf.uint8`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `dtype`. 3-D with shape `[height, width, channels]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.draw_bounding_boxes.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.draw_bounding_boxes.md
new file mode 100644
index 0000000000..0e1c6115c7
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.draw_bounding_boxes.md
@@ -0,0 +1,32 @@
+### `tf.image.draw_bounding_boxes(images, boxes, name=None)` {#draw_bounding_boxes}
+
+Draw bounding boxes on a batch of images.
+
+Outputs a copy of `images` but draws on top of the pixels zero or more bounding
+boxes specified by the locations in `boxes`. The coordinates of the each
+bounding box in `boxes are encoded as `[y_min, x_min, y_max, x_max]`. The
+bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
+height of the underlying image.
+
+For example, if an image is 100 x 200 pixels and the bounding box is
+`[0.1, 0.5, 0.2, 0.9]`, the bottom-left and upper-right coordinates of the
+bounding box will be `(10, 40)` to `(50, 180)`.
+
+Parts of the bounding box may fall outside the image.
+
+##### Args:
+
+
+*  <b>`images`</b>: A `Tensor`. Must be one of the following types: `float32`, `half`.
+    4-D with shape `[batch, height, width, depth]`. A batch of images.
+*  <b>`boxes`</b>: A `Tensor` of type `float32`.
+    3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding
+    boxes.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `images`.
+  4-D with the same shape as `images`. The batch of input images with
+  bounding boxes drawn on the images.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.per_image_whitening.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.per_image_whitening.md
new file mode 100644
index 0000000000..8f72af6a31
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.per_image_whitening.md
@@ -0,0 +1,29 @@
+### `tf.image.per_image_whitening(image)` {#per_image_whitening}
+
+Linearly scales `image` to have zero mean and unit norm.
+
+This op computes `(x - mean) / adjusted_stddev`, where `mean` is the average
+of all values in image, and
+`adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements()))`.
+
+`stddev` is the standard deviation of all values in `image`. It is capped
+away from zero to protect against division by 0 when handling uniform images.
+
+Note that this implementation is limited:
+*  It only whitens based on the statistics of an individual image.
+*  It does not take into account the covariance structure.
+
+##### Args:
+
+
+*  <b>`image`</b>: 3-D tensor of shape `[height, width, channels]`.
+
+##### Returns:
+
+  The whitened image with same shape as `image`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the shape of 'image' is incompatible with this function.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.random_flip_left_right.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.random_flip_left_right.md
new file mode 100644
index 0000000000..d063895136
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.random_flip_left_right.md
@@ -0,0 +1,24 @@
+### `tf.image.random_flip_left_right(image, seed=None)` {#random_flip_left_right}
+
+Randomly flip an image horizontally (left to right).
+
+With a 1 in 2 chance, outputs the contents of `image` flipped along the
+second dimension, which is `width`.  Otherwise output the image as-is.
+
+##### Args:
+
+
+*  <b>`image`</b>: A 3-D tensor of shape `[height, width, channels].`
+*  <b>`seed`</b>: A Python integer. Used to create a random seed. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+
+##### Returns:
+
+  A 3-D tensor of the same type and shape as `image`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the shape of `image` not supported.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.resize_image_with_crop_or_pad.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.resize_image_with_crop_or_pad.md
deleted file mode 100644
index c93111bd99..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.resize_image_with_crop_or_pad.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.image.resize_image_with_crop_or_pad(image, target_height, target_width)` {#resize_image_with_crop_or_pad}
-
-Crops and/or pads an image to a target width and height.
-
-Resizes an image to a target width and height by either centrally
-cropping the image or padding it evenly with zeros.
-
-If `width` or `height` is greater than the specified `target_width` or
-`target_height` respectively, this op centrally crops along that dimension.
-If `width` or `height` is smaller than the specified `target_width` or
-`target_height` respectively, this op centrally pads with 0 along that
-dimension.
-
-##### Args:
-
-
-*  <b>`image`</b>: 3-D tensor of shape [height, width, channels]
-*  <b>`target_height`</b>: Target height.
-*  <b>`target_width`</b>: Target width.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `target_height` or `target_width` are zero or negative.
-
-##### Returns:
-
-  Cropped and/or padded image of shape
-  `[target_height, target_width, channels]`
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.transpose_image.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.transpose_image.md
new file mode 100644
index 0000000000..1cc527d345
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.image.transpose_image.md
@@ -0,0 +1,20 @@
+### `tf.image.transpose_image(image)` {#transpose_image}
+
+Transpose an image by swapping the first and second dimension.
+
+See also `transpose()`.
+
+##### Args:
+
+
+*  <b>`image`</b>: 3-D tensor of shape `[height, width, channels]`
+
+##### Returns:
+
+  A 3-D tensor of shape `[width, height, channels]`
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the shape of `image` not supported.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.initialize_all_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.initialize_all_variables.md
deleted file mode 100644
index 9a0e5d8261..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.initialize_all_variables.md
+++ /dev/null
@@ -1,10 +0,0 @@
-### `tf.initialize_all_variables()` {#initialize_all_variables}
-
-Returns an Op that initializes all variables.
-
-This is just a shortcut for `initialize_variables(all_variables())`
-
-##### Returns:
-
-  An Op that initializes all variables in the graph.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.invert_permutation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.invert_permutation.md
new file mode 100644
index 0000000000..b12cc7e94c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.invert_permutation.md
@@ -0,0 +1,30 @@
+### `tf.invert_permutation(x, name=None)` {#invert_permutation}
+
+Computes the inverse permutation of a tensor.
+
+This operation computes the inverse of an index permutation. It takes a 1-D
+integer tensor `x`, which represents the indices of a zero-based array, and
+swaps each value with its index position. In other words, for an output tensor
+`y` and an input tensor `x`, this operation computes the following:
+
+`y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`
+
+The values must include 0. There can be no duplicate values or negative values.
+
+For example:
+
+```prettyprint
+# tensor `x` is [3, 4, 0, 2, 1]
+invert_permutation(x) ==> [2, 4, 3, 0, 1]
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` of type `int32`. 1-D.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `int32`. 1-D.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.is_nan.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.is_nan.md
deleted file mode 100644
index 1bf3a6825c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.is_nan.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.is_nan(x, name=None)` {#is_nan}
-
-Returns which elements of x are NaN.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.is_numeric_tensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.is_numeric_tensor.md
new file mode 100644
index 0000000000..c2e61b856d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.is_numeric_tensor.md
@@ -0,0 +1,4 @@
+### `tf.is_numeric_tensor(tensor)` {#is_numeric_tensor}
+
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.load_op_library.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.load_op_library.md
new file mode 100644
index 0000000000..0f38dfe4d5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.load_op_library.md
@@ -0,0 +1,24 @@
+### `tf.load_op_library(library_filename)` {#load_op_library}
+
+Loads a TensorFlow plugin, containing custom ops and kernels.
+
+Pass "library_filename" to a platform-specific mechanism for dynamically
+loading a library. The rules for determining the exact location of the
+library are platform-specific and are not documented here.
+
+##### Args:
+
+
+*  <b>`library_filename`</b>: Path to the plugin.
+    Relative or absolute filesystem path to a dynamic library file.
+
+##### Returns:
+
+  A python module containing the Python wrappers for Ops defined in
+  the plugin.
+
+##### Raises:
+
+
+*  <b>`RuntimeError`</b>: when unable to load the library or get the python wrappers.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.logical_and.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.logical_and.md
deleted file mode 100644
index dd5b563c8b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.logical_and.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.logical_and(x, y, name=None)` {#logical_and}
-
-Returns the truth value of x AND y element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` of type `bool`.
-*  <b>`y`</b>: A `Tensor` of type `bool`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.map_fn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.map_fn.md
new file mode 100644
index 0000000000..1892d7b03c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.map_fn.md
@@ -0,0 +1,42 @@
+### `tf.map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#map_fn}
+
+map on the list of tensors unpacked from `elems` on dimension 0.
+
+This map operator repeatedly applies the callable `fn` to a sequence of
+elements from first to last. The elements are made of the tensors unpacked
+from `elems`. `dtype` is the data type of the return value of `fn`. Users
+must provide `dtype` if it is different from the data type of `elems`.
+
+Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
+of the result tensor is `[len(values)] + fn(values[0]).shape`.
+
+##### Args:
+
+
+*  <b>`fn`</b>: The callable to be performed.
+*  <b>`elems`</b>: A tensor to be unpacked to apply `fn`.
+*  <b>`dtype`</b>: (optional) The output type of `fn`.
+*  <b>`parallel_iterations`</b>: (optional) The number of iterations allowed to run
+                       in parallel.
+*  <b>`back_prop`</b>: (optional) True enables back propagation.
+*  <b>`swap_memory`</b>: (optional) True enables GPU-CPU memory swapping.
+*  <b>`name`</b>: (optional) Name prefix for the returned tensors.
+
+##### Returns:
+
+  A tensor that packs the results of applying `fn` to the list of tensors
+  unpacked from `elems`, from first to last.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `fn` is not callable.
+
+##### Example:
+
+  ```python
+  elems = [1, 2, 3, 4, 5, 6]
+  squares = map_fn(lambda x: x * x, elems)
+  # squares == [1, 4, 9, 16, 25, 36]
+  ```
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.matmul.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.matmul.md
deleted file mode 100644
index 6602562ecc..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.matmul.md
+++ /dev/null
@@ -1,46 +0,0 @@
-### `tf.matmul(a, b, transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=False, name=None)` {#matmul}
-
-Multiplies matrix `a` by matrix `b`, producing `a` * `b`.
-
-The inputs must be two-dimensional matrices, with matching inner dimensions,
-possibly after transposition.
-
-Both matrices must be of the same type. The supported types are:
-`float`, `double`, `int32`, `complex64`.
-
-Either matrix can be transposed on the fly by setting the corresponding flag
-to `True`. This is `False` by default.
-
-If one or both of the matrices contain a lot of zeros, a more efficient
-multiplication algorithm can be used by setting the corresponding
-`a_is_sparse` or `b_is_sparse` flag to `True`. These are `False` by default.
-
-For example:
-
-```python
-# 2-D tensor `a`
-a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3]) => [[1. 2. 3.]
-                                                      [4. 5. 6.]]
-# 2-D tensor `b`
-b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2]) => [[7. 8.]
-                                                         [9. 10.]
-                                                         [11. 12.]]
-c = tf.matmul(a, b) => [[58 64]
-                        [139 154]]
-```
-
-##### Args:
-
-
-*  <b>`a`</b>: `Tensor` of type `float`, `double`, `int32` or `complex64`.
-*  <b>`b`</b>: `Tensor` with same type as `a`.
-*  <b>`transpose_a`</b>: If `True`, `a` is transposed before multiplication.
-*  <b>`transpose_b`</b>: If `True`, `b` is transposed before multiplication.
-*  <b>`a_is_sparse`</b>: If `True`, `a` is treated as a sparse matrix.
-*  <b>`b_is_sparse`</b>: If `True`, `b` is treated as a sparse matrix.
-*  <b>`name`</b>: Name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of the same type as `a`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.matrix_determinant.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.matrix_determinant.md
new file mode 100644
index 0000000000..a5cd5a7fe6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.matrix_determinant.md
@@ -0,0 +1,16 @@
+### `tf.matrix_determinant(input, name=None)` {#matrix_determinant}
+
+Calculates the determinant of a square matrix.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+    A tensor of shape `[M, M]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+  A scalar, equal to the determinant of the input.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.matrix_solve_ls.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.matrix_solve_ls.md
deleted file mode 100644
index 8f5548d2cb..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.matrix_solve_ls.md
+++ /dev/null
@@ -1,47 +0,0 @@
-### `tf.matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None)` {#matrix_solve_ls}
-
-Solves a linear least-squares problem.
-
-Below we will use the following notation
-`matrix`=\\(A \in \Re^{m \times n}\\),
-`rhs`=\\(B  \in \Re^{m \times k}\\),
-`output`=\\(X  \in \Re^{n \times k}\\),
-`l2_regularizer`=\\(\lambda\\).
-
-If `fast` is `True`, then the solution is computed by solving the normal
-equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then
-\\(X = (A^T A + \lambda I)^{-1} A^T B\\), which solves the regularized
-least-squares problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k}}
-||A Z - B||_F^2 + \lambda ||Z||_F^2\\). If \\(m \lt n\\) then `output` is
-computed as \\(X = A^T (A A^T + \lambda I)^{-1} B\\),
-which (for \\(\lambda = 0\\)) is the minimum-norm solution to the
-under-determined linear system, i.e.
-\\(X = \mathrm{argmin}_{Z \in \Re^{n \times k}} ||Z||_F^2 \\),
-subject to \\(A Z = B\\).
-Notice that the fast path is only numerically stable when \\(A\\) is
-numerically full rank and has a condition number
-\\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach}}}\\)
-or \\(\lambda\\) is sufficiently large.
-
-If `fast` is `False` then the solution is computed using the rank revealing
-QR decomposition with column pivoting. This will always compute a
-least-squares solution that minimizes the residual norm
-\\(||A X - B||_F^2 \\), even when \\(A\\) is rank deficient or
-ill-conditioned. Notice: The current version does not compute a minimum norm
-solution. If `fast` is `False` then `l2_regularizer` is ignored.
-
-##### Args:
-
-
-*  <b>`matrix`</b>: 2-D `Tensor` of shape `[M, N]`.
-*  <b>`rhs`</b>: 2-D `Tensor` of shape is `[M, K]`.
-*  <b>`l2_regularizer`</b>: 0-D  `double` `Tensor`. Ignored if `fast=False`.
-*  <b>`fast`</b>: bool. Defaults to `True`.
-*  <b>`name`</b>: string, optional name of the operation.
-
-##### Returns:
-
-
-*  <b>`output`</b>: Matrix of shape `[N, K]` containing the matrix that solves
-    `matrix * output = rhs` in the least-squares sense.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.minimum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.minimum.md
new file mode 100644
index 0000000000..bff13483f4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.minimum.md
@@ -0,0 +1,15 @@
+### `tf.minimum(x, y, name=None)` {#minimum}
+
+Returns the min of x and y (i.e. x < y ? x : y) element-wise, broadcasts.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.avg_pool.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.avg_pool.md
deleted file mode 100644
index 33da8534c2..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.avg_pool.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.nn.avg_pool(value, ksize, strides, padding, data_format='NHWC', name=None)` {#avg_pool}
-
-Performs the average pooling on the input.
-
-Each entry in `output` is the mean of the corresponding size `ksize`
-window in `value`.
-
-##### Args:
-
-
-*  <b>`value`</b>: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type
-    `float32`, `float64`, `qint8`, `quint8`, or `qint32`.
-*  <b>`ksize`</b>: A list of ints that has length >= 4.
-    The size of the window for each dimension of the input tensor.
-*  <b>`strides`</b>: A list of ints that has length >= 4.
-    The stride of the sliding window for each dimension of the
-    input tensor.
-*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
-*  <b>`data_format`</b>: A string. 'NHWC' and 'NCHW' are supported.
-*  <b>`name`</b>: Optional name for the operation.
-
-##### Returns:
-
-  A `Tensor` with the same type as `value`.  The average pooled output tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.batch_normalization.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.batch_normalization.md
deleted file mode 100644
index eda1d7d053..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.batch_normalization.md
+++ /dev/null
@@ -1,46 +0,0 @@
-### `tf.nn.batch_normalization(x, mean, variance, offset, scale, variance_epsilon, name=None)` {#batch_normalization}
-
-Batch normalization.
-
-As described in http://arxiv.org/abs/1502.03167.
-Normalizes a tensor by `mean` and `variance`, and applies (optionally) a
-`scale` \\(\gamma\\) to it, as well as an `offset` \\(\beta\\):
-
-\\(\frac{\gamma(x-\mu)}{\sigma}+\beta\\)
-
-`mean`, `variance`, `offset` and `scale` are all expected to be of one of two
-shapes:
-  * In all generality, they can have the same number of dimensions as the
-    input `x`, with identical sizes as `x` for the dimensions that are not
-    normalized over (the 'depth' dimension(s)), and dimension 1 for the
-    others which are being normalized over.
-    `mean` and `variance` in this case would typically be the outputs of
-    `tf.nn.moments(..., keep_dims=True)` during training, or running averages
-    thereof during inference.
-  * In the common case where the 'depth' dimension is the last dimension in
-    the input tensor `x`, they may be one dimensional tensors of the same
-    size as the 'depth' dimension.
-    This is the case for example for the common `[batch, depth]` layout of
-    fully-connected layers, and `[batch, height, width, depth]` for
-    convolutions.
-    `mean` and `variance` in this case would typically be the outputs of
-    `tf.nn.moments(..., keep_dims=False)` during training, or running averages
-    thereof during inference.
-
-##### Args:
-
-
-*  <b>`x`</b>: Input `Tensor` of arbitrary dimensionality.
-*  <b>`mean`</b>: A mean `Tensor`.
-*  <b>`variance`</b>: A variance `Tensor`.
-*  <b>`offset`</b>: An offset `Tensor`, often denoted \\(\beta\\) in equations, or
-    None. If present, will be added to the normalized tensor.
-*  <b>`scale`</b>: A scale `Tensor`, often denoted \\(\gamma\\) in equations, or
-    `None`. If present, the scale is applied to the normalized tensor.
-*  <b>`variance_epsilon`</b>: A small float number to avoid dividing by 0.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  the normalized, scaled, offset tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.embedding_lookup.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.embedding_lookup.md
new file mode 100644
index 0000000000..588c2b393d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.embedding_lookup.md
@@ -0,0 +1,50 @@
+### `tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None, validate_indices=True)` {#embedding_lookup}
+
+Looks up `ids` in a list of embedding tensors.
+
+This function is used to perform parallel lookups on the list of
+tensors in `params`.  It is a generalization of
+[`tf.gather()`](../../api_docs/python/array_ops.md#gather), where `params` is
+interpreted as a partition of a larger embedding tensor.
+
+If `len(params) > 1`, each element `id` of `ids` is partitioned between
+the elements of `params` according to the `partition_strategy`.
+In all strategies, if the id space does not evenly divide the number of
+partitions, each of the first `(max_id + 1) % len(params)` partitions will
+be assigned one more id.
+
+If `partition_strategy` is `"mod"`, we assign each id to partition
+`p = id % len(params)`. For instance,
+13 ids are split across 5 partitions as:
+`[[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]]`
+
+If `partition_strategy` is `"div"`, we assign ids to partitions in a
+contiguous manner. In this case, 13 ids are split across 5 partitions as:
+`[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]`
+
+The results of the lookup are concatenated into a dense
+tensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`.
+
+##### Args:
+
+
+*  <b>`params`</b>: A list of tensors with the same type and which can be concatenated
+    along dimension 0. Each `Tensor` must be appropriately sized for the given
+    `partition_strategy`.
+*  <b>`ids`</b>: A `Tensor` with type `int32` or `int64` containing the ids to be looked
+    up in `params`.
+*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy, relevant
+    if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default
+    is `"mod"`.
+*  <b>`name`</b>: A name for the operation (optional).
+*  <b>`validate_indices`</b>: Whether or not to validate gather indices.
+
+##### Returns:
+
+  A `Tensor` with the same type as the tensors in `params`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `params` is empty.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.learned_unigram_candidate_sampler.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.learned_unigram_candidate_sampler.md
deleted file mode 100644
index 4f69938e59..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.learned_unigram_candidate_sampler.md
+++ /dev/null
@@ -1,53 +0,0 @@
-### `tf.nn.learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)` {#learned_unigram_candidate_sampler}
-
-Samples a set of classes from a distribution learned during training.
-
-This operation randomly samples a tensor of sampled classes
-(`sampled_candidates`) from the range of integers `[0, range_max)`.
-
-The elements of `sampled_candidates` are drawn without replacement
-(if `unique=True`) or with replacement (if `unique=False`) from
-the base distribution.
-
-The base distribution for this operation is constructed on the fly
-during training.  It is a unigram distribution over the target
-classes seen so far during training.  Every integer in `[0, range_max)`
-begins with a weight of 1, and is incremented by 1 each time it is
-seen as a target class.  The base distribution is not saved to checkpoints,
-so it is reset when the model is reloaded.
-
-In addition, this operation returns tensors `true_expected_count`
-and `sampled_expected_count` representing the number of times each
-of the target classes (`true_classes`) and the sampled
-classes (`sampled_candidates`) is expected to occur in an average
-tensor of sampled classes.  These values correspond to `Q(y|x)`
-defined in [this
-document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
-If `unique=True`, then these are post-rejection probabilities and we
-compute them approximately.
-
-##### Args:
-
-
-*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
-    num_true]`. The target classes.
-*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
-*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
-*  <b>`unique`</b>: A `bool`. Determines whether all sampled classes in a batch are
-    unique.
-*  <b>`range_max`</b>: An `int`. The number of possible classes.
-*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-
-*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
-    The sampled classes.
-*  <b>`true_expected_count`</b>: A tensor of type `float`.  Same shape as
-    `true_classes`. The expected counts under the sampling distribution
-    of each of `true_classes`.
-*  <b>`sampled_expected_count`</b>: A tensor of type `float`. Same shape as
-    `sampled_candidates`. The expected counts under the sampling distribution
-    of each of `sampled_candidates`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.log_softmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.log_softmax.md
new file mode 100644
index 0000000000..18e1f96590
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.log_softmax.md
@@ -0,0 +1,19 @@
+### `tf.nn.log_softmax(logits, name=None)` {#log_softmax}
+
+Computes log softmax activations.
+
+For each batch `i` and class `j` we have
+
+    logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
+
+##### Args:
+
+
+*  <b>`logits`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+    2-D with shape `[batch_size, num_classes]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `logits`. Same shape as `logits`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.max_pool3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.max_pool3d.md
new file mode 100644
index 0000000000..471fcb532f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.max_pool3d.md
@@ -0,0 +1,23 @@
+### `tf.nn.max_pool3d(input, ksize, strides, padding, name=None)` {#max_pool3d}
+
+Performs 3D max pooling on the input.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+    Shape `[batch, depth, rows, cols, channels]` tensor to pool over.
+*  <b>`ksize`</b>: A list of `ints` that has length `>= 5`.
+    1-D tensor of length 5. The size of the window for each dimension of
+    the input tensor. Must have `ksize[0] = ksize[1] = 1`.
+*  <b>`strides`</b>: A list of `ints` that has length `>= 5`.
+    1-D tensor of length 5. The stride of the sliding window for each
+    dimension of `input`. Must have `strides[0] = strides[4] = 1`.
+*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
+    The type of padding algorithm to use.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`. The max pooled output tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.moments.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.moments.md
new file mode 100644
index 0000000000..704bb5ba49
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.moments.md
@@ -0,0 +1,30 @@
+### `tf.nn.moments(x, axes, shift=None, name=None, keep_dims=False)` {#moments}
+
+Calculate the mean and variance of `x`.
+
+The mean and variance are calculated by aggregating the contents of `x`
+across `axes`.  If `x` is 1-D and `axes = [0]` this is just the mean
+and variance of a vector.
+
+When using these moments for batch normalization (see
+`tf.nn.batch_normalization`):
+  * for so-called "global normalization", used with convolutional filters with
+    shape `[batch, height, width, depth]`, pass `axes=[0, 1, 2]`.
+  * for simple batch normalization pass `axes=[0]` (batch only).
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`.
+*  <b>`axes`</b>: array of ints.  Axes along which to compute mean and
+    variance.
+*  <b>`shift`</b>: A `Tensor` containing the value by which to shift the data for
+    numerical stability, or `None` if no shift is to be performed. A shift
+    close to the true mean provides the most numerically stable results.
+*  <b>`keep_dims`</b>: produce moments with the same dimensionality as the input.
+*  <b>`name`</b>: Name used to scope the operations that compute the moments.
+
+##### Returns:
+
+  Two `Tensor` objects: `mean` and `variance`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.relu.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.relu.md
new file mode 100644
index 0000000000..5811a1da96
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.relu.md
@@ -0,0 +1,14 @@
+### `tf.nn.relu(features, name=None)` {#relu}
+
+Computes rectified linear: `max(features, 0)`.
+
+##### Args:
+
+
+*  <b>`features`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `features`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.softmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.softmax.md
deleted file mode 100644
index be31bb2093..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.softmax.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.nn.softmax(logits, name=None)` {#softmax}
-
-Computes softmax activations.
-
-For each batch `i` and class `j` we have
-
-    softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))
-
-##### Args:
-
-
-*  <b>`logits`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-    2-D with shape `[batch_size, num_classes]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `logits`. Same shape as `logits`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.softsign.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.softsign.md
deleted file mode 100644
index 971b2a8134..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.softsign.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.nn.softsign(features, name=None)` {#softsign}
-
-Computes softsign: `features / (abs(features) + 1)`.
-
-##### Args:
-
-
-*  <b>`features`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `features`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.sufficient_statistics.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.sufficient_statistics.md
deleted file mode 100644
index 92cb5596e6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.sufficient_statistics.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.nn.sufficient_statistics(x, axes, shift=None, keep_dims=False, name=None)` {#sufficient_statistics}
-
-Calculate the sufficient statistics for the mean and variance of `x`.
-
-These sufficient statistics are computed using the one pass algorithm on
-an input that's optionally shifted. See:
-https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`.
-*  <b>`axes`</b>: Array of ints. Axes along which to compute mean and variance.
-*  <b>`shift`</b>: A `Tensor` containing the value by which to shift the data for
-    numerical stability, or `None` if no shift is to be performed. A shift
-    close to the true mean provides the most numerically stable results.
-*  <b>`keep_dims`</b>: produce statistics with the same dimensionality as the input.
-*  <b>`name`</b>: Name used to scope the operations that compute the sufficient stats.
-
-##### Returns:
-
-  Four `Tensor` objects of the same type as `x`:
-  * the count (number of elements to average over).
-  * the (possibly shifted) sum of the elements in the array.
-  * the (possibly shifted) sum of squares of the elements in the array.
-  * the shift by which the mean must be corrected or None if `shift` is None.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.zero_fraction.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.zero_fraction.md
new file mode 100644
index 0000000000..f4d126a041
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.nn.zero_fraction.md
@@ -0,0 +1,21 @@
+### `tf.nn.zero_fraction(value, name=None)` {#zero_fraction}
+
+Returns the fraction of zeros in `value`.
+
+If `value` is empty, the result is `nan`.
+
+This is useful in summaries to measure and report sparsity.  For example,
+
+    z = tf.Relu(...)
+    summ = tf.scalar_summary('sparsity', tf.nn.zero_fraction(z))
+
+##### Args:
+
+
+*  <b>`value`</b>: A tensor of numeric type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The fraction of zeros in `value`, with type `float32`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ones.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ones.md
new file mode 100644
index 0000000000..8a4c9073d0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ones.md
@@ -0,0 +1,24 @@
+### `tf.ones(shape, dtype=tf.float32, name=None)` {#ones}
+
+Creates a tensor with all elements set to 1.
+
+This operation returns a tensor of type `dtype` with shape `shape` and all
+elements set to 1.
+
+For example:
+
+```python
+tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]
+```
+
+##### Args:
+
+
+*  <b>`shape`</b>: Either a list of integers, or a 1-D `Tensor` of type `int32`.
+*  <b>`dtype`</b>: The type of an element in the resulting `Tensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` with all elements set to 1.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.python_io.tf_record_iterator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.python_io.tf_record_iterator.md
deleted file mode 100644
index f5e90ea422..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.python_io.tf_record_iterator.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.python_io.tf_record_iterator(path)` {#tf_record_iterator}
-
-An iterator that read the records from a TFRecords file.
-
-##### Args:
-
-
-*  <b>`path`</b>: The path to the TFRecords file.
-
-##### Yields:
-
-  Strings.
-
-##### Raises:
-
-
-*  <b>`IOError`</b>: If `path` cannot be opened for reading.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.random_crop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.random_crop.md
new file mode 100644
index 0000000000..d389872919
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.random_crop.md
@@ -0,0 +1,25 @@
+### `tf.random_crop(value, size, seed=None, name=None)` {#random_crop}
+
+Randomly crops a tensor to a given size.
+
+Slices a shape `size` portion out of `value` at a uniformly chosen offset.
+Requires `value.shape >= size`.
+
+If a dimension should not be cropped, pass the full size of that dimension.
+For example, RGB images can be cropped with
+`size = [crop_height, crop_width, 3]`.
+
+##### Args:
+
+
+*  <b>`value`</b>: Input tensor to crop.
+*  <b>`size`</b>: 1-D tensor with size the rank of `value`.
+*  <b>`seed`</b>: Python integer. Used to create a random seed. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  A cropped tensor of the same rank as `value` and shape `size`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.random_uniform_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.random_uniform_initializer.md
new file mode 100644
index 0000000000..1afd318d3b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.random_uniform_initializer.md
@@ -0,0 +1,25 @@
+### `tf.random_uniform_initializer(minval=0.0, maxval=1.0, seed=None, dtype=tf.float32)` {#random_uniform_initializer}
+
+Returns an initializer that generates tensors with a uniform distribution.
+
+##### Args:
+
+
+*  <b>`minval`</b>: a python scalar or a scalar tensor. lower bound of the range
+    of random values to generate.
+*  <b>`maxval`</b>: a python scalar or a scalar tensor. upper bound of the range
+    of random values to generate.
+*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`dtype`</b>: The data type. Only floating point types are supported.
+
+##### Returns:
+
+  An initializer that generates tensors with a uniform distribution.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.range.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.range.md
deleted file mode 100644
index c33825d3be..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.range.md
+++ /dev/null
@@ -1,37 +0,0 @@
-### `tf.range(start, limit=None, delta=1, name='range')` {#range}
-
-Creates a sequence of integers.
-
-Creates a sequence of integers that begins at `start` and extends by
-increments of `delta` up to but not including `limit`.
-
-Like the Python builtin `range`, `start` defaults to 0, so that
-`range(n) = range(0, n)`.
-
-For example:
-
-```
-# 'start' is 3
-# 'limit' is 18
-# 'delta' is 3
-tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
-
-# 'limit' is 5
-tf.range(limit) ==> [0, 1, 2, 3, 4]
-```
-
-##### Args:
-
-
-*  <b>`start`</b>: A 0-D (scalar) of type `int32`. First entry in sequence.
-    Defaults to 0.
-*  <b>`limit`</b>: A 0-D (scalar) of type `int32`. Upper limit of sequence,
-    exclusive.
-*  <b>`delta`</b>: A 0-D `Tensor` (scalar) of type `int32`. Optional. Default is 1.
-    Number that increments `start`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An 1-D `int32` `Tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.reduce_all.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.reduce_all.md
deleted file mode 100644
index 3137d5c49e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.reduce_all.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.reduce_all(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_all}
-
-Computes the "logical and" of elements across dimensions of a tensor.
-
-Reduces `input_tensor` along the dimensions given in `reduction_indices`.
-Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
-entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
-are retained with length 1.
-
-If `reduction_indices` has no entries, all dimensions are reduced, and a
-tensor with a single element is returned.
-
-For example:
-
-```python
-# 'x' is [[True,  True]
-#         [False, False]]
-tf.reduce_all(x) ==> False
-tf.reduce_all(x, 0) ==> [False, False]
-tf.reduce_all(x, 1) ==> [True, False]
-```
-
-##### Args:
-
-
-*  <b>`input_tensor`</b>: The boolean tensor to reduce.
-*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
-    reduces all dimensions.
-*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The reduced tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.reverse_sequence.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.reverse_sequence.md
deleted file mode 100644
index fac4ac2ebe..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.reverse_sequence.md
+++ /dev/null
@@ -1,76 +0,0 @@
-### `tf.reverse_sequence(input, seq_lengths, seq_dim, batch_dim=None, name=None)` {#reverse_sequence}
-
-Reverses variable length slices.
-
-This op first slices `input` along the dimension `batch_dim`, and for each
-slice `i`, reverses the first `seq_lengths[i]` elements along
-the dimension `seq_dim`.
-
-The elements of `seq_lengths` must obey `seq_lengths[i] < input.dims[seq_dim]`,
-and `seq_lengths` must be a vector of length `input.dims[batch_dim]`.
-
-The output slice `i` along dimension `batch_dim` is then given by input
-slice `i`, with the first `seq_lengths[i]` slices along dimension
-`seq_dim` reversed.
-
-For example:
-
-```prettyprint
-# Given this:
-batch_dim = 0
-seq_dim = 1
-input.dims = (4, 8, ...)
-seq_lengths = [7, 2, 3, 5]
-
-# then slices of input are reversed on seq_dim, but only up to seq_lengths:
-output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
-output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
-output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
-output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]
-
-# while entries past seq_lens are copied through:
-output[0, 7:, :, ...] = input[0, 7:, :, ...]
-output[1, 2:, :, ...] = input[1, 2:, :, ...]
-output[2, 3:, :, ...] = input[2, 3:, :, ...]
-output[3, 2:, :, ...] = input[3, 2:, :, ...]
-```
-
-In contrast, if:
-
-```prettyprint
-# Given this:
-batch_dim = 2
-seq_dim = 0
-input.dims = (8, ?, 4, ...)
-seq_lengths = [7, 2, 3, 5]
-
-# then slices of input are reversed on seq_dim, but only up to seq_lengths:
-output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
-output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
-output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
-output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]
-
-# while entries past seq_lens are copied through:
-output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
-output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
-output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
-output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. The input to reverse.
-*  <b>`seq_lengths`</b>: A `Tensor` of type `int64`.
-    1-D with length `input.dims(batch_dim)` and
-    `max(seq_lengths) < input.dims(seq_dim)`
-*  <b>`seq_dim`</b>: An `int`. The dimension which is partially reversed.
-*  <b>`batch_dim`</b>: An optional `int`. Defaults to `0`.
-    The dimension along which reversal is performed.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-  The partially reversed input. It has the same shape as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.sign.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.sign.md
new file mode 100644
index 0000000000..f0c021a741
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.sign.md
@@ -0,0 +1,18 @@
+### `tf.sign(x, name=None)` {#sign}
+
+Returns an element-wise indication of the sign of a number.
+
+`y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`.
+
+For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.sin.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.sin.md
new file mode 100644
index 0000000000..aeeaf0c7e6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.sin.md
@@ -0,0 +1,14 @@
+### `tf.sin(x, name=None)` {#sin}
+
+Computes sin of x element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.space_to_depth.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.space_to_depth.md
deleted file mode 100644
index 68706d2e5a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.space_to_depth.md
+++ /dev/null
@@ -1,87 +0,0 @@
-### `tf.space_to_depth(input, block_size, name=None)` {#space_to_depth}
-
-SpaceToDepth for tensors of type T.
-
-Rearranges blocks of spatial data, into depth. More specifically,
-this op outputs a copy of the input tensor where values from the `height`
-and `width` dimensions are moved to the `depth` dimension.
-The attr `block_size` indicates the input block size and how the data is moved.
-
-  * Non-overlapping blocks of size `block_size x block size` are rearranged
-    into depth at each location.
-  * The depth of the output tensor is `input_depth * block_size * block_size`.
-  * The input tensor's height and width must be divisible by block_size.
-
-That is, assuming the input is in the shape:
-`[batch, height, width, depth]`,
-the shape of the output will be:
-`[batch, height/block_size, width/block_size, depth*block_size*block_size]`
-
-This operation requires that the input tensor be of rank 4, and that
-`block_size` be >=1 and a divisor of both the input `height` and `width`.
-
-This operation is useful for resizing the activations between convolutions
-(but keeping all data), e.g. instead of pooling. It is also useful for training
-purely convolutional models.
-
-For example, given this input of shape `[1, 2, 2, 1]`, and block_size of 2:
-
-```prettyprint
-x = [[[[1], [2]],
-      [[3], [4]]]]
-```
-
-This operation will output a tensor of shape `[1, 1, 1, 4]`:
-
-```prettyprint
-[[[[1, 2, 3, 4]]]]
-```
-
-Here, the input has a batch of 1 and each batch element has shape `[2, 2, 1]`,
-the corresponding output will have a single element (i.e. width and height are
-both 1) and will have a depth of 4 channels (1 * block_size * block_size).
-The output element shape is `[1, 1, 4]`.
-
-For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`, e.g.
-
-```prettyprint
-x = [[[[1, 2, 3], [4, 5, 6]],
-      [[7, 8, 9], [10, 11, 12]]]]
-```
-
-This operation, for block_size of 2, will return the following tensor of shape
-`[1, 1, 1, 12]`
-
-```prettyprint
-[[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
-```
-
-Similarly, for the following input of shape `[1 4 4 1]`, and a block size of 2:
-
-```prettyprint
-x = [[[[1],   [2],  [5],  [6]],
-      [[3],   [4],  [7],  [8]],
-      [[9],  [10], [13],  [14]],
-      [[11], [12], [15],  [16]]]]
-```
-
-the operator will return the following tensor of shape `[1 2 2 4]`:
-
-```prettyprint
-x = [[[[1, 2, 3, 4],
-       [5, 6, 7, 8]],
-      [[9, 10, 11, 12],
-       [13, 14, 15, 16]]]]
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`.
-*  <b>`block_size`</b>: An `int`. The size of the spatial block.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.sparse_mask.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.sparse_mask.md
deleted file mode 100644
index d2fa38733b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.sparse_mask.md
+++ /dev/null
@@ -1,39 +0,0 @@
-### `tf.sparse_mask(a, mask_indices, name=None)` {#sparse_mask}
-
-Masks elements of `IndexedSlices`.
-
-Given an `IndexedSlices` instance `a`, returns another `IndexedSlices` that
-contains a subset of the slices of `a`. Only the slices at indices specified
-in `mask_indices` are returned.
-
-This is useful when you need to extract a subset of slices in an
-`IndexedSlices` object.
-
-For example:
-
-```python
-# `a` contains slices at indices [12, 26, 37, 45] from a large tensor
-# with shape [1000, 10]
-a.indices => [12, 26, 37, 45]
-tf.shape(a.values) => [4, 10]
-
-# `b` will be the subset of `a` slices at its second and third indices, so
-# we want to mask of its first and last indices (which are at absolute
-# indices 12, 45)
-b = tf.sparse_mask(a, [12, 45])
-
-b.indices => [26, 37]
-tf.shape(b.values) => [2, 10]
-
-```
-
-##### Args:
-
-  * `a`: An `IndexedSlices` instance.
-  * `mask_indices`: Indices of elements to mask.
-  * `name`: A name for the operation (optional).
-
-##### Returns:
-
-  The masked `IndexedSlices` instance.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.string_to_hash_bucket_strong.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.string_to_hash_bucket_strong.md
new file mode 100644
index 0000000000..67cf3b6fd9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.string_to_hash_bucket_strong.md
@@ -0,0 +1,30 @@
+### `tf.string_to_hash_bucket_strong(input, num_buckets, key, name=None)` {#string_to_hash_bucket_strong}
+
+Converts each string in the input Tensor to its hash mod by a number of buckets.
+
+The hash function is deterministic on the content of the string within the
+process. The hash function is a keyed hash function, where attribute `key`
+defines the key of the hash function. `key` is an array of 2 elements.
+
+A strong hash is important when inputs may be malicious, e.g. URLs with
+additional components. Adversaries could try to make their inputs hash to the
+same bucket for a denial-of-service attack or to skew the results. A strong
+hash prevents this by making it dificult, if not infeasible, to compute inputs
+that hash to the same bucket. This comes at a cost of roughly 4x higher compute
+time than tf.string_to_hash_bucket_fast.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `string`. The strings to assign a hash bucket.
+*  <b>`num_buckets`</b>: An `int` that is `>= 1`. The number of buckets.
+*  <b>`key`</b>: A list of `ints`.
+    The key for the keyed hash function passed as a list of two uint64
+    elements.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `int64`.
+  A Tensor of the same shape as the input `string_tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.tanh.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.tanh.md
new file mode 100644
index 0000000000..b41e51c019
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.tanh.md
@@ -0,0 +1,16 @@
+### `tf.tanh(x, name=None)` {#tanh}
+
+Computes hyperbolic tangent of `x` element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A Tensor with type `float`, `double`, `int32`, `complex64`, `int64`,
+    or `qint32`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A Tensor with the same type as `x` if `x.dtype != qint32` otherwise
+    the return type is `quint8`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.test.main.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.test.main.md
deleted file mode 100644
index c7aa9cf801..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.test.main.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.test.main()` {#main}
-
-Runs all unit tests.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.to_double.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.to_double.md
deleted file mode 100644
index 0cabea178e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.to_double.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.to_double(x, name='ToDouble')` {#to_double}
-
-Casts a tensor to type `float64`.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` or `SparseTensor` with same shape as `x` with type `float64`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `x` cannot be cast to the `float64`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.to_float.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.to_float.md
deleted file mode 100644
index b45b49b982..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.to_float.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.to_float(x, name='ToFloat')` {#to_float}
-
-Casts a tensor to type `float32`.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` or `SparseTensor` with same shape as `x` with type `float32`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `x` cannot be cast to the `float32`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.trace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.trace.md
deleted file mode 100644
index 3b1e71fda1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.trace.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.trace(x, name=None)` {#trace}
-
-Compute the trace of a tensor `x`.
-
-`trace(x)` returns the sum of along the diagonal.
-
-For example:
-
-```python
-# 'x' is [[1, 1],
-#         [1, 1]]
-tf.trace(x) ==> 2
-
-# 'x' is [[1,2,3],
-#         [4,5,6],
-#         [7,8,9]]
-tf.trace(x) ==> 15
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: 2-D tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The trace of input tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.FtrlOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.FtrlOptimizer.md
deleted file mode 100644
index 4fe719ee6b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.FtrlOptimizer.md
+++ /dev/null
@@ -1,32 +0,0 @@
-Optimizer that implements the FTRL algorithm.
-
-See this [paper](
-https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
-
-- - -
-
-#### `tf.train.FtrlOptimizer.__init__(learning_rate, learning_rate_power=-0.5, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, use_locking=False, name='Ftrl')` {#FtrlOptimizer.__init__}
-
-Construct a new FTRL optimizer.
-
-##### Args:
-
-
-*  <b>`learning_rate`</b>: A float value or a constant float `Tensor`.
-*  <b>`learning_rate_power`</b>: A float value, must be less or equal to zero.
-*  <b>`initial_accumulator_value`</b>: The starting value for accumulators.
-    Only positive values are allowed.
-*  <b>`l1_regularization_strength`</b>: A float value, must be greater than or
-    equal to zero.
-*  <b>`l2_regularization_strength`</b>: A float value, must be greater than or
-    equal to zero.
-*  <b>`use_locking`</b>: If `True` use locks for update operations.
-*  <b>`name`</b>: Optional name prefix for the operations created when applying
-    gradients.  Defaults to "Ftrl".
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If one of the arguments is invalid.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.LooperThread.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.LooperThread.md
new file mode 100644
index 0000000000..046f35d718
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.LooperThread.md
@@ -0,0 +1,215 @@
+A thread that runs code repeatedly, optionally on a timer.
+
+This thread class is intended to be used with a `Coordinator`.  It repeatedly
+runs code specified either as `target` and `args` or by the `run_loop()`
+method.
+
+Before each run the thread checks if the coordinator has requested stop.  In
+that case the looper thread terminates immediately.
+
+If the code being run raises an exception, that exception is reported to the
+coordinator and the thread terminates.  The coordinator will then request all
+the other threads it coordinates to stop.
+
+You typically pass looper threads to the supervisor `Join()` method.
+- - -
+
+#### `tf.train.LooperThread.__init__(coord, timer_interval_secs, target=None, args=None, kwargs=None)` {#LooperThread.__init__}
+
+Create a LooperThread.
+
+##### Args:
+
+
+*  <b>`coord`</b>: A Coordinator.
+*  <b>`timer_interval_secs`</b>: Time boundaries at which to call Run(), or None
+    if it should be called back to back.
+*  <b>`target`</b>: Optional callable object that will be executed in the thread.
+*  <b>`args`</b>: Optional arguments to pass to `target` when calling it.
+*  <b>`kwargs`</b>: Optional keyword arguments to pass to `target` when calling it.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If one of the arguments is invalid.
+
+
+- - -
+
+#### `tf.train.LooperThread.daemon` {#LooperThread.daemon}
+
+A boolean value indicating whether this thread is a daemon thread (True) or not (False).
+
+This must be set before start() is called, otherwise RuntimeError is
+raised. Its initial value is inherited from the creating thread; the
+main thread is not a daemon thread and therefore all threads created in
+the main thread default to daemon = False.
+
+The entire Python program exits when no alive non-daemon threads are
+left.
+
+
+- - -
+
+#### `tf.train.LooperThread.getName()` {#LooperThread.getName}
+
+
+
+
+- - -
+
+#### `tf.train.LooperThread.ident` {#LooperThread.ident}
+
+Thread identifier of this thread or None if it has not been started.
+
+This is a nonzero integer. See the thread.get_ident() function. Thread
+identifiers may be recycled when a thread exits and another thread is
+created. The identifier is available even after the thread has exited.
+
+
+- - -
+
+#### `tf.train.LooperThread.isAlive()` {#LooperThread.isAlive}
+
+Return whether the thread is alive.
+
+This method returns True just before the run() method starts until just
+after the run() method terminates. The module function enumerate()
+returns a list of all alive threads.
+
+
+- - -
+
+#### `tf.train.LooperThread.isDaemon()` {#LooperThread.isDaemon}
+
+
+
+
+- - -
+
+#### `tf.train.LooperThread.is_alive()` {#LooperThread.is_alive}
+
+Return whether the thread is alive.
+
+This method returns True just before the run() method starts until just
+after the run() method terminates. The module function enumerate()
+returns a list of all alive threads.
+
+
+- - -
+
+#### `tf.train.LooperThread.join(timeout=None)` {#LooperThread.join}
+
+Wait until the thread terminates.
+
+This blocks the calling thread until the thread whose join() method is
+called terminates -- either normally or through an unhandled exception
+or until the optional timeout occurs.
+
+When the timeout argument is present and not None, it should be a
+floating point number specifying a timeout for the operation in seconds
+(or fractions thereof). As join() always returns None, you must call
+isAlive() after join() to decide whether a timeout happened -- if the
+thread is still alive, the join() call timed out.
+
+When the timeout argument is not present or None, the operation will
+block until the thread terminates.
+
+A thread can be join()ed many times.
+
+join() raises a RuntimeError if an attempt is made to join the current
+thread as that would cause a deadlock. It is also an error to join() a
+thread before it has been started and attempts to do so raises the same
+exception.
+
+
+- - -
+
+#### `tf.train.LooperThread.loop(coord, timer_interval_secs, target, args=None, kwargs=None)` {#LooperThread.loop}
+
+Start a LooperThread that calls a function periodically.
+
+If `timer_interval_secs` is None the thread calls `target(args)`
+repeatedly.  Otherwise `target(args)` is called every `timer_interval_secs`
+seconds.  The thread terminates when a stop of the coordinator is
+requested.
+
+##### Args:
+
+
+*  <b>`coord`</b>: A Coordinator.
+*  <b>`timer_interval_secs`</b>: Number. Time boundaries at which to call `target`.
+*  <b>`target`</b>: A callable object.
+*  <b>`args`</b>: Optional arguments to pass to `target` when calling it.
+*  <b>`kwargs`</b>: Optional keyword arguments to pass to `target` when calling it.
+
+##### Returns:
+
+  The started thread.
+
+
+- - -
+
+#### `tf.train.LooperThread.name` {#LooperThread.name}
+
+A string used for identification purposes only.
+
+It has no semantics. Multiple threads may be given the same name. The
+initial name is set by the constructor.
+
+
+- - -
+
+#### `tf.train.LooperThread.run()` {#LooperThread.run}
+
+
+
+
+- - -
+
+#### `tf.train.LooperThread.run_loop()` {#LooperThread.run_loop}
+
+Called at 'timer_interval_secs' boundaries.
+
+
+- - -
+
+#### `tf.train.LooperThread.setDaemon(daemonic)` {#LooperThread.setDaemon}
+
+
+
+
+- - -
+
+#### `tf.train.LooperThread.setName(name)` {#LooperThread.setName}
+
+
+
+
+- - -
+
+#### `tf.train.LooperThread.start()` {#LooperThread.start}
+
+Start the thread's activity.
+
+It must be called at most once per thread object. It arranges for the
+object's run() method to be invoked in a separate thread of control.
+
+This method will raise a RuntimeError if called more than once on the
+same thread object.
+
+
+- - -
+
+#### `tf.train.LooperThread.start_loop()` {#LooperThread.start_loop}
+
+Called when the thread starts.
+
+
+- - -
+
+#### `tf.train.LooperThread.stop_loop()` {#LooperThread.stop_loop}
+
+Called when the thread stops.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.MomentumOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.MomentumOptimizer.md
deleted file mode 100644
index 45256f65fc..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.MomentumOptimizer.md
+++ /dev/null
@@ -1,18 +0,0 @@
-Optimizer that implements the Momentum algorithm.
-
-- - -
-
-#### `tf.train.MomentumOptimizer.__init__(learning_rate, momentum, use_locking=False, name='Momentum')` {#MomentumOptimizer.__init__}
-
-Construct a new Momentum optimizer.
-
-##### Args:
-
-
-*  <b>`learning_rate`</b>: A `Tensor` or a floating point value.  The learning rate.
-*  <b>`momentum`</b>: A `Tensor` or a floating point value.  The momentum.
-*  <b>`use_locking`</b>: If `True` use locks for update operations.
-*  <b>`name`</b>: Optional name prefix for the operations created when applying
-    gradients.  Defaults to "Momentum".
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.RMSPropOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.RMSPropOptimizer.md
deleted file mode 100644
index 317f1e2adf..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.RMSPropOptimizer.md
+++ /dev/null
@@ -1,23 +0,0 @@
-Optimizer that implements the RMSProp algorithm.
-
-See the [paper]
-(http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).
-
-- - -
-
-#### `tf.train.RMSPropOptimizer.__init__(learning_rate, decay=0.9, momentum=0.0, epsilon=1e-10, use_locking=False, name='RMSProp')` {#RMSPropOptimizer.__init__}
-
-Construct a new RMSProp optimizer.
-
-##### Args:
-
-
-*  <b>`learning_rate`</b>: A Tensor or a floating point value.  The learning rate.
-*  <b>`decay`</b>: Discounting factor for the history/coming gradient
-*  <b>`momentum`</b>: A scalar tensor.
-*  <b>`epsilon`</b>: Small value to avoid zero denominator.
-*  <b>`use_locking`</b>: If True use locks for update operation.
-*  <b>`name`</b>: Optional name prefix for the operations created when applying
-    gradients. Defaults to "RMSProp".
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.Saver.from_proto.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.Saver.from_proto.md
deleted file mode 100644
index 247f621e8a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.Saver.from_proto.md
+++ /dev/null
@@ -1,4 +0,0 @@
-#### `tf.train.Saver.from_proto(saver_def)` {#Saver.from_proto}
-
-Returns a `Saver` object created from `saver_def`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.Server.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.Server.md
new file mode 100644
index 0000000000..3f87ed3bf0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.Server.md
@@ -0,0 +1,113 @@
+An in-process TensorFlow server, for use in distributed training.
+
+A `tf.train.Server` instance encapsulates a set of devices and a
+[`tf.Session`](../../api_docs/python/client.md#Session) target that
+can participate in distributed training. A server belongs to a
+cluster (specified by a [`tf.train.ClusterSpec`](#ClusterSpec)), and
+corresponds to a particular task in a named job. The server can
+communicate with any other server in the same cluster.
+
+- - -
+
+#### `tf.train.Server.__init__(server_or_cluster_def, job_name=None, task_index=None, protocol=None, start=True)` {#Server.__init__}
+
+Creates a new server with the given definition.
+
+The `job_name`, `task_index`, and `protocol` arguments are optional, and
+override any information provided in `server_or_cluster_def`.
+
+##### Args:
+
+
+*  <b>`server_or_cluster_def`</b>: A `tf.train.ServerDef` or
+    `tf.train.ClusterDef` protocol buffer, or a
+    `tf.train.ClusterSpec` object, describing the server to be
+    created and/or the cluster of which it is a member.
+*  <b>`job_name`</b>: (Optional.) Specifies the name of the job of which the server
+    is a member. Defaults to the value in `server_or_cluster_def`, if
+    specified.
+*  <b>`task_index`</b>: (Optional.) Specifies the task index of the server in its
+    job. Defaults to the value in `server_or_cluster_def`, if specified.
+    Otherwise defaults to 0 if the server's job has only one task.
+*  <b>`protocol`</b>: (Optional.) Specifies the protocol to be used by the server.
+    Acceptable values include `"grpc"`. Defaults to the value in
+    `server_or_cluster_def`, if specified. Otherwise defaults to `"grpc"`.
+*  <b>`start`</b>: (Optional.) Boolean, indicating whether to start the server
+    after creating it. Defaults to `True`.
+
+##### Raises:
+
+  tf.errors.OpError: Or one of its subclasses if an error occurs while
+    creating the TensorFlow server.
+
+
+- - -
+
+#### `tf.train.Server.create_local_server(start=True)` {#Server.create_local_server}
+
+Creates a new single-process cluster running on the local host.
+
+This method is a convenience wrapper for creating a
+`tf.train.Server` with a `tf.train.ServerDef` that specifies a
+single-process cluster containing a single task in a job called
+`"local"`.
+
+##### Args:
+
+
+*  <b>`start`</b>: (Optional.) Boolean, indicating whether to start the server after
+    creating it. Defaults to `True`.
+
+##### Returns:
+
+  A local `tf.train.Server`.
+
+
+- - -
+
+#### `tf.train.Server.target` {#Server.target}
+
+Returns the target for a `tf.Session` to connect to this server.
+
+To create a
+[`tf.Session`](../../api_docs/python/client.md#Session) that
+connects to this server, use the following snippet:
+
+```python
+server = tf.train.Server(...)
+with tf.Session(server.target):
+  # ...
+```
+
+##### Returns:
+
+  A string containing a session target for this server.
+
+
+
+- - -
+
+#### `tf.train.Server.start()` {#Server.start}
+
+Starts this server.
+
+##### Raises:
+
+  tf.errors.OpError: Or one of its subclasses if an error occurs while
+    starting the TensorFlow server.
+
+
+- - -
+
+#### `tf.train.Server.join()` {#Server.join}
+
+Blocks until the server has shut down.
+
+This method currently blocks forever.
+
+##### Raises:
+
+  tf.errors.OpError: Or one of its subclasses if an error occurs while
+    joining the TensorFlow server.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.SummaryWriter.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.SummaryWriter.md
deleted file mode 100644
index a7f5aef5f1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.SummaryWriter.md
+++ /dev/null
@@ -1,170 +0,0 @@
-Writes `Summary` protocol buffers to event files.
-
-The `SummaryWriter` class provides a mechanism to create an event file in a
-given directory and add summaries and events to it. The class updates the
-file contents asynchronously. This allows a training program to call methods
-to add data to the file directly from the training loop, without slowing down
-training.
-
-- - -
-
-#### `tf.train.SummaryWriter.__init__(logdir, graph=None, max_queue=10, flush_secs=120, graph_def=None)` {#SummaryWriter.__init__}
-
-Creates a `SummaryWriter` and an event file.
-
-On construction the summary writer creates a new event file in `logdir`.
-This event file will contain `Event` protocol buffers constructed when you
-call one of the following functions: `add_summary()`, `add_session_log()`,
-`add_event()`, or `add_graph()`.
-
-If you pass a `Graph` to the constructor it is added to
-the event file. (This is equivalent to calling `add_graph()` later).
-
-TensorBoard will pick the graph from the file and display it graphically so
-you can interactively explore the graph you built. You will usually pass
-the graph from the session in which you launched it:
-
-```python
-...create a graph...
-# Launch the graph in a session.
-sess = tf.Session()
-# Create a summary writer, add the 'graph' to the event file.
-writer = tf.train.SummaryWriter(<some-directory>, sess.graph)
-```
-
-The other arguments to the constructor control the asynchronous writes to
-the event file:
-
-*  `flush_secs`: How often, in seconds, to flush the added summaries
-   and events to disk.
-*  `max_queue`: Maximum number of summaries or events pending to be
-   written to disk before one of the 'add' calls block.
-
-##### Args:
-
-
-*  <b>`logdir`</b>: A string. Directory where event file will be written.
-*  <b>`graph`</b>: A `Graph` object, such as `sess.graph`.
-*  <b>`max_queue`</b>: Integer. Size of the queue for pending events and summaries.
-*  <b>`flush_secs`</b>: Number. How often, in seconds, to flush the
-    pending events and summaries to disk.
-*  <b>`graph_def`</b>: DEPRECATED: Use the `graph` argument instead.
-
-
-
-- - -
-
-#### `tf.train.SummaryWriter.add_summary(summary, global_step=None)` {#SummaryWriter.add_summary}
-
-Adds a `Summary` protocol buffer to the event file.
-
-This method wraps the provided summary in an `Event` protocol buffer
-and adds it to the event file.
-
-You can pass the result of evaluating any summary op, using
-[`Session.run()`](client.md#Session.run) or
-[`Tensor.eval()`](framework.md#Tensor.eval), to this
-function. Alternatively, you can pass a `tf.Summary` protocol
-buffer that you populate with your own data. The latter is
-commonly done to report evaluation results in event files.
-
-##### Args:
-
-
-*  <b>`summary`</b>: A `Summary` protocol buffer, optionally serialized as a string.
-*  <b>`global_step`</b>: Number. Optional global step value to record with the
-    summary.
-
-
-- - -
-
-#### `tf.train.SummaryWriter.add_session_log(session_log, global_step=None)` {#SummaryWriter.add_session_log}
-
-Adds a `SessionLog` protocol buffer to the event file.
-
-This method wraps the provided session in an `Event` procotol buffer
-and adds it to the event file.
-
-##### Args:
-
-
-*  <b>`session_log`</b>: A `SessionLog` protocol buffer.
-*  <b>`global_step`</b>: Number. Optional global step value to record with the
-    summary.
-
-
-- - -
-
-#### `tf.train.SummaryWriter.add_event(event)` {#SummaryWriter.add_event}
-
-Adds an event to the event file.
-
-##### Args:
-
-
-*  <b>`event`</b>: An `Event` protocol buffer.
-
-
-- - -
-
-#### `tf.train.SummaryWriter.add_graph(graph, global_step=None, graph_def=None)` {#SummaryWriter.add_graph}
-
-Adds a `Graph` to the event file.
-
-The graph described by the protocol buffer will be displayed by
-TensorBoard. Most users pass a graph in the constructor instead.
-
-##### Args:
-
-
-*  <b>`graph`</b>: A `Graph` object, such as `sess.graph`.
-*  <b>`global_step`</b>: Number. Optional global step counter to record with the
-    graph.
-*  <b>`graph_def`</b>: DEPRECATED. Use the `graph` parameter instead.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If both graph and graph_def are passed to the method.
-
-
-- - -
-
-#### `tf.train.SummaryWriter.add_run_metadata(run_metadata, tag, global_step=None)` {#SummaryWriter.add_run_metadata}
-
-Adds a metadata information for a single session.run() call.
-
-##### Args:
-
-
-*  <b>`run_metadata`</b>: A `RunMetadata` protobuf object.
-*  <b>`tag`</b>: The tag name for this metadata.
-*  <b>`global_step`</b>: Number. Optional global step counter to record with the
-    StepStats.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the provided tag was already used for this type of event.
-
-
-
-- - -
-
-#### `tf.train.SummaryWriter.flush()` {#SummaryWriter.flush}
-
-Flushes the event file to disk.
-
-Call this method to make sure that all pending events have been written to
-disk.
-
-
-- - -
-
-#### `tf.train.SummaryWriter.close()` {#SummaryWriter.close}
-
-Flushes the event file to disk and close the file.
-
-Call this method when you do not need the summary writer anymore.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.Supervisor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.Supervisor.md
deleted file mode 100644
index b3d17eac2d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.Supervisor.md
+++ /dev/null
@@ -1,845 +0,0 @@
-A training helper that checkpoints models and computes summaries.
-
-The Supervisor is a small wrapper around a `Coordinator`, a `Saver`,
-and a `SessionManager` that takes care of common needs of Tensorflow
-training programs.
-
-#### Use for a single program
-
-```python
-with tf.Graph().as_default():
-  ...add operations to the graph...
-  # Create a Supervisor that will checkpoint the model in '/tmp/mydir'.
-  sv = Supervisor(logdir='/tmp/mydir')
-  # Get a Tensorflow session managed by the supervisor.
-  with sv.managed_session(FLAGS.master) as sess:
-    # Use the session to train the graph.
-    while not sv.should_stop():
-      sess.run(<my_train_op>)
-```
-
-Within the `with sv.managed_session()` block all variables in the graph have
-been initialized.  In addition, a few services have been started to
-checkpoint the model and add summaries to the event log.
-
-If the program crashes and is restarted, the managed session automatically
-reinitialize variables from the most recent checkpoint.
-
-The supervisor is notified of any exception raised by one of the services.
-After an exception is raised, `should_stop()` returns `True`.  In that case
-the training loop should also stop.  This is why the training loop has to
-check for `sv.should_stop()`.
-
-Exceptions that indicate that the training inputs have been exhausted,
-`tf.errors.OutOfRangeError`, also cause `sv.should_stop()` to return `True`
-but are not re-raised from the `with` block: they indicate a normal
-termination.
-
-#### Use for multiple replicas
-
-To train with replicas you deploy the same program in a `Cluster`.
-One of the tasks must be identified as the *chief*: the task that handles
-initialization, checkpoints, summaries, and recovery.  The other tasks
-depend on the *chief* for these services.
-
-The only change you have to do to the single program code is to indicate
-if the program is running as the *chief*.
-
-```python
-# Choose a task as the chief. This could be based on server_def.task_index,
-# or job_def.name, or job_def.tasks. It's entirely up to the end user.
-# But there can be only one *chief*.
-is_chief = (server_def.task_index == 0)
-server = tf.train.Server(server_def)
-
-with tf.Graph().as_default():
-  ...add operations to the graph...
-  # Create a Supervisor that uses log directory on a shared file system.
-  # Indicate if you are the 'chief'
-  sv = Supervisor(logdir='/shared_directory/...', is_chief=is_chief)
-  # Get a Session in a TensorFlow server on the cluster.
-  with sv.managed_session(server.target) as sess:
-    # Use the session to train the graph.
-    while not sv.should_stop():
-      sess.run(<my_train_op>)
-```
-
-In the *chief* task, the `Supervisor` works exactly as in the first example
-above.  In the other tasks `sv.managed_session()` waits for the Model to have
-been intialized before returning a session to the training code.  The
-non-chief tasks depend on the chief taks for initializing the model.
-
-If one of the tasks crashes and restarts, `managed_session()`
-checks if the Model is initialized.  If yes, it just creates a session and
-returns it to the training code that proceeds normally.  If the model needs
-to be initialized, the chief task takes care of reinitializing it; the other
-tasks just wait for the model to have been initialized.
-
-NOTE: This modified program still works fine as a single program.
-The single program marks itself as the chief.
-
-#### What `master` string to use
-
-Whether you are running on your machine or in the cluster you can use the
-following values for the --master flag:
-
-* Specifying `''` requests an in-process session that does not use RPC.
-
-* Specifying `'local'` requests a session that uses the RPC-based
-  "Master interface" to run TensorFlow programs. See
-  [`tf.train.Server.create_local_server()`](#Server.create_local_server) for
-  details.
-
-* Specifying `'grpc://hostname:port'` requests a session that uses
-  the RPC interface to a specific , and also allows the in-process
-  master to access remote tensorflow workers. Often, it is
-  appropriate to pass `server.target` (for some `tf.train.Server`
-  named `server).
-
-#### Advanced use
-
-##### Launching additional services
-
-`managed_session()` launches the Checkpoint and Summary services (threads).
-If you need more services to run you can simply launch them in the block
-controlled by `managed_session()`.
-
-Example: Start a thread to print losses.  We want this thread to run
-every 60 seconds, so we launch it with `sv.loop()`.
-
-  ```python
-  ...
-  sv = Supervisor(logdir='/tmp/mydir')
-  with sv.managed_session(FLAGS.master) as sess:
-    sv.loop(60, print_loss, (sess))
-    while not sv.should_stop():
-      sess.run(my_train_op)
-  ```
-
-##### Launching fewer services
-
-`managed_session()` launches the "summary" and "checkpoint" threads which use
-either the optionally `summary_op` and `saver` passed to the constructor, or
-default ones created automatically by the supervisor.  If you want to run
-your own summary and checkpointing logic, disable these services by passing
-`None` to the `summary_op` and `saver` parameters.
-
-Example: Create summaries manually every 100 steps in the chief.
-
-  ```python
-  # Create a Supervisor with no automatic summaries.
-  sv = Supervisor(logdir='/tmp/mydir', is_chief=is_chief, summary_op=None)
-  # As summary_op was None, managed_session() does not start the
-  # summary thread.
-  with sv.managed_session(FLAGS.master) as sess:
-    for step in xrange(1000000):
-      if sv.should_stop():
-        break
-      if is_chief and step % 100 == 0:
-        # Create the summary every 100 chief steps.
-        sv.summary_computed(sess, sess.run(my_summary_op))
-      else:
-        # Train normally
-        sess.run(my_train_op)
-  ```
-
-##### Custom model initialization
-
-`managed_session()` only supports initializing the model by running an
-`init_op` or restoring from the latest checkpoint.  If you have special
-initialization needs, see how to specify a `local_init_op` when creating the
-supervisor.  You can also use the `SessionManager` directly to create a
-session and check if it could be initialized automatically.
-
-- - -
-
-#### `tf.train.Supervisor.__init__(graph=None, ready_op=0, is_chief=True, init_op=0, init_feed_dict=None, local_init_op=0, logdir=None, summary_op=0, saver=0, global_step=0, save_summaries_secs=120, save_model_secs=600, recovery_wait_secs=30, stop_grace_secs=120, checkpoint_basename='model.ckpt', session_manager=None, summary_writer=0, init_fn=None)` {#Supervisor.__init__}
-
-Create a `Supervisor`.
-
-##### Args:
-
-
-*  <b>`graph`</b>: A `Graph`.  The graph that the model will use.  Defaults to the
-    default `Graph`.  The supervisor may add operations to the graph before
-    creating a session, but the graph should not be modified by the caller
-    after passing it to the supervisor.
-*  <b>`ready_op`</b>: 1-D string `Tensor`.  This tensor is evaluated by supervisors in
-    `prepare_or_wait_for_session()` to check if the model is ready to use.
-    The model is considered ready if it returns an empty array.  Defaults to
-    the tensor returned from `tf.report_uninitialized_variables()`  If
-    `None`, the model is not checked for readiness.
-*  <b>`is_chief`</b>: If True, create a chief supervisor in charge of initializing
-    and restoring the model.  If False, create a supervisor that relies
-    on a chief supervisor for inits and restore.
-*  <b>`init_op`</b>: `Operation`.  Used by chief supervisors to initialize the model
-    when it can not be recovered.  Defaults to an `Operation` that
-    initializes all variables.  If `None`, no initialization is done
-    automatically unless you pass a value for `init_fn`, see below.
-*  <b>`init_feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
-    This feed dictionary will be used when `init_op` is evaluated.
-*  <b>`local_init_op`</b>: `Operation`. Used by all supervisors to run initializations
-    that should run for every new supervisor instance. By default these
-    are table initializers and initializers for local variables.
-    If `None`, no further per supervisor-instance initialization is
-    done automatically.
-*  <b>`logdir`</b>: A string.  Optional path to a directory where to checkpoint the
-    model and log events for the visualizer.  Used by chief supervisors.
-    The directory will be created if it does not exist.
-*  <b>`summary_op`</b>: An `Operation` that returns a Summary for the event logs.
-    Used by chief supervisors if a `logdir` was specified.  Defaults to the
-    operation returned from merge_all_summaries().  If `None`, summaries are
-    not computed automatically.
-*  <b>`saver`</b>: A Saver object.  Used by chief supervisors if a `logdir` was
-    specified.  Defaults to the saved returned by Saver().
-    If `None`, the model is not saved automatically.
-*  <b>`global_step`</b>: An integer Tensor of size 1 that counts steps.  The value
-    from 'global_step' is used in summaries and checkpoint filenames.
-    Default to the op named 'global_step' in the graph if it exists, is of
-    rank 1, size 1, and of type tf.int32 ot tf.int64.  If `None` the global
-    step is not recorded in summaries and checkpoint files.  Used by chief
-    supervisors if a `logdir` was specified.
-*  <b>`save_summaries_secs`</b>: Number of seconds between the computation of
-    summaries for the event log.  Defaults to 120 seconds.  Pass 0 to
-    disable summaries.
-*  <b>`save_model_secs`</b>: Number of seconds between the creation of model
-    checkpoints.  Defaults to 600 seconds.  Pass 0 to disable checkpoints.
-*  <b>`recovery_wait_secs`</b>: Number of seconds between checks that the model
-    is ready.  Used by supervisors when waiting for a chief supervisor
-    to initialize or restore the model.  Defaults to 30 seconds.
-*  <b>`stop_grace_secs`</b>: Grace period, in seconds, given to running threads to
-    stop when `stop()` is called.  Defaults to 120 seconds.
-*  <b>`checkpoint_basename`</b>: The basename for checkpoint saving.
-*  <b>`session_manager`</b>: `SessionManager`, which manages Session creation and
-    recovery. If it is `None`, a default `SessionManager` will be created
-    with the set of arguments passed in for backwards compatibility.
-*  <b>`summary_writer`</b>: `SummaryWriter` to use or `USE_DEFAULT`.  Can be `None`
-    to indicate that no summaries should be written.
-*  <b>`init_fn`</b>: Optional callable used to initialize the model. Called
-    after the optional `init_op` is called.  The callable must accept one
-    argument, the session being initialized.
-
-##### Returns:
-
-  A `Supervisor`.
-
-
-- - -
-
-#### `tf.train.Supervisor.managed_session(master='', config=None, start_standard_services=True, close_summary_writer=True)` {#Supervisor.managed_session}
-
-Returns a context manager for a managed session.
-
-This context manager creates and automatically recovers a session.  It
-optionally starts the standard services that handle checkpoints and
-summaries.  It monitors exceptions raised from the `with` block or from the
-services and stops the supervisor as needed.
-
-The context manager is typically used as follows:
-
-```python
-def train():
-  sv = tf.train.Supervisor(...)
-  with sv.managed_session(<master>) as sess:
-    for step in xrange(..):
-      if sv.should_stop():
-        break
-      sess.run(<my training op>)
-      ...do other things needed at each training step...
-```
-
-An exception raised from the `with` block or one of the service threads is
-raised again when the block exits.  This is done after stopping all threads
-and closing the session.  For example, an `AbortedError` exception, raised
-in case of preemption of one of the workers in a distributed model, is
-raised again when the block exits.
-
-If you want to retry the training loop in case of preemption you can do it
-as follows:
-
-```python
-def main(...):
-  while True
-    try:
-      train()
-    except tf.errors.Aborted:
-      pass
-```
-
-As a special case, exceptions used for control flow, such as
-`OutOfRangeError` which reports that input queues are exhausted, are not
-raised again from the `with` block: they indicate a clean termination of
-the training loop and are considered normal termination.
-
-##### Args:
-
-
-*  <b>`master`</b>: name of the TensorFlow master to use.  See the `tf.Session`
-    constructor for how this is interpreted.
-*  <b>`config`</b>: Optional `ConfigProto` proto used to configure the session.
-    Passed as-is to create the session.
-*  <b>`start_standard_services`</b>: Whether to start the standard services,
-    such as checkpoint, summary and step counter.
-*  <b>`close_summary_writer`</b>: Whether to close the summary writer when
-    closing the session.  Defaults to True.
-
-##### Returns:
-
-  A context manager that yields a `Session` restored from the latest
-  checkpoint or initialized from scratch if not checkpoint exists.  The
-  session is closed when the `with` block exits.
-
-
-- - -
-
-#### `tf.train.Supervisor.prepare_or_wait_for_session(master='', config=None, wait_for_checkpoint=False, max_wait_secs=7200, start_standard_services=True)` {#Supervisor.prepare_or_wait_for_session}
-
-Make sure the model is ready to be used.
-
-Create a session on 'master', recovering or initializing the model as
-needed, or wait for a session to be ready.  If running as the chief
-and `start_standard_service` is set to True, also call the session
-manager to start the standard services.
-
-##### Args:
-
-
-*  <b>`master`</b>: name of the TensorFlow master to use.  See the `tf.Session`
-    constructor for how this is interpreted.
-*  <b>`config`</b>: Optional ConfigProto proto used to configure the session,
-    which is passed as-is to create the session.
-*  <b>`wait_for_checkpoint`</b>: Whether we should wait for the availability of a
-    checkpoint before creating Session. Defaults to False.
-*  <b>`max_wait_secs`</b>: Maximum time to wait for the session to become available.
-*  <b>`start_standard_services`</b>: Whether to start the standard services and the
-    queue runners.
-
-##### Returns:
-
-  A Session object that can be used to drive the model.
-
-
-- - -
-
-#### `tf.train.Supervisor.start_standard_services(sess)` {#Supervisor.start_standard_services}
-
-Start the standard services for 'sess'.
-
-This starts services in the background.  The services started depend
-on the parameters to the constructor and may include:
-
-  - A Summary thread computing summaries every save_summaries_secs.
-  - A Checkpoint thread saving the model every save_model_secs.
-  - A StepCounter thread measure step time.
-
-##### Args:
-
-
-*  <b>`sess`</b>: A Session.
-
-##### Returns:
-
-  A list of threads that are running the standard services.  You can use
-  the Supervisor's Coordinator to join these threads with:
-    sv.coord.Join(<list of threads>)
-
-##### Raises:
-
-
-*  <b>`RuntimeError`</b>: If called with a non-chief Supervisor.
-*  <b>`ValueError`</b>: If not `logdir` was passed to the constructor as the
-    services need a log directory.
-
-
-- - -
-
-#### `tf.train.Supervisor.start_queue_runners(sess, queue_runners=None)` {#Supervisor.start_queue_runners}
-
-Start threads for `QueueRunners`.
-
-Note that the queue runners collected in the graph key `QUEUE_RUNNERS`
-are already started automatically when you create a session with the
-supervisor, so unless you have non-collected queue runners to start
-you do not need to call this explicitely.
-
-##### Args:
-
-
-*  <b>`sess`</b>: A `Session`.
-*  <b>`queue_runners`</b>: A list of `QueueRunners`. If not specified, we'll use the
-    list of queue runners gathered in the graph under the key
-    `GraphKeys.QUEUE_RUNNERS`.
-
-##### Returns:
-
-  The list of threads started for the `QueueRunners`.
-
-
-- - -
-
-#### `tf.train.Supervisor.summary_computed(sess, summary, global_step=None)` {#Supervisor.summary_computed}
-
-Indicate that a summary was computed.
-
-##### Args:
-
-
-*  <b>`sess`</b>: A `Session` object.
-*  <b>`summary`</b>: A Summary proto, or a string holding a serialized summary proto.
-*  <b>`global_step`</b>: Int. global step this summary is associated with. If `None`,
-    it will try to fetch the current step.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if 'summary' is not a Summary proto or a string.
-*  <b>`RuntimeError`</b>: if the Supervisor was created without a `logdir`.
-
-
-
-- - -
-
-#### `tf.train.Supervisor.stop(threads=None, close_summary_writer=True)` {#Supervisor.stop}
-
-Stop the services and the coordinator.
-
-This does not close the session.
-
-##### Args:
-
-
-*  <b>`threads`</b>: Optional list of threads to join with the coordinator.  If
-    `None`, defaults to the threads running the standard services, the
-    threads started for `QueueRunners`, and the threads started by the
-    `loop()` method.  To wait on additional threads, pass the
-    list in this parameter.
-*  <b>`close_summary_writer`</b>: Whether to close the `summary_writer`.  Defaults to
-    `True` if the summary writer was created by the supervisor, `False`
-    otherwise.
-
-
-- - -
-
-#### `tf.train.Supervisor.request_stop(ex=None)` {#Supervisor.request_stop}
-
-Request that the coordinator stop the threads.
-
-See `Coordinator.request_stop()`.
-
-##### Args:
-
-
-*  <b>`ex`</b>: Optional `Exception`, or Python `exc_info` tuple as returned by
-    `sys.exc_info()`.  If this is the first call to `request_stop()` the
-    corresponding exception is recorded and re-raised from `join()`.
-
-
-- - -
-
-#### `tf.train.Supervisor.should_stop()` {#Supervisor.should_stop}
-
-Check if the coordinator was told to stop.
-
-See `Coordinator.should_stop()`.
-
-##### Returns:
-
-  True if the coordinator was told to stop, False otherwise.
-
-
-- - -
-
-#### `tf.train.Supervisor.stop_on_exception()` {#Supervisor.stop_on_exception}
-
-Context handler to stop the supervisor when an exception is raised.
-
-See `Coordinator.stop_on_exception()`.
-
-##### Returns:
-
-  A context handler.
-
-
-- - -
-
-#### `tf.train.Supervisor.wait_for_stop()` {#Supervisor.wait_for_stop}
-
-Block waiting for the coordinator to stop.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.train.Supervisor.Loop(timer_interval_secs, target, args=None, kwargs=None)` {#Supervisor.Loop}
-
-Start a LooperThread that calls a function periodically.
-
-If `timer_interval_secs` is None the thread calls `target(*args, **kwargs)`
-repeatedly.  Otherwise it calls it every `timer_interval_secs`
-seconds.  The thread terminates when a stop is requested.
-
-The started thread is added to the list of threads managed by the supervisor
-so it does not need to be passed to the `stop()` method.
-
-##### Args:
-
-
-*  <b>`timer_interval_secs`</b>: Number. Time boundaries at which to call `target`.
-*  <b>`target`</b>: A callable object.
-*  <b>`args`</b>: Optional arguments to pass to `target` when calling it.
-*  <b>`kwargs`</b>: Optional keyword arguments to pass to `target` when calling it.
-
-##### Returns:
-
-  The started thread.
-
-
-- - -
-
-#### `tf.train.Supervisor.PrepareSession(master='', config=None, wait_for_checkpoint=False, max_wait_secs=7200, start_standard_services=True)` {#Supervisor.PrepareSession}
-
-Make sure the model is ready to be used.
-
-Create a session on 'master', recovering or initializing the model as
-needed, or wait for a session to be ready.  If running as the chief
-and `start_standard_service` is set to True, also call the session
-manager to start the standard services.
-
-##### Args:
-
-
-*  <b>`master`</b>: name of the TensorFlow master to use.  See the `tf.Session`
-    constructor for how this is interpreted.
-*  <b>`config`</b>: Optional ConfigProto proto used to configure the session,
-    which is passed as-is to create the session.
-*  <b>`wait_for_checkpoint`</b>: Whether we should wait for the availability of a
-    checkpoint before creating Session. Defaults to False.
-*  <b>`max_wait_secs`</b>: Maximum time to wait for the session to become available.
-*  <b>`start_standard_services`</b>: Whether to start the standard services and the
-    queue runners.
-
-##### Returns:
-
-  A Session object that can be used to drive the model.
-
-
-- - -
-
-#### `tf.train.Supervisor.RequestStop(ex=None)` {#Supervisor.RequestStop}
-
-Request that the coordinator stop the threads.
-
-See `Coordinator.request_stop()`.
-
-##### Args:
-
-
-*  <b>`ex`</b>: Optional `Exception`, or Python `exc_info` tuple as returned by
-    `sys.exc_info()`.  If this is the first call to `request_stop()` the
-    corresponding exception is recorded and re-raised from `join()`.
-
-
-- - -
-
-#### `tf.train.Supervisor.ShouldStop()` {#Supervisor.ShouldStop}
-
-Check if the coordinator was told to stop.
-
-See `Coordinator.should_stop()`.
-
-##### Returns:
-
-  True if the coordinator was told to stop, False otherwise.
-
-
-- - -
-
-#### `tf.train.Supervisor.StartQueueRunners(sess, queue_runners=None)` {#Supervisor.StartQueueRunners}
-
-Start threads for `QueueRunners`.
-
-Note that the queue runners collected in the graph key `QUEUE_RUNNERS`
-are already started automatically when you create a session with the
-supervisor, so unless you have non-collected queue runners to start
-you do not need to call this explicitely.
-
-##### Args:
-
-
-*  <b>`sess`</b>: A `Session`.
-*  <b>`queue_runners`</b>: A list of `QueueRunners`. If not specified, we'll use the
-    list of queue runners gathered in the graph under the key
-    `GraphKeys.QUEUE_RUNNERS`.
-
-##### Returns:
-
-  The list of threads started for the `QueueRunners`.
-
-
-- - -
-
-#### `tf.train.Supervisor.StartStandardServices(sess)` {#Supervisor.StartStandardServices}
-
-Start the standard services for 'sess'.
-
-This starts services in the background.  The services started depend
-on the parameters to the constructor and may include:
-
-  - A Summary thread computing summaries every save_summaries_secs.
-  - A Checkpoint thread saving the model every save_model_secs.
-  - A StepCounter thread measure step time.
-
-##### Args:
-
-
-*  <b>`sess`</b>: A Session.
-
-##### Returns:
-
-  A list of threads that are running the standard services.  You can use
-  the Supervisor's Coordinator to join these threads with:
-    sv.coord.Join(<list of threads>)
-
-##### Raises:
-
-
-*  <b>`RuntimeError`</b>: If called with a non-chief Supervisor.
-*  <b>`ValueError`</b>: If not `logdir` was passed to the constructor as the
-    services need a log directory.
-
-
-- - -
-
-#### `tf.train.Supervisor.Stop(threads=None, close_summary_writer=True)` {#Supervisor.Stop}
-
-Stop the services and the coordinator.
-
-This does not close the session.
-
-##### Args:
-
-
-*  <b>`threads`</b>: Optional list of threads to join with the coordinator.  If
-    `None`, defaults to the threads running the standard services, the
-    threads started for `QueueRunners`, and the threads started by the
-    `loop()` method.  To wait on additional threads, pass the
-    list in this parameter.
-*  <b>`close_summary_writer`</b>: Whether to close the `summary_writer`.  Defaults to
-    `True` if the summary writer was created by the supervisor, `False`
-    otherwise.
-
-
-- - -
-
-#### `tf.train.Supervisor.StopOnException()` {#Supervisor.StopOnException}
-
-Context handler to stop the supervisor when an exception is raised.
-
-See `Coordinator.stop_on_exception()`.
-
-##### Returns:
-
-  A context handler.
-
-
-- - -
-
-#### `tf.train.Supervisor.SummaryComputed(sess, summary, global_step=None)` {#Supervisor.SummaryComputed}
-
-Indicate that a summary was computed.
-
-##### Args:
-
-
-*  <b>`sess`</b>: A `Session` object.
-*  <b>`summary`</b>: A Summary proto, or a string holding a serialized summary proto.
-*  <b>`global_step`</b>: Int. global step this summary is associated with. If `None`,
-    it will try to fetch the current step.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if 'summary' is not a Summary proto or a string.
-*  <b>`RuntimeError`</b>: if the Supervisor was created without a `logdir`.
-
-
-- - -
-
-#### `tf.train.Supervisor.WaitForStop()` {#Supervisor.WaitForStop}
-
-Block waiting for the coordinator to stop.
-
-
-- - -
-
-#### `tf.train.Supervisor.coord` {#Supervisor.coord}
-
-Return the Coordinator used by the Supervisor.
-
-The Coordinator can be useful if you want to run multiple threads
-during your training.
-
-##### Returns:
-
-  A Coordinator object.
-
-
-- - -
-
-#### `tf.train.Supervisor.global_step` {#Supervisor.global_step}
-
-Return the global_step Tensor used by the supervisor.
-
-##### Returns:
-
-  An integer Tensor for the global_step.
-
-
-- - -
-
-#### `tf.train.Supervisor.init_feed_dict` {#Supervisor.init_feed_dict}
-
-Return the feed dictionary used when evaluating the `init_op`.
-
-##### Returns:
-
-  A feed dictionary or `None`.
-
-
-- - -
-
-#### `tf.train.Supervisor.init_op` {#Supervisor.init_op}
-
-Return the Init Op used by the supervisor.
-
-##### Returns:
-
-  An Op or `None`.
-
-
-- - -
-
-#### `tf.train.Supervisor.is_chief` {#Supervisor.is_chief}
-
-Return True if this is a chief supervisor.
-
-##### Returns:
-
-  A bool.
-
-
-- - -
-
-#### `tf.train.Supervisor.loop(timer_interval_secs, target, args=None, kwargs=None)` {#Supervisor.loop}
-
-Start a LooperThread that calls a function periodically.
-
-If `timer_interval_secs` is None the thread calls `target(*args, **kwargs)`
-repeatedly.  Otherwise it calls it every `timer_interval_secs`
-seconds.  The thread terminates when a stop is requested.
-
-The started thread is added to the list of threads managed by the supervisor
-so it does not need to be passed to the `stop()` method.
-
-##### Args:
-
-
-*  <b>`timer_interval_secs`</b>: Number. Time boundaries at which to call `target`.
-*  <b>`target`</b>: A callable object.
-*  <b>`args`</b>: Optional arguments to pass to `target` when calling it.
-*  <b>`kwargs`</b>: Optional keyword arguments to pass to `target` when calling it.
-
-##### Returns:
-
-  The started thread.
-
-
-- - -
-
-#### `tf.train.Supervisor.ready_op` {#Supervisor.ready_op}
-
-Return the Ready Op used by the supervisor.
-
-##### Returns:
-
-  An Op or `None`.
-
-
-- - -
-
-#### `tf.train.Supervisor.save_model_secs` {#Supervisor.save_model_secs}
-
-Return the delay between checkpoints.
-
-##### Returns:
-
-  A timestamp.
-
-
-- - -
-
-#### `tf.train.Supervisor.save_path` {#Supervisor.save_path}
-
-Return the save path used by the supervisor.
-
-##### Returns:
-
-  A string.
-
-
-- - -
-
-#### `tf.train.Supervisor.save_summaries_secs` {#Supervisor.save_summaries_secs}
-
-Return the delay between summary computations.
-
-##### Returns:
-
-  A timestamp.
-
-
-- - -
-
-#### `tf.train.Supervisor.saver` {#Supervisor.saver}
-
-Return the Saver used by the supervisor.
-
-##### Returns:
-
-  A Saver object.
-
-
-- - -
-
-#### `tf.train.Supervisor.session_manager` {#Supervisor.session_manager}
-
-Return the SessionManager used by the Supervisor.
-
-##### Returns:
-
-  A SessionManager object.
-
-
-- - -
-
-#### `tf.train.Supervisor.summary_op` {#Supervisor.summary_op}
-
-Return the Summary Tensor used by the chief supervisor.
-
-##### Returns:
-
-  A string Tensor for the summary or `None`.
-
-
-- - -
-
-#### `tf.train.Supervisor.summary_writer` {#Supervisor.summary_writer}
-
-Return the SummaryWriter used by the chief supervisor.
-
-##### Returns:
-
-  A SummaryWriter.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.generate_checkpoint_state_proto.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.generate_checkpoint_state_proto.md
deleted file mode 100644
index 7405b289e3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.generate_checkpoint_state_proto.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.train.generate_checkpoint_state_proto(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None)` {#generate_checkpoint_state_proto}
-
-Generates a checkpoint state proto.
-
-##### Args:
-
-
-*  <b>`save_dir`</b>: Directory where the model was saved.
-*  <b>`model_checkpoint_path`</b>: The checkpoint file.
-*  <b>`all_model_checkpoint_paths`</b>: List of strings.  Paths to all not-yet-deleted
-    checkpoints, sorted from oldest to newest.  If this is a non-empty list,
-    the last element must be equal to model_checkpoint_path.  These paths
-    are also saved in the CheckpointState proto.
-
-##### Returns:
-
-  CheckpointState proto with model_checkpoint_path and
-  all_model_checkpoint_paths updated to either absolute paths or
-  relative paths to the current save_dir.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.input_producer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.input_producer.md
new file mode 100644
index 0000000000..41a417aac3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.input_producer.md
@@ -0,0 +1,38 @@
+### `tf.train.input_producer(input_tensor, element_shape=None, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, summary_name=None, name=None)` {#input_producer}
+
+Output the rows of `input_tensor` to a queue for an input pipeline.
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: A tensor with the rows to produce. Must be at
+    one-dimensional. Must either have a fully-defined shape, or
+    `element_shape` must be defined.
+*  <b>`element_shape`</b>: (Optional.) A `TensorShape` representing the shape of a
+    row of `input_tensor`, if it cannot be inferred.
+*  <b>`num_epochs`</b>: (Optional.) An integer. If specified `input_producer` produces
+    each row of `input_tensor` `num_epochs` times before generating an
+    `OutOfRange` error. If not specified, `input_producer` can cycle through
+    the rows of `input_tensor` an unlimited number of times.
+*  <b>`shuffle`</b>: (Optional.) A boolean. If true, the rows are randomly shuffled
+    within each eopch.
+*  <b>`seed`</b>: (Optional.) An integer. The seed to use if `shuffle` is true.
+*  <b>`capacity`</b>: (Optional.) The capacity of the queue to be used for buffering
+    the input.
+*  <b>`shared_name`</b>: (Optional.) If set, this queue will be shared under the given
+    name across multiple sessions.
+*  <b>`summary_name`</b>: (Optional.) If set, a scalar summary for the current queue
+    size will be generated, using this name as part of the tag.
+*  <b>`name`</b>: (Optional.) A name for queue.
+
+##### Returns:
+
+  A queue with the output rows.  A `QueueRunner` for the queue is
+  added to the current `QUEUE_RUNNER` collection of the current
+  graph.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the shape of the input cannot be inferred from the arguments.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.limit_epochs.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.limit_epochs.md
deleted file mode 100644
index ba3e710df4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.limit_epochs.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.train.limit_epochs(tensor, num_epochs=None, name=None)` {#limit_epochs}
-
-Returns tensor `num_epochs` times and then raises an `OutOfRange` error.
-
-##### Args:
-
-
-*  <b>`tensor`</b>: Any `Tensor`.
-*  <b>`num_epochs`</b>: A positive integer (optional).  If specified, limits the number
-    of steps the output tensor may be evaluated.
-*  <b>`name`</b>: A name for the operations (optional).
-
-##### Returns:
-
-  tensor or `OutOfRange`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `num_epochs` is invalid.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.shuffle_batch.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.shuffle_batch.md
new file mode 100644
index 0000000000..bf2591801b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.shuffle_batch.md
@@ -0,0 +1,74 @@
+### `tf.train.shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, num_threads=1, seed=None, enqueue_many=False, shapes=None, shared_name=None, name=None)` {#shuffle_batch}
+
+Creates batches by randomly shuffling tensors.
+
+This function adds the following to the current `Graph`:
+
+* A shuffling queue into which tensors from `tensors` are enqueued.
+* A `dequeue_many` operation to create batches from the queue.
+* A `QueueRunner` to `QUEUE_RUNNER` collection, to enqueue the tensors
+  from `tensors`.
+
+If `enqueue_many` is `False`, `tensors` is assumed to represent a
+single example.  An input tensor with shape `[x, y, z]` will be output
+as a tensor with shape `[batch_size, x, y, z]`.
+
+If `enqueue_many` is `True`, `tensors` is assumed to represent a
+batch of examples, where the first dimension is indexed by example,
+and all members of `tensors` should have the same size in the
+first dimension.  If an input tensor has shape `[*, x, y, z]`, the
+output will have shape `[batch_size, x, y, z]`.
+
+The `capacity` argument controls the how long the prefetching is allowed to
+grow the queues.
+
+The returned operation is a dequeue operation and will throw
+`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
+operation is feeding another input queue, its queue runner will catch
+this exception, however, if this operation is used in your main thread
+you are responsible for catching this yourself.
+
+For example:
+
+```python
+# Creates batches of 32 images and 32 labels.
+image_batch, label_batch = tf.train.shuffle_batch(
+      [single_image, single_label],
+      batch_size=32,
+      num_threads=4,
+      capacity=50000,
+      min_after_dequeue=10000)
+```
+
+*N.B.:* You must ensure that either (i) the `shapes` argument is
+passed, or (ii) all of the tensors in `tensors` must have
+fully-defined shapes. `ValueError` will be raised if neither of
+these conditions holds.
+
+##### Args:
+
+
+*  <b>`tensors`</b>: The list or dictionary of tensors to enqueue.
+*  <b>`batch_size`</b>: The new batch size pulled from the queue.
+*  <b>`capacity`</b>: An integer. The maximum number of elements in the queue.
+*  <b>`min_after_dequeue`</b>: Minimum number elements in the queue after a
+    dequeue, used to ensure a level of mixing of elements.
+*  <b>`num_threads`</b>: The number of threads enqueuing `tensor_list`.
+*  <b>`seed`</b>: Seed for the random shuffling within the queue.
+*  <b>`enqueue_many`</b>: Whether each tensor in `tensor_list` is a single example.
+*  <b>`shapes`</b>: (Optional) The shapes for each example.  Defaults to the
+    inferred shapes for `tensor_list`.
+*  <b>`shared_name`</b>: (Optional) If set, this queue will be shared under the given
+    name across multiple sessions.
+*  <b>`name`</b>: (Optional) A name for the operations.
+
+##### Returns:
+
+  A list or dictionary of tensors with the types as `tensors`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be
+    inferred from the elements of `tensors`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.summary_iterator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.summary_iterator.md
new file mode 100644
index 0000000000..5702571441
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.train.summary_iterator.md
@@ -0,0 +1,42 @@
+### `tf.train.summary_iterator(path)` {#summary_iterator}
+
+An iterator for reading `Event` protocol buffers from an event file.
+
+You can use this function to read events written to an event file. It returns
+a Python iterator that yields `Event` protocol buffers.
+
+Example: Print the contents of an events file.
+
+```python
+for e in tf.train.summary_iterator(path to events file):
+    print(e)
+```
+
+Example: Print selected summary values.
+
+```python
+# This example supposes that the events file contains summaries with a
+# summary value tag 'loss'.  These could have been added by calling
+# `add_summary()`, passing the output of a scalar summary op created with
+# with: `tf.scalar_summary(['loss'], loss_tensor)`.
+for e in tf.train.summary_iterator(path to events file):
+    for v in e.summary.value:
+        if v.tag == 'loss':
+            print(v.simple_value)
+```
+
+See the protocol buffer definitions of
+[Event](https://www.tensorflow.org/code/tensorflow/core/util/event.proto)
+and
+[Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
+for more information about their attributes.
+
+##### Args:
+
+
+*  <b>`path`</b>: The path to an event file created by a `SummaryWriter`.
+
+##### Yields:
+
+  `Event` protocol buffers.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.trainable_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.trainable_variables.md
new file mode 100644
index 0000000000..894d64a2b4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.trainable_variables.md
@@ -0,0 +1,13 @@
+### `tf.trainable_variables()` {#trainable_variables}
+
+Returns all variables created with `trainable=True`.
+
+When passed `trainable=True`, the `Variable()` constructor automatically
+adds new variables to the graph collection
+`GraphKeys.TRAINABLE_VARIABLES`. This convenience function returns the
+contents of that collection.
+
+##### Returns:
+
+  A list of Variable objects.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.transpose.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.transpose.md
deleted file mode 100644
index c6b76c7824..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.transpose.md
+++ /dev/null
@@ -1,49 +0,0 @@
-### `tf.transpose(a, perm=None, name='transpose')` {#transpose}
-
-Transposes `a`. Permutes the dimensions according to `perm`.
-
-The returned tensor's dimension i will correspond to the input dimension
-`perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
-the rank of the input tensor. Hence by default, this operation performs a
-regular matrix transpose on 2-D input Tensors.
-
-For example:
-
-```python
-# 'x' is [[1 2 3]
-#         [4 5 6]]
-tf.transpose(x) ==> [[1 4]
-                     [2 5]
-                     [3 6]]
-
-# Equivalently
-tf.transpose(x, perm=[1, 0]) ==> [[1 4]
-                                  [2 5]
-                                  [3 6]]
-
-# 'perm' is more useful for n-dimensional tensors, for n > 2
-# 'x' is   [[[1  2  3]
-#            [4  5  6]]
-#           [[7  8  9]
-#            [10 11 12]]]
-# Take the transpose of the matrices in dimension-0
-tf.transpose(x, perm=[0, 2, 1]) ==> [[[1  4]
-                                      [2  5]
-                                      [3  6]]
-
-                                     [[7 10]
-                                      [8 11]
-                                      [9 12]]]
-```
-
-##### Args:
-
-
-*  <b>`a`</b>: A `Tensor`.
-*  <b>`perm`</b>: A permutation of the dimensions of `a`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A transposed `Tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.variable_axis_size_partitioner.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.variable_axis_size_partitioner.md
deleted file mode 100644
index 5d8822e83c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.variable_axis_size_partitioner.md
+++ /dev/null
@@ -1,37 +0,0 @@
-### `tf.variable_axis_size_partitioner(max_shard_bytes, axis=0, bytes_per_string_element=16, max_shards=None)` {#variable_axis_size_partitioner}
-
-Get a partitioner for VariableScope to keep shards below `max_shard_bytes`.
-
-This partitioner will shard a Variable along one axis, attempting to keep
-the maximum shard size below `max_shard_bytes`.  In practice, this is not
-always possible when sharding along only one axis.  When this happens,
-this axis is sharded as much as possible (i.e., every dimension becomes
-a separate shard).
-
-If the partitioner hits the `max_shards` limit, then each shard may end up
-larger than `max_shard_bytes`. By default `max_shards` equals `None` and no
-limit on the number of shards is enforced.
-
-One reasonable value for `max_shard_bytes` is `(64 << 20) - 1`, or almost
-`64MB`, to keep below the protobuf byte limit.
-
-##### Args:
-
-
-*  <b>`max_shard_bytes`</b>: The maximum size any given shard is allowed to be.
-*  <b>`axis`</b>: The axis to partition along.  Default: outermost axis.
-*  <b>`bytes_per_string_element`</b>: If the `Variable` is of type string, this provides
-    an estimate of how large each scalar in the `Variable` is.
-*  <b>`max_shards`</b>: The maximum number of shards in int created taking precedence
-    over `max_shard_bytes`.
-
-##### Returns:
-
-  A partition function usable as the `partitioner` argument to
-  `variable_scope`, `get_variable`, and `get_partitioned_variable_list`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If any of the byte counts are non-positive.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.Dimension.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.Dimension.md
deleted file mode 100644
index f149b6cb65..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.Dimension.md
+++ /dev/null
@@ -1,83 +0,0 @@
-Represents the value of one dimension in a TensorShape.
-- - -
-
-#### `tf.Dimension.__init__(value)` {#Dimension.__init__}
-
-Creates a new Dimension with the given value.
-
-
-- - -
-
-#### `tf.Dimension.assert_is_compatible_with(other)` {#Dimension.assert_is_compatible_with}
-
-Raises an exception if `other` is not compatible with this Dimension.
-
-##### Args:
-
-
-*  <b>`other`</b>: Another Dimension.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `self` and `other` are not compatible (see
-    is_compatible_with).
-
-
-- - -
-
-#### `tf.Dimension.is_compatible_with(other)` {#Dimension.is_compatible_with}
-
-Returns true if `other` is compatible with this Dimension.
-
-Two known Dimensions are compatible if they have the same value.
-An unknown Dimension is compatible with all other Dimensions.
-
-##### Args:
-
-
-*  <b>`other`</b>: Another Dimension.
-
-##### Returns:
-
-  True if this Dimension and `other` are compatible.
-
-
-- - -
-
-#### `tf.Dimension.merge_with(other)` {#Dimension.merge_with}
-
-Returns a Dimension that combines the information in `self` and `other`.
-
-Dimensions are combined as follows:
-
-    Dimension(n)   .merge_with(Dimension(n))    == Dimension(n)
-    Dimension(n)   .merge_with(Dimension(None)) == Dimension(n)
-    Dimension(None).merge_with(Dimension(n))    == Dimension(n)
-    Dimension(None).merge_with(Dimension(None)) == Dimension(None)
-    Dimension(n)   .merge_with(Dimension(m)) raises ValueError for n != m
-
-##### Args:
-
-
-*  <b>`other`</b>: Another Dimension.
-
-##### Returns:
-
-  A Dimension containing the combined information of `self` and
-  `other`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `self` and `other` are not compatible (see
-    is_compatible_with).
-
-
-- - -
-
-#### `tf.Dimension.value` {#Dimension.value}
-
-The value of this dimension, or None if it is unknown.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.FixedLenSequenceFeature.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.FixedLenSequenceFeature.md
deleted file mode 100644
index 607b81a9bf..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.FixedLenSequenceFeature.md
+++ /dev/null
@@ -1,31 +0,0 @@
-Configuration for a dense input feature in a sequence item.
-
-To treat a sparse input as dense, provide `allow_missing=True`; otherwise,
-the parse functions will fail on any examples missing this feature.
-
-Fields:
-  shape: Shape of input data.
-  dtype: Data type of input.
-  allow_missing: Whether to allow this feature to be missing from a feature
-    list item.
-- - -
-
-#### `tf.FixedLenSequenceFeature.allow_missing` {#FixedLenSequenceFeature.allow_missing}
-
-Alias for field number 2
-
-
-- - -
-
-#### `tf.FixedLenSequenceFeature.dtype` {#FixedLenSequenceFeature.dtype}
-
-Alias for field number 1
-
-
-- - -
-
-#### `tf.FixedLenSequenceFeature.shape` {#FixedLenSequenceFeature.shape}
-
-Alias for field number 0
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.Graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.Graph.md
deleted file mode 100644
index 762a117664..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.Graph.md
+++ /dev/null
@@ -1,783 +0,0 @@
-A TensorFlow computation, represented as a dataflow graph.
-
-A `Graph` contains a set of
-[`Operation`](../../api_docs/python/framework.md#Operation) objects,
-which represent units of computation; and
-[`Tensor`](../../api_docs/python/framework.md#Tensor) objects, which represent
-the units of data that flow between operations.
-
-A default `Graph` is always registered, and accessible by calling
-[`tf.get_default_graph()`](../../api_docs/python/framework.md#get_default_graph).
-To add an operation to the default graph, simply call one of the functions
-that defines a new `Operation`:
-
-```
-c = tf.constant(4.0)
-assert c.graph is tf.get_default_graph()
-```
-
-Another typical usage involves the
-[`Graph.as_default()`](../../api_docs/python/framework.md#Graph.as_default)
-context manager, which overrides the current default graph for the
-lifetime of the context:
-
-```python
-g = tf.Graph()
-with g.as_default():
-  # Define operations and tensors in `g`.
-  c = tf.constant(30.0)
-  assert c.graph is g
-```
-
-Important note: This class *is not* thread-safe for graph construction. All
-operations should be created from a single thread, or external
-synchronization must be provided. Unless otherwise specified, all methods
-are not thread-safe.
-
-- - -
-
-#### `tf.Graph.__init__()` {#Graph.__init__}
-
-Creates a new, empty Graph.
-
-
-- - -
-
-#### `tf.Graph.as_default()` {#Graph.as_default}
-
-Returns a context manager that makes this `Graph` the default graph.
-
-This method should be used if you want to create multiple graphs
-in the same process. For convenience, a global default graph is
-provided, and all ops will be added to this graph if you do not
-create a new graph explicitly. Use this method with the `with` keyword
-to specify that ops created within the scope of a block should be
-added to this graph.
-
-The default graph is a property of the current thread. If you
-create a new thread, and wish to use the default graph in that
-thread, you must explicitly add a `with g.as_default():` in that
-thread's function.
-
-The following code examples are equivalent:
-
-```python
-# 1. Using Graph.as_default():
-g = tf.Graph()
-with g.as_default():
-  c = tf.constant(5.0)
-  assert c.graph is g
-
-# 2. Constructing and making default:
-with tf.Graph().as_default() as g:
-  c = tf.constant(5.0)
-  assert c.graph is g
-```
-
-##### Returns:
-
-  A context manager for using this graph as the default graph.
-
-
-- - -
-
-#### `tf.Graph.as_graph_def(from_version=None, add_shapes=False)` {#Graph.as_graph_def}
-
-Returns a serialized `GraphDef` representation of this graph.
-
-The serialized `GraphDef` can be imported into another `Graph`
-(using [`import_graph_def()`](#import_graph_def)) or used with the
-[C++ Session API](../../api_docs/cc/index.md).
-
-This method is thread-safe.
-
-##### Args:
-
-
-*  <b>`from_version`</b>: Optional.  If this is set, returns a `GraphDef`
-    containing only the nodes that were added to this graph since
-    its `version` property had the given value.
-*  <b>`add_shapes`</b>: If true, adds an "_output_shapes" list attr to each
-    node with the inferred shapes of each of its outputs.
-
-##### Returns:
-
-  A [`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
-  protocol buffer.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the `graph_def` would be too large.
-
-
-- - -
-
-#### `tf.Graph.finalize()` {#Graph.finalize}
-
-Finalizes this graph, making it read-only.
-
-After calling `g.finalize()`, no new operations can be added to
-`g`.  This method is used to ensure that no operations are added
-to a graph when it is shared between multiple threads, for example
-when using a [`QueueRunner`](../../api_docs/python/train.md#QueueRunner).
-
-
-- - -
-
-#### `tf.Graph.finalized` {#Graph.finalized}
-
-True if this graph has been finalized.
-
-
-
-- - -
-
-#### `tf.Graph.control_dependencies(control_inputs)` {#Graph.control_dependencies}
-
-Returns a context manager that specifies control dependencies.
-
-Use with the `with` keyword to specify that all operations constructed
-within the context should have control dependencies on
-`control_inputs`. For example:
-
-```python
-with g.control_dependencies([a, b, c]):
-  # `d` and `e` will only run after `a`, `b`, and `c` have executed.
-  d = ...
-  e = ...
-```
-
-Multiple calls to `control_dependencies()` can be nested, and in
-that case a new `Operation` will have control dependencies on the union
-of `control_inputs` from all active contexts.
-
-```python
-with g.control_dependencies([a, b]):
-  # Ops constructed here run after `a` and `b`.
-  with g.control_dependencies([c, d]):
-    # Ops constructed here run after `a`, `b`, `c`, and `d`.
-```
-
-You can pass None to clear the control dependencies:
-
-```python
-with g.control_dependencies([a, b]):
-  # Ops constructed here run after `a` and `b`.
-  with g.control_dependencies(None):
-    # Ops constructed here run normally, not waiting for either `a` or `b`.
-    with g.control_dependencies([c, d]):
-      # Ops constructed here run after `c` and `d`, also not waiting
-      # for either `a` or `b`.
-```
-
-*N.B.* The control dependencies context applies *only* to ops that
-are constructed within the context. Merely using an op or tensor
-in the context does not add a control dependency. The following
-example illustrates this point:
-
-```python
-# WRONG
-def my_func(pred, tensor):
-  t = tf.matmul(tensor, tensor)
-  with tf.control_dependencies([pred]):
-    # The matmul op is created outside the context, so no control
-    # dependency will be added.
-    return t
-
-# RIGHT
-def my_func(pred, tensor):
-  with tf.control_dependencies([pred]):
-    # The matmul op is created in the context, so a control dependency
-    # will be added.
-    return tf.matmul(tensor, tensor)
-```
-
-##### Args:
-
-
-*  <b>`control_inputs`</b>: A list of `Operation` or `Tensor` objects which
-    must be executed or computed before running the operations
-    defined in the context.  Can also be `None` to clear the control
-    dependencies.
-
-##### Returns:
-
- A context manager that specifies control dependencies for all
- operations constructed within the context.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `control_inputs` is not a list of `Operation` or
-    `Tensor` objects.
-
-
-- - -
-
-#### `tf.Graph.device(device_name_or_function)` {#Graph.device}
-
-Returns a context manager that specifies the default device to use.
-
-The `device_name_or_function` argument may either be a device name
-string, a device function, or None:
-
-* If it is a device name string, all operations constructed in
-  this context will be assigned to the device with that name, unless
-  overridden by a nested `device()` context.
-* If it is a function, it will be treated as a function from
-  Operation objects to device name strings, and invoked each time
-  a new Operation is created. The Operation will be assigned to
-  the device with the returned name.
-* If it is None, all `device()` invocations from the enclosing context
-  will be ignored.
-
-For information about the valid syntax of device name strings, see
-the documentation in
-[`DeviceNameUtils`](https://www.tensorflow.org/code/tensorflow/core/util/device_name_utils.h).
-
-For example:
-
-```python
-with g.device('/gpu:0'):
-  # All operations constructed in this context will be placed
-  # on GPU 0.
-  with g.device(None):
-    # All operations constructed in this context will have no
-    # assigned device.
-
-# Defines a function from `Operation` to device string.
-def matmul_on_gpu(n):
-  if n.type == "MatMul":
-    return "/gpu:0"
-  else:
-    return "/cpu:0"
-
-with g.device(matmul_on_gpu):
-  # All operations of type "MatMul" constructed in this context
-  # will be placed on GPU 0; all other operations will be placed
-  # on CPU 0.
-```
-
-**N.B.** The device scope may be overridden by op wrappers or
-other library code. For example, a variable assignment op
-`v.assign()` must be colocated with the `tf.Variable` `v`, and
-incompatible device scopes will be ignored.
-
-##### Args:
-
-
-*  <b>`device_name_or_function`</b>: The device name or function to use in
-    the context.
-
-##### Returns:
-
-  A context manager that specifies the default device to use for newly
-  created ops.
-
-
-- - -
-
-#### `tf.Graph.name_scope(name)` {#Graph.name_scope}
-
-Returns a context manager that creates hierarchical names for operations.
-
-A graph maintains a stack of name scopes. A `with name_scope(...):`
-statement pushes a new name onto the stack for the lifetime of the context.
-
-The `name` argument will be interpreted as follows:
-
-* A string (not ending with '/') will create a new name scope, in which
-  `name` is appended to the prefix of all operations created in the
-  context. If `name` has been used before, it will be made unique by
-  calling `self.unique_name(name)`.
-* A scope previously captured from a `with g.name_scope(...) as
-  scope:` statement will be treated as an "absolute" name scope, which
-  makes it possible to re-enter existing scopes.
-* A value of `None` or the empty string will reset the current name scope
-  to the top-level (empty) name scope.
-
-For example:
-
-```python
-with tf.Graph().as_default() as g:
-  c = tf.constant(5.0, name="c")
-  assert c.op.name == "c"
-  c_1 = tf.constant(6.0, name="c")
-  assert c_1.op.name == "c_1"
-
-  # Creates a scope called "nested"
-  with g.name_scope("nested") as scope:
-    nested_c = tf.constant(10.0, name="c")
-    assert nested_c.op.name == "nested/c"
-
-    # Creates a nested scope called "inner".
-    with g.name_scope("inner"):
-      nested_inner_c = tf.constant(20.0, name="c")
-      assert nested_inner_c.op.name == "nested/inner/c"
-
-    # Create a nested scope called "inner_1".
-    with g.name_scope("inner"):
-      nested_inner_1_c = tf.constant(30.0, name="c")
-      assert nested_inner_1_c.op.name == "nested/inner_1/c"
-
-      # Treats `scope` as an absolute name scope, and
-      # switches to the "nested/" scope.
-      with g.name_scope(scope):
-        nested_d = tf.constant(40.0, name="d")
-        assert nested_d.op.name == "nested/d"
-
-        with g.name_scope(""):
-          e = tf.constant(50.0, name="e")
-          assert e.op.name == "e"
-```
-
-The name of the scope itself can be captured by `with
-g.name_scope(...) as scope:`, which stores the name of the scope
-in the variable `scope`. This value can be used to name an
-operation that represents the overall result of executing the ops
-in a scope. For example:
-
-```python
-inputs = tf.constant(...)
-with g.name_scope('my_layer') as scope:
-  weights = tf.Variable(..., name="weights")
-  biases = tf.Variable(..., name="biases")
-  affine = tf.matmul(inputs, weights) + biases
-  output = tf.nn.relu(affine, name=scope)
-```
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the scope.
-
-##### Returns:
-
-  A context manager that installs `name` as a new name scope.
-
-
-
-A `Graph` instance supports an arbitrary number of "collections"
-that are identified by name. For convenience when building a large
-graph, collections can store groups of related objects: for
-example, the `tf.Variable` uses a collection (named
-[`tf.GraphKeys.VARIABLES`](../../api_docs/python/framework.md#GraphKeys)) for
-all variables that are created during the construction of a graph. The caller
-may define additional collections by specifying a new name.
-
-- - -
-
-#### `tf.Graph.add_to_collection(name, value)` {#Graph.add_to_collection}
-
-Stores `value` in the collection with the given `name`.
-
-Note that collections are not sets, so it is possible to add a value to
-a collection several times.
-
-##### Args:
-
-
-*  <b>`name`</b>: The key for the collection. The `GraphKeys` class
-    contains many standard names for collections.
-*  <b>`value`</b>: The value to add to the collection.
-
-
-- - -
-
-#### `tf.Graph.add_to_collections(names, value)` {#Graph.add_to_collections}
-
-Stores `value` in the collections given by `names`.
-
-Note that collections are not sets, so it is possible to add a value to
-a collection several times. This function makes sure that duplicates in
-`names` are ignored, but it will not check for pre-existing membership of
-`value` in any of the collections in `names`.
-
-`names` can be any iterable, but if `names` is a string, it is treated as a
-single collection name.
-
-##### Args:
-
-
-*  <b>`names`</b>: The keys for the collections to add to. The `GraphKeys` class
-    contains many standard names for collections.
-*  <b>`value`</b>: The value to add to the collections.
-
-
-- - -
-
-#### `tf.Graph.get_collection(name, scope=None)` {#Graph.get_collection}
-
-Returns a list of values in the collection with the given `name`.
-
-This is different from `get_collection_ref()` which always returns the
-actual collection list if it exists in that it returns a new list each time
-it is called.
-
-##### Args:
-
-
-*  <b>`name`</b>: The key for the collection. For example, the `GraphKeys` class
-    contains many standard names for collections.
-*  <b>`scope`</b>: (Optional.) If supplied, the resulting list is filtered to include
-    only items whose `name` attribute matches using `re.match`. Items
-    without a `name` attribute are never returned if a scope is supplied and
-    the choice or `re.match` means that a `scope` without special tokens
-    filters by prefix.
-
-##### Returns:
-
-  The list of values in the collection with the given `name`, or
-  an empty list if no value has been added to that collection. The
-  list contains the values in the order under which they were
-  collected.
-
-
-- - -
-
-#### `tf.Graph.get_collection_ref(name)` {#Graph.get_collection_ref}
-
-Returns a list of values in the collection with the given `name`.
-
-If the collection exists, this returns the list itself, which can
-be modified in place to change the collection.  If the collection does
-not exist, it is created as an empty list and the list is returned.
-
-This is different from `get_collection()` which always returns a copy of
-the collection list if it exists and never creates an empty collection.
-
-##### Args:
-
-
-*  <b>`name`</b>: The key for the collection. For example, the `GraphKeys` class
-    contains many standard names for collections.
-
-##### Returns:
-
-  The list of values in the collection with the given `name`, or an empty
-  list if no value has been added to that collection.
-
-
-
-- - -
-
-#### `tf.Graph.as_graph_element(obj, allow_tensor=True, allow_operation=True)` {#Graph.as_graph_element}
-
-Returns the object referred to by `obj`, as an `Operation` or `Tensor`.
-
-This function validates that `obj` represents an element of this
-graph, and gives an informative error message if it is not.
-
-This function is the canonical way to get/validate an object of
-one of the allowed types from an external argument reference in the
-Session API.
-
-This method may be called concurrently from multiple threads.
-
-##### Args:
-
-
-*  <b>`obj`</b>: A `Tensor`, an `Operation`, or the name of a tensor or operation.
-    Can also be any object with an `_as_graph_element()` method that returns
-    a value of one of these types.
-*  <b>`allow_tensor`</b>: If true, `obj` may refer to a `Tensor`.
-*  <b>`allow_operation`</b>: If true, `obj` may refer to an `Operation`.
-
-##### Returns:
-
-  The `Tensor` or `Operation` in the Graph corresponding to `obj`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `obj` is not a type we support attempting to convert
-    to types.
-*  <b>`ValueError`</b>: If `obj` is of an appropriate type but invalid. For
-    example, an invalid string.
-*  <b>`KeyError`</b>: If `obj` is not an object in the graph.
-
-
-- - -
-
-#### `tf.Graph.get_operation_by_name(name)` {#Graph.get_operation_by_name}
-
-Returns the `Operation` with the given `name`.
-
-This method may be called concurrently from multiple threads.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name of the `Operation` to return.
-
-##### Returns:
-
-  The `Operation` with the given `name`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `name` is not a string.
-*  <b>`KeyError`</b>: If `name` does not correspond to an operation in this graph.
-
-
-- - -
-
-#### `tf.Graph.get_tensor_by_name(name)` {#Graph.get_tensor_by_name}
-
-Returns the `Tensor` with the given `name`.
-
-This method may be called concurrently from multiple threads.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name of the `Tensor` to return.
-
-##### Returns:
-
-  The `Tensor` with the given `name`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `name` is not a string.
-*  <b>`KeyError`</b>: If `name` does not correspond to a tensor in this graph.
-
-
-- - -
-
-#### `tf.Graph.get_operations()` {#Graph.get_operations}
-
-Return the list of operations in the graph.
-
-You can modify the operations in place, but modifications
-to the list such as inserts/delete have no effect on the
-list of operations known to the graph.
-
-This method may be called concurrently from multiple threads.
-
-##### Returns:
-
-  A list of Operations.
-
-
-
-- - -
-
-#### `tf.Graph.seed` {#Graph.seed}
-
-The graph-level random seed of this graph.
-
-
-- - -
-
-#### `tf.Graph.unique_name(name, mark_as_used=True)` {#Graph.unique_name}
-
-Return a unique operation name for `name`.
-
-Note: You rarely need to call `unique_name()` directly.  Most of
-the time you just need to create `with g.name_scope()` blocks to
-generate structured names.
-
-`unique_name` is used to generate structured names, separated by
-`"/"`, to help identify operations when debugging a graph.
-Operation names are displayed in error messages reported by the
-TensorFlow runtime, and in various visualization tools such as
-TensorBoard.
-
-If `mark_as_used` is set to `True`, which is the default, a new
-unique name is created and marked as in use. If it's set to `False`,
-the unique name is returned without actually being marked as used.
-This is useful when the caller simply wants to know what the name
-to be created will be.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name for an operation.
-*  <b>`mark_as_used`</b>: Whether to mark this name as being used.
-
-##### Returns:
-
-  A string to be passed to `create_op()` that will be used
-  to name the operation being created.
-
-
-- - -
-
-#### `tf.Graph.version` {#Graph.version}
-
-Returns a version number that increases as ops are added to the graph.
-
-Note that this is unrelated to the
-[GraphDef version](#Graph.graph_def_version).
-
-
-- - -
-
-#### `tf.Graph.graph_def_versions` {#Graph.graph_def_versions}
-
-The GraphDef version information of this graph.
-
-For details on the meaning of each version, see [`GraphDef`]
-(https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto).
-
-##### Returns:
-
-  A `VersionDef`.
-
-
-
-- - -
-
-#### `tf.Graph.create_op(op_type, inputs, dtypes, input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True)` {#Graph.create_op}
-
-Creates an `Operation` in this graph.
-
-This is a low-level interface for creating an `Operation`. Most
-programs will not call this method directly, and instead use the
-Python op constructors, such as `tf.constant()`, which add ops to
-the default graph.
-
-##### Args:
-
-
-*  <b>`op_type`</b>: The `Operation` type to create. This corresponds to the
-    `OpDef.name` field for the proto that defines the operation.
-*  <b>`inputs`</b>: A list of `Tensor` objects that will be inputs to the `Operation`.
-*  <b>`dtypes`</b>: A list of `DType` objects that will be the types of the tensors
-    that the operation produces.
-*  <b>`input_types`</b>: (Optional.) A list of `DType`s that will be the types of
-    the tensors that the operation consumes. By default, uses the base
-    `DType` of each input in `inputs`. Operations that expect
-    reference-typed inputs must specify `input_types` explicitly.
-*  <b>`name`</b>: (Optional.) A string name for the operation. If not specified, a
-    name is generated based on `op_type`.
-*  <b>`attrs`</b>: (Optional.) A dictionary where the key is the attribute name (a
-    string) and the value is the respective `attr` attribute of the
-    `NodeDef` proto that will represent the operation (an `AttrValue`
-    proto).
-*  <b>`op_def`</b>: (Optional.) The `OpDef` proto that describes the `op_type` that
-    the operation will have.
-*  <b>`compute_shapes`</b>: (Optional.) If True, shape inference will be performed
-    to compute the shapes of the outputs.
-*  <b>`compute_device`</b>: (Optional.) If True, device functions will be executed
-    to compute the device property of the Operation.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if any of the inputs is not a `Tensor`.
-*  <b>`ValueError`</b>: if colocation conflicts with existing device assignment.
-
-##### Returns:
-
-  An `Operation` object.
-
-
-- - -
-
-#### `tf.Graph.gradient_override_map(op_type_map)` {#Graph.gradient_override_map}
-
-EXPERIMENTAL: A context manager for overriding gradient functions.
-
-This context manager can be used to override the gradient function
-that will be used for ops within the scope of the context.
-
-For example:
-
-```python
-@tf.RegisterGradient("CustomSquare")
-def _custom_square_grad(op, grad):
-  # ...
-
-with tf.Graph().as_default() as g:
-  c = tf.constant(5.0)
-  s_1 = tf.square(c)  # Uses the default gradient for tf.square.
-  with g.gradient_override_map({"Square": "CustomSquare"}):
-    s_2 = tf.square(s_2)  # Uses _custom_square_grad to compute the
-                          # gradient of s_2.
-```
-
-##### Args:
-
-
-*  <b>`op_type_map`</b>: A dictionary mapping op type strings to alternative op
-    type strings.
-
-##### Returns:
-
-  A context manager that sets the alternative op type to be used for one
-  or more ops created in that context.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `op_type_map` is not a dictionary mapping strings to
-    strings.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.Graph.colocate_with(op, ignore_existing=False)` {#Graph.colocate_with}
-
-Returns a context manager that specifies an op to colocate with.
-
-Note: this function is not for public use, only for internal libraries.
-
-For example:
-
-```python
-a = tf.Variable([1.0])
-with g.colocate_with(a):
-  b = tf.constant(1.0)
-  c = tf.add(a, b)
-```
-
-`b` and `c` will always be colocated with `a`, no matter where `a`
-is eventually placed.
-
-##### Args:
-
-
-*  <b>`op`</b>: The op to colocate all created ops with.
-*  <b>`ignore_existing`</b>: If true, only applies colocation of this op within
-    the context, rather than applying all colocation properties
-    on the stack.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if op is None.
-
-##### Yields:
-
-  A context manager that specifies the op with which to colocate
-  newly created ops.
-
-
-- - -
-
-#### `tf.Graph.get_all_collection_keys()` {#Graph.get_all_collection_keys}
-
-Returns a list of collections used in this graph.
-
-
-- - -
-
-#### `tf.Graph.is_feedable(tensor)` {#Graph.is_feedable}
-
-Returns `True` if and only if `tensor` is feedable.
-
-
-- - -
-
-#### `tf.Graph.prevent_feeding(tensor)` {#Graph.prevent_feeding}
-
-Marks the given `tensor` as unfeedable in this graph.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.GraphKeys.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.GraphKeys.md
deleted file mode 100644
index 1d656f4018..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.GraphKeys.md
+++ /dev/null
@@ -1,36 +0,0 @@
-Standard names to use for graph collections.
-
-The standard library uses various well-known names to collect and
-retrieve values associated with a graph. For example, the
-`tf.Optimizer` subclasses default to optimizing the variables
-collected under `tf.GraphKeys.TRAINABLE_VARIABLES` if none is
-specified, but it is also possible to pass an explicit list of
-variables.
-
-The following standard keys are defined:
-
-* `VARIABLES`: the `Variable` objects that comprise a model, and
-  must be saved and restored together. See
-  [`tf.all_variables()`](../../api_docs/python/state_ops.md#all_variables)
-  for more details.
-* `TRAINABLE_VARIABLES`: the subset of `Variable` objects that will
-  be trained by an optimizer. See
-  [`tf.trainable_variables()`](../../api_docs/python/state_ops.md#trainable_variables)
-  for more details.
-* `SUMMARIES`: the summary `Tensor` objects that have been created in the
-  graph. See
-  [`tf.merge_all_summaries()`](../../api_docs/python/train.md#merge_all_summaries)
-  for more details.
-* `QUEUE_RUNNERS`: the `QueueRunner` objects that are used to
-  produce input for a computation. See
-  [`tf.start_queue_runners()`](../../api_docs/python/train.md#start_queue_runners)
-  for more details.
-* `MOVING_AVERAGE_VARIABLES`: the subset of `Variable` objects that will also
-  keep moving averages.  See
-  [`tf.moving_average_variables()`](../../api_docs/python/state_ops.md#moving_average_variables)
-  for more details.
-* `REGULARIZATION_LOSSES`: regularization losses collected during graph
-  construction.
-* `WEIGHTS`: weights inside neural network layers
-* `BIASES`: biases inside neural network layers
-* `ACTIVATIONS`: activations of neural network layers
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.OpError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.OpError.md
new file mode 100644
index 0000000000..c23014ad17
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.OpError.md
@@ -0,0 +1,62 @@
+A generic error that is raised when TensorFlow execution fails.
+
+Whenever possible, the session will raise a more specific subclass
+of `OpError` from the `tf.errors` module.
+
+- - -
+
+#### `tf.OpError.op` {#OpError.op}
+
+The operation that failed, if known.
+
+*N.B.* If the failed op was synthesized at runtime, e.g. a `Send`
+or `Recv` op, there will be no corresponding
+[`Operation`](../../api_docs/python/framework.md#Operation)
+object.  In that case, this will return `None`, and you should
+instead use the [`OpError.node_def`](#OpError.node_def) to
+discover information about the op.
+
+##### Returns:
+
+  The `Operation` that failed, or None.
+
+
+- - -
+
+#### `tf.OpError.node_def` {#OpError.node_def}
+
+The `NodeDef` proto representing the op that failed.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.OpError.__init__(node_def, op, message, error_code)` {#OpError.__init__}
+
+Creates a new `OpError` indicating that a particular op failed.
+
+##### Args:
+
+
+*  <b>`node_def`</b>: The `graph_pb2.NodeDef` proto representing the op that failed,
+    if known; otherwise None.
+*  <b>`op`</b>: The `ops.Operation` that failed, if known; otherwise None.
+*  <b>`message`</b>: The message string describing the failure.
+*  <b>`error_code`</b>: The `error_codes_pb2.Code` describing the error.
+
+
+- - -
+
+#### `tf.OpError.error_code` {#OpError.error_code}
+
+The integer error code that describes the error.
+
+
+- - -
+
+#### `tf.OpError.message` {#OpError.message}
+
+The error message that describes the error.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.Print.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.Print.md
new file mode 100644
index 0000000000..b1ec7c1af0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.Print.md
@@ -0,0 +1,23 @@
+### `tf.Print(input_, data, message=None, first_n=None, summarize=None, name=None)` {#Print}
+
+Prints a list of tensors.
+
+This is an identity op with the side effect of printing `data` when
+evaluating.
+
+##### Args:
+
+
+*  <b>`input_`</b>: A tensor passed through this op.
+*  <b>`data`</b>: A list of tensors to print out when op is evaluated.
+*  <b>`message`</b>: A string, prefix of the error message.
+*  <b>`first_n`</b>: Only log `first_n` number of times. Negative numbers log always;
+           this is the default.
+*  <b>`summarize`</b>: Only print this many entries of each tensor. If None, then a
+             maximum of 3 elements are printed per input tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  Same tensor as `input_`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.SparseTensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.SparseTensor.md
deleted file mode 100644
index a999b3862f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.SparseTensor.md
+++ /dev/null
@@ -1,143 +0,0 @@
-Represents a sparse tensor.
-
-Tensorflow represents a sparse tensor as three separate dense tensors:
-`indices`, `values`, and `shape`.  In Python, the three tensors are
-collected into a `SparseTensor` class for ease of use.  If you have separate
-`indices`, `values`, and `shape` tensors, wrap them in a `SparseTensor`
-object before passing to the ops below.
-
-Concretely, the sparse tensor `SparseTensor(indices, values, shape)` is
-
-* `indices`: A 2-D int64 tensor of shape `[N, ndims]`.
-* `values`: A 1-D tensor of any type and shape `[N]`.
-* `shape`: A 1-D int64 tensor of shape `[ndims]`.
-
-where `N` and `ndims` are the number of values, and number of dimensions in
-the `SparseTensor` respectively.
-
-The corresponding dense tensor satisfies
-
-```python
-dense.shape = shape
-dense[tuple(indices[i])] = values[i]
-```
-
-By convention, `indices` should be sorted in row-major order (or equivalently
-lexicographic order on the tuples `indices[i]`).  This is not enforced when
-`SparseTensor` objects are constructed, but most ops assume correct ordering.
-If the ordering of sparse tensor `st` is wrong, a fixed version can be
-obtained by calling `tf.sparse_reorder(st)`.
-
-Example: The sparse tensor
-
-```python
-SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], shape=[3, 4])
-```
-
-represents the dense tensor
-
-```python
-[[1, 0, 0, 0]
- [0, 0, 2, 0]
- [0, 0, 0, 0]]
-```
-
-- - -
-
-#### `tf.SparseTensor.__init__(indices, values, shape)` {#SparseTensor.__init__}
-
-Creates a `SparseTensor`.
-
-##### Args:
-
-
-*  <b>`indices`</b>: A 2-D int64 tensor of shape `[N, ndims]`.
-*  <b>`values`</b>: A 1-D tensor of any type and shape `[N]`.
-*  <b>`shape`</b>: A 1-D int64 tensor of shape `[ndims]`.
-
-##### Returns:
-
-  A `SparseTensor`
-
-
-- - -
-
-#### `tf.SparseTensor.indices` {#SparseTensor.indices}
-
-The indices of non-zero values in the represented dense tensor.
-
-##### Returns:
-
-  A 2-D Tensor of int64 with shape `[N, ndims]`, where `N` is the
-    number of non-zero values in the tensor, and `ndims` is the rank.
-
-
-- - -
-
-#### `tf.SparseTensor.values` {#SparseTensor.values}
-
-The non-zero values in the represented dense tensor.
-
-##### Returns:
-
-  A 1-D Tensor of any data type.
-
-
-- - -
-
-#### `tf.SparseTensor.dtype` {#SparseTensor.dtype}
-
-The `DType` of elements in this tensor.
-
-
-- - -
-
-#### `tf.SparseTensor.shape` {#SparseTensor.shape}
-
-A 1-D Tensor of int64 representing the shape of the dense tensor.
-
-
-- - -
-
-#### `tf.SparseTensor.graph` {#SparseTensor.graph}
-
-The `Graph` that contains the index, value, and shape tensors.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.SparseTensor.eval(feed_dict=None, session=None)` {#SparseTensor.eval}
-
-Evaluates this sparse tensor in a `Session`.
-
-Calling this method will execute all preceding operations that
-produce the inputs needed for the operation that produces this
-tensor.
-
-*N.B.* Before invoking `SparseTensor.eval()`, its graph must have been
-launched in a session, and either a default session must be
-available, or `session` must be specified explicitly.
-
-##### Args:
-
-
-*  <b>`feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
-    See [`Session.run()`](../../api_docs/python/client.md#Session.run) for a
-    description of the valid feed values.
-*  <b>`session`</b>: (Optional.) The `Session` to be used to evaluate this sparse
-    tensor. If none, the default session will be used.
-
-##### Returns:
-
-  A `SparseTensorValue` object.
-
-
-- - -
-
-#### `tf.SparseTensor.from_value(cls, sparse_tensor_value)` {#SparseTensor.from_value}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.TextLineReader.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.TextLineReader.md
new file mode 100644
index 0000000000..ebb023a2fa
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.TextLineReader.md
@@ -0,0 +1,148 @@
+A Reader that outputs the lines of a file delimited by newlines.
+
+Newlines are stripped from the output.
+See ReaderBase for supported methods.
+- - -
+
+#### `tf.TextLineReader.__init__(skip_header_lines=None, name=None)` {#TextLineReader.__init__}
+
+Create a TextLineReader.
+
+##### Args:
+
+
+*  <b>`skip_header_lines`</b>: An optional int. Defaults to 0.  Number of lines
+    to skip from the beginning of every file.
+*  <b>`name`</b>: A name for the operation (optional).
+
+
+- - -
+
+#### `tf.TextLineReader.num_records_produced(name=None)` {#TextLineReader.num_records_produced}
+
+Returns the number of records this reader has produced.
+
+This is the same as the number of Read executions that have
+succeeded.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.TextLineReader.num_work_units_completed(name=None)` {#TextLineReader.num_work_units_completed}
+
+Returns the number of work units this reader has finished processing.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  An int64 Tensor.
+
+
+- - -
+
+#### `tf.TextLineReader.read(queue, name=None)` {#TextLineReader.read}
+
+Returns the next record (key, value pair) produced by a reader.
+
+Will dequeue a work unit from queue if necessary (e.g. when the
+Reader needs to start reading from a new file since it has
+finished with the previous file).
+
+##### Args:
+
+
+*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
+    to a Queue, with string work items.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of Tensors (key, value).
+
+*  <b>`key`</b>: A string scalar Tensor.
+*  <b>`value`</b>: A string scalar Tensor.
+
+
+- - -
+
+#### `tf.TextLineReader.reader_ref` {#TextLineReader.reader_ref}
+
+Op that implements the reader.
+
+
+- - -
+
+#### `tf.TextLineReader.reset(name=None)` {#TextLineReader.reset}
+
+Restore a reader to its initial clean state.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.TextLineReader.restore_state(state, name=None)` {#TextLineReader.restore_state}
+
+Restore a reader to a previously saved state.
+
+Not all Readers support being restored, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`state`</b>: A string Tensor.
+    Result of a SerializeState of a Reader with matching type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The created Operation.
+
+
+- - -
+
+#### `tf.TextLineReader.serialize_state(name=None)` {#TextLineReader.serialize_state}
+
+Produce a string tensor that encodes the state of a reader.
+
+Not all Readers support being serialized, so this can produce an
+Unimplemented error.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A string Tensor.
+
+
+- - -
+
+#### `tf.TextLineReader.supports_serialize` {#TextLineReader.supports_serialize}
+
+Whether the Reader implementation can serialize its state.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.VarLenFeature.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.VarLenFeature.md
deleted file mode 100644
index a7b49bfcd6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.VarLenFeature.md
+++ /dev/null
@@ -1,11 +0,0 @@
-Configuration for parsing a variable-length input feature.
-
-Fields:
-  dtype: Data type of input.
-- - -
-
-#### `tf.VarLenFeature.dtype` {#VarLenFeature.dtype}
-
-Alias for field number 0
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.add_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.add_n.md
deleted file mode 100644
index c214a46057..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.add_n.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.add_n(inputs, name=None)` {#add_n}
-
-Add all input tensors element wise.
-
-##### Args:
-
-
-*  <b>`inputs`</b>: A list of at least 1 `Tensor` objects of the same type in: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
-    Must all be the same size and shape.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `inputs`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.argmax.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.argmax.md
new file mode 100644
index 0000000000..af0a2270a9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.argmax.md
@@ -0,0 +1,17 @@
+### `tf.argmax(input, dimension, name=None)` {#argmax}
+
+Returns the index with the largest value across dimensions of a tensor.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+*  <b>`dimension`</b>: A `Tensor` of type `int32`.
+    int32, 0 <= dimension < rank(input).  Describes which dimension
+    of the input Tensor to reduce across. For vectors, use dimension = 0.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `int64`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.argmin.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.argmin.md
new file mode 100644
index 0000000000..002d5ed816
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.argmin.md
@@ -0,0 +1,17 @@
+### `tf.argmin(input, dimension, name=None)` {#argmin}
+
+Returns the index with the smallest value across dimensions of a tensor.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+*  <b>`dimension`</b>: A `Tensor` of type `int32`.
+    int32, 0 <= dimension < rank(input).  Describes which dimension
+    of the input Tensor to reduce across. For vectors, use dimension = 0.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `int64`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.assert_positive.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.assert_positive.md
deleted file mode 100644
index 8b727d6215..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.assert_positive.md
+++ /dev/null
@@ -1,33 +0,0 @@
-### `tf.assert_positive(x, data=None, summarize=None, name=None)` {#assert_positive}
-
-Assert the condition `x > 0` holds element-wise.
-
-Example of adding a dependency to an operation:
-
-```python
-with tf.control_dependencies([tf.assert_positive(x)]):
-  output = tf.reduce_sum(x)
-```
-
-Example of adding dependency to the tensor being checked:
-
-```python
-x = tf.with_dependencies([tf.assert_positive(x)], x)
-```
-
-Positive means, for every element `x[i]` of `x`, we have `x[i] > 0`.
-If `x` is empty this is trivially satisfied.
-
-##### Args:
-
-
-*  <b>`x`</b>: Numeric `Tensor`.
-*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
-    error message and first few entries of `x`.
-*  <b>`summarize`</b>: Print this many entries of each tensor.
-*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_positive".
-
-##### Returns:
-
-  Op raising `InvalidArgumentError` unless `x` is all positive.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.assert_proper_iterable.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.assert_proper_iterable.md
new file mode 100644
index 0000000000..ba01073765
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.assert_proper_iterable.md
@@ -0,0 +1,18 @@
+### `tf.assert_proper_iterable(values)` {#assert_proper_iterable}
+
+Static assert that values is a "proper" iterable.
+
+`Ops` that expect iterables of `Tensor` can call this to validate input.
+Useful since `Tensor`, `ndarray`, byte/text type are all iterables themselves.
+
+##### Args:
+
+
+*  <b>`values`</b>: Object to be checked.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `values` is not iterable or is one of
+    `Tensor`, `SparseTensor`, `np.array`, `tf.compat.bytes_or_text_types`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.audio_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.audio_summary.md
new file mode 100644
index 0000000000..a592378b88
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.audio_summary.md
@@ -0,0 +1,35 @@
+### `tf.audio_summary(tag, tensor, sample_rate, max_outputs=3, collections=None, name=None)` {#audio_summary}
+
+Outputs a `Summary` protocol buffer with audio.
+
+The summary has up to `max_outputs` summary values containing audio. The
+audio is built from `tensor` which must be 3-D with shape `[batch_size,
+frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
+assumed to be in the range of `[-1.0, 1.0]` with a sample rate of
+`sample_rate`.
+
+The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
+build the `tag` of the summary values:
+
+*  If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
+*  If `max_outputs` is greater than 1, the summary value tags are
+   generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
+
+##### Args:
+
+
+*  <b>`tag`</b>: A scalar `Tensor` of type `string`. Used to build the `tag`
+    of the summary values.
+*  <b>`tensor`</b>: A 3-D `float32` `Tensor` of shape `[batch_size, frames, channels]`
+    or a 2-D `float32` `Tensor` of shape `[batch_size, frames]`.
+*  <b>`sample_rate`</b>: The sample rate of the signal in hertz.
+*  <b>`max_outputs`</b>: Max number of batch elements to generate audio for.
+*  <b>`collections`</b>: Optional list of ops.GraphKeys.  The collections to add the
+    summary to.  Defaults to [ops.GraphKeys.SUMMARIES]
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A scalar `Tensor` of type `string`. The serialized `Summary` protocol
+  buffer.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_ifft.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_ifft.md
new file mode 100644
index 0000000000..c4b865425b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_ifft.md
@@ -0,0 +1,18 @@
+### `tf.batch_ifft(input, name=None)` {#batch_ifft}
+
+Compute the inverse 1-dimensional discrete Fourier Transform over the inner-most
+
+dimension of `input`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`.
+  A complex64 tensor of the same shape as `input`. The inner-most
+  dimension of `input` is replaced with its inverse 1D Fourier Transform.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_diag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_diag.md
deleted file mode 100644
index 6e5458ba6c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_diag.md
+++ /dev/null
@@ -1,42 +0,0 @@
-### `tf.batch_matrix_diag(diagonal, name=None)` {#batch_matrix_diag}
-
-Returns a batched diagonal tensor with a given batched diagonal values.
-
-Given a `diagonal`, this operation returns a tensor with the `diagonal` and
-everything else padded with zeros. The diagonal is computed as follows:
-
-Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a
-tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where:
-
-`output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.
-
-For example:
-
-```prettyprint
-# 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]
-
-and diagonal.shape = (2, 4)
-
-tf.batch_matrix_diag(diagonal) ==> [[[1, 0, 0, 0]
-                                     [0, 2, 0, 0]
-                                     [0, 0, 3, 0]
-                                     [0, 0, 0, 4]],
-                                    [[5, 0, 0, 0]
-                                     [0, 6, 0, 0]
-                                     [0, 0, 7, 0]
-                                     [0, 0, 0, 8]]]
-
-which has shape (2, 4, 4)
-```
-
-##### Args:
-
-
-*  <b>`diagonal`</b>: A `Tensor`. Rank `k`, where `k >= 1`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `diagonal`.
-  Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_diag_part.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_diag_part.md
new file mode 100644
index 0000000000..0eb431d7a9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_diag_part.md
@@ -0,0 +1,46 @@
+### `tf.batch_matrix_diag_part(input, name=None)` {#batch_matrix_diag_part}
+
+Returns the batched diagonal part of a batched tensor.
+
+This operation returns a tensor with the `diagonal` part
+of the batched `input`. The `diagonal` part is computed as follows:
+
+Assume `input` has `k` dimensions `[I, J, K, ..., N, N]`, then the output is a
+tensor of rank `k - 1` with dimensions `[I, J, K, ..., N]` where:
+
+`diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`.
+
+The input must be at least a matrix.
+
+For example:
+
+```prettyprint
+# 'input' is [[[1, 0, 0, 0]
+               [0, 2, 0, 0]
+               [0, 0, 3, 0]
+               [0, 0, 0, 4]],
+              [[5, 0, 0, 0]
+               [0, 6, 0, 0]
+               [0, 0, 7, 0]
+               [0, 0, 0, 8]]]
+
+and input.shape = (2, 4, 4)
+
+tf.batch_matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]]
+
+which has shape (2, 4)
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`.
+    Rank `k` tensor where `k >= 2` and the last two dimensions are equal.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+  The extracted diagonal(s) having shape
+  `diagonal.shape = input.shape[:-1]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_solve.md
new file mode 100644
index 0000000000..f75ea79bc5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_solve.md
@@ -0,0 +1,27 @@
+### `tf.batch_matrix_solve(matrix, rhs, adjoint=None, name=None)` {#batch_matrix_solve}
+
+Solves systems of linear equations. Checks for invertibility.
+
+Matrix is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+form square matrices. Rhs is a tensor of shape
+`[..., M, K]`. The output is a tensor shape `[..., M, K]`.  If `adjoint` is `False` then each output
+matrix satisfies `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
+If `adjoint` is `True` then each output
+matrix satisfies `adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]`.
+
+##### Args:
+
+
+*  <b>`matrix`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
+    Shape is `[..., M, M]`.
+*  <b>`rhs`</b>: A `Tensor`. Must have the same type as `matrix`.
+    Shape is `[..., M, K]`.
+*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
+    Boolean indicating whether to solve with `matrix` or its (block-wise)
+    adjoint.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `matrix`. Shape is `[..., M, K]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_solve_ls.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_solve_ls.md
deleted file mode 100644
index 2b33669fa2..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_solve_ls.md
+++ /dev/null
@@ -1,56 +0,0 @@
-### `tf.batch_matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None)` {#batch_matrix_solve_ls}
-
-Solves multiple linear least-squares problems.
-
-`matrix` is a tensor of shape `[..., M, N]` whose inner-most 2 dimensions
-form `M`-by-`N` matrices. Rhs is a tensor of shape `[..., M, K]` whose
-inner-most 2 dimensions form `M`-by-`K` matrices.   The computed output is a
-`Tensor` of shape `[..., N, K]` whose inner-most 2 dimensions form `M`-by-`K`
-matrices that solve the equations
-`matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]` in the least squares
-sense.
-
-Below we will use the following notation for each pair of
-matrix and right-hand sides in the batch:
-
-`matrix`=\\(A \in \Re^{m \times n}\\),
-`rhs`=\\(B  \in \Re^{m \times k}\\),
-`output`=\\(X  \in \Re^{n \times k}\\),
-`l2_regularizer`=\\(\lambda\\).
-
-If `fast` is `True`, then the solution is computed by solving the normal
-equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then
-\\(X = (A^T A + \lambda I)^{-1} A^T B\\), which solves the least-squares
-problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k}} ||A Z - B||_F^2 +
-\lambda ||Z||_F^2\\). If \\(m \lt n\\) then `output` is computed as
-\\(X = A^T (A A^T + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is
-the minimum-norm solution to the under-determined linear system, i.e.
-\\(X = \mathrm{argmin}_{Z \in \Re^{n \times k}} ||Z||_F^2 \\), subject to
-\\(A Z = B\\). Notice that the fast path is only numerically stable when
-\\(A\\) is numerically full rank and has a condition number
-\\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach}}}\\) or\\(\lambda\\)
-is sufficiently large.
-
-If `fast` is `False` an algorithm based on the numerically robust complete
-orthogonal decomposition is used. This computes the minimum-norm
-least-squares solution, even when \\(A\\) is rank deficient. This path is
-typically 6-7 times slower than the fast path. If `fast` is `False` then
-`l2_regularizer` is ignored.
-
-##### Args:
-
-
-*  <b>`matrix`</b>: `Tensor` of shape `[..., M, N]`.
-*  <b>`rhs`</b>: `Tensor` of shape `[..., M, K]`.
-*  <b>`l2_regularizer`</b>: 0-D `double` `Tensor`. Ignored if `fast=False`.
-*  <b>`fast`</b>: bool. Defaults to `True`.
-*  <b>`name`</b>: string, optional name of the operation.
-
-##### Returns:
-
-
-*  <b>`output`</b>: `Tensor` of shape `[..., N, K]` whose inner-most 2 dimensions form
-    `M`-by-`K` matrices that solve the equations
-    `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]` in the least
-    squares sense.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_triangular_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_triangular_solve.md
deleted file mode 100644
index 297e19088d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.batch_matrix_triangular_solve.md
+++ /dev/null
@@ -1,39 +0,0 @@
-### `tf.batch_matrix_triangular_solve(matrix, rhs, lower=None, adjoint=None, name=None)` {#batch_matrix_triangular_solve}
-
-Solves systems of linear equations with upper or lower triangular matrices by
-
-backsubstitution.
-
-`matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form
-square matrices. If `lower` is `True` then the strictly upper triangular part
-of each inner-most matrix is assumed to be zero and not accessed.
-If `lower` is False then the strictly lower triangular part of each inner-most
-matrix is assumed to be zero and not accessed.
-`rhs` is a tensor of shape [..., M, K]`.
-
-The output is a tensor of shape `[..., M, K]`. If `adjoint` is `True` then the
-innermost matrices in output` satisfy matrix equations
-`matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
-If `adjoint` is `False` then the strictly then the  innermost matrices in
-`output` satisfy matrix equations
-`adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`.
-
-##### Args:
-
-
-*  <b>`matrix`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
-    Shape is `[..., M, M]`.
-*  <b>`rhs`</b>: A `Tensor`. Must have the same type as `matrix`.
-    Shape is `[..., M, K]`.
-*  <b>`lower`</b>: An optional `bool`. Defaults to `True`.
-    Boolean indicating whether the innermost matrices in `matrix` are
-    lower or upper triangular.
-*  <b>`adjoint`</b>: An optional `bool`. Defaults to `False`.
-    Boolean indicating whether to solve with `matrix` or its (block-wise)
-    adjoint.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `matrix`. Shape is `[..., M, K]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.clip_by_average_norm.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.clip_by_average_norm.md
deleted file mode 100644
index 4598e183d8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.clip_by_average_norm.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.clip_by_average_norm(t, clip_norm, name=None)` {#clip_by_average_norm}
-
-Clips tensor values to a maximum average L2-norm.
-
-Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
-normalizes `t` so that its average L2-norm is less than or equal to
-`clip_norm`. Specifically, if the average L2-norm is already less than or
-equal to `clip_norm`, then `t` is not modified. If the average L2-norm is
-greater than `clip_norm`, then this operation returns a tensor of the same
-type and shape as `t` with its values set to:
-
-`t * clip_norm / l2norm_avg(t)`
-
-In this case, the average L2-norm of the output tensor is `clip_norm`.
-
-This operation is typically used to clip gradients before applying them with
-an optimizer.
-
-##### Args:
-
-
-*  <b>`t`</b>: A `Tensor`.
-*  <b>`clip_norm`</b>: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A clipped `Tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.clip_by_global_norm.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.clip_by_global_norm.md
new file mode 100644
index 0000000000..a40f621bf4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.clip_by_global_norm.md
@@ -0,0 +1,50 @@
+### `tf.clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None)` {#clip_by_global_norm}
+
+Clips values of multiple tensors by the ratio of the sum of their norms.
+
+Given a tuple or list of tensors `t_list`, and a clipping ratio `clip_norm`,
+this operation returns a list of clipped tensors `list_clipped`
+and the global norm (`global_norm`) of all tensors in `t_list`. Optionally,
+if you've already computed the global norm for `t_list`, you can specify
+the global norm with `use_norm`.
+
+To perform the clipping, the values `t_list[i]` are set to:
+
+    t_list[i] * clip_norm / max(global_norm, clip_norm)
+
+where:
+
+    global_norm = sqrt(sum([l2norm(t)**2 for t in t_list]))
+
+If `clip_norm > global_norm` then the entries in `t_list` remain as they are,
+otherwise they're all shrunk by the global ratio.
+
+Any of the entries of `t_list` that are of type `None` are ignored.
+
+This is the correct way to perform gradient clipping (for example, see
+[Pascanu et al., 2012](http://arxiv.org/abs/1211.5063)
+([pdf](http://arxiv.org/pdf/1211.5063.pdf))).
+
+However, it is slower than `clip_by_norm()` because all the parameters must be
+ready before the clipping operation can be performed.
+
+##### Args:
+
+
+*  <b>`t_list`</b>: A tuple or list of mixed `Tensors`, `IndexedSlices`, or None.
+*  <b>`clip_norm`</b>: A 0-D (scalar) `Tensor` > 0. The clipping ratio.
+*  <b>`use_norm`</b>: A 0-D (scalar) `Tensor` of type `float` (optional). The global
+    norm to use. If not provided, `global_norm()` is used to compute the norm.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+
+*  <b>`list_clipped`</b>: A list of `Tensors` of the same type as `list_t`.
+*  <b>`global_norm`</b>: A 0-D (scalar) `Tensor` representing the global norm.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `t_list` is not a sequence.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.clip_by_norm.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.clip_by_norm.md
deleted file mode 100644
index a393375986..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.clip_by_norm.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.clip_by_norm(t, clip_norm, name=None)` {#clip_by_norm}
-
-Clips tensor values to a maximum L2-norm.
-
-Given a tensor `t`, and a maximum clip value `clip_norm`, this operation
-normalizes `t` so that its L2-norm is less than or equal to `clip_norm`.
-Specifically, if the L2-norm is already less than or equal to `clip_norm`,
-then `t` is not modified. If the L2-norm is greater than `clip_norm`, then
-this operation returns a tensor of the same type and shape as `t` with its
-values set to:
-
-`t * clip_norm / l2norm(t)`
-
-In this case, the L2-norm of the output tensor is `clip_norm`.
-
-This operation is typically used to clip gradients before applying them with
-an optimizer.
-
-##### Args:
-
-
-*  <b>`t`</b>: A `Tensor`.
-*  <b>`clip_norm`</b>: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A clipped `Tensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.constant_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.constant_initializer.md
deleted file mode 100644
index 4ac524d708..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.constant_initializer.md
+++ /dev/null
@@ -1,20 +0,0 @@
-### `tf.constant_initializer(value=0.0, dtype=tf.float32)` {#constant_initializer}
-
-Returns an initializer that generates tensors with a single value.
-
-##### Args:
-
-
-*  <b>`value`</b>: A Python scalar. All elements of the initialized variable
-    will be set to this value.
-*  <b>`dtype`</b>: The data type. Only floating point types are supported.
-
-##### Returns:
-
-  An initializer that generates tensors with a single value.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.ContinuousDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.ContinuousDistribution.md
deleted file mode 100644
index e474870cd4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.ContinuousDistribution.md
+++ /dev/null
@@ -1,153 +0,0 @@
-Base class for continuous probability distributions.
-
-`ContinuousDistribution` defines the API for the likelihood functions `pdf`
-and `log_pdf` of continuous probability distributions, and a property
-`is_reparameterized` (returning `True` or `False`) which describes
-whether the samples of this distribution are calculated in a differentiable
-way from a non-parameterized distribution.  For example, the `Normal`
-distribution with parameters `mu` and `sigma` is reparameterized as
-
-```Normal(mu, sigma) = sigma * Normal(0, 1) + mu```
-
-Subclasses must override `pdf` and `log_pdf` but one can call this base
-class's implementation.  They must also override the `is_reparameterized`
-property.
-
-See `BaseDistribution` for more information on the API for probability
-distributions.
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.batch_shape(name=None)` {#ContinuousDistribution.batch_shape}
-
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-The product of the dimensions of the `batch_shape` is the number of
-independent distributions of this kind the instance represents.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `batch_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.cdf(value, name='cdf')` {#ContinuousDistribution.cdf}
-
-Cumulative distribution function.
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.dtype` {#ContinuousDistribution.dtype}
-
-dtype of samples from this distribution.
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.entropy(name=None)` {#ContinuousDistribution.entropy}
-
-Entropy of the distribution in nats.
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.event_shape(name=None)` {#ContinuousDistribution.event_shape}
-
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-##### Args:
-
-
-*  <b>`name`</b>: name to give to the op
-
-##### Returns:
-
-  `Tensor` `event_shape`
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.get_batch_shape()` {#ContinuousDistribution.get_batch_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `batch_shape`. May be only partially defined.
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.get_event_shape()` {#ContinuousDistribution.get_event_shape}
-
-`TensorShape` available at graph construction time.
-
-Same meaning as `event_shape`. May be only partially defined.
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.is_reparameterized` {#ContinuousDistribution.is_reparameterized}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.log_cdf(value, name='log_cdf')` {#ContinuousDistribution.log_cdf}
-
-Log CDF.
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.log_pdf(value, name='log_pdf')` {#ContinuousDistribution.log_pdf}
-
-Log of the probability density function.
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.mean` {#ContinuousDistribution.mean}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.name` {#ContinuousDistribution.name}
-
-Name to prepend to all ops.
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.pdf(value, name='pdf')` {#ContinuousDistribution.pdf}
-
-Probability density function.
-
-
-- - -
-
-#### `tf.contrib.distributions.ContinuousDistribution.sample(n, seed=None, name=None)` {#ContinuousDistribution.sample}
-
-Generate `n` samples.
-
-##### Args:
-
-
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
-
-##### Returns:
-
-
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.DirichletMultinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.DirichletMultinomial.md
deleted file mode 100644
index 1d8cb6a6dd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.DirichletMultinomial.md
+++ /dev/null
@@ -1,185 +0,0 @@
-DirichletMultinomial mixture distribution.
-
-This distribution is parameterized by a vector `alpha` of concentration
-parameters for `k` classes.
-
-#### Mathematical details
-
-The Dirichlet Multinomial is a distribution over k-class count data, meaning
-for each k-tuple of non-negative integer `counts = [c_1,...,c_k]`, we have a
-probability of these draws being made from the distribution.  The distribution
-has hyperparameters `alpha = (alpha_1,...,alpha_k)`, and probability mass
-function (pmf):
-
-```pmf(counts) = C! / (c_1!...c_k!) * Beta(alpha + c) / Beta(alpha)```
-
-where above `C = sum_j c_j`, `N!` is `N` factorial, and
-`Beta(x) = prod_j Gamma(x_j) / Gamma(sum_j x_j)` is the multivariate beta
-function.
-
-This is a mixture distribution in that `N` samples can be produced by:
-  1. Choose class probabilities `p = (p_1,...,p_k) ~ Dir(alpha)`
-  2. Draw integers `m = (m_1,...,m_k) ~ Multinomial(p, N)`
-
-This class provides methods to create indexed batches of Dirichlet
-Multinomial distributions.  If the provided `alpha` is rank 2 or higher, for
-every fixed set of leading dimensions, the last dimension represents one
-single Dirichlet Multinomial distribution.  When calling distribution
-functions (e.g. `dist.pdf(counts)`), `alpha` and `counts` are broadcast to the
-same shape (if possible).  In all cases, the last dimension of alpha/counts
-represents single Dirichlet Multinomial distributions.
-
-#### Examples
-
-```python
-alpha = [1, 2, 3]
-dist = DirichletMultinomial(alpha)
-```
-
-Creates a 3-class distribution, with the 3rd class is most likely to be drawn.
-The distribution functions can be evaluated on counts.
-
-```python
-# counts same shape as alpha.
-counts = [0, 2, 0]
-dist.pdf(counts)  # Shape []
-
-# alpha will be broadcast to [[1, 2, 3], [1, 2, 3]] to match counts.
-counts = [[11, 22, 33], [44, 55, 66]]
-dist.pdf(counts)  # Shape [2]
-
-# alpha will be broadcast to shape [5, 7, 3] to match counts.
-counts = [[...]]  # Shape [5, 7, 3]
-dist.pdf(counts)  # Shape [5, 7]
-```
-
-Creates a 2-batch of 3-class distributions.
-
-```python
-alpha = [[1, 2, 3], [4, 5, 6]]  # Shape [2, 3]
-dist = DirichletMultinomial(alpha)
-
-# counts will be broadcast to [[11, 22, 33], [11, 22, 33]] to match alpha.
-counts = [11, 22, 33]
-dist.pdf(counts)  # Shape [2]
-```
-- - -
-
-#### `tf.contrib.distributions.DirichletMultinomial.__init__(alpha)` {#DirichletMultinomial.__init__}
-
-Initialize a batch of DirichletMultinomial distributions.
-
-##### Args:
-
-
-*  <b>`alpha`</b>: Shape `[N1,..., Nn, k]` positive `float` or `double` tensor with
-    `n >= 0`.  Defines this as a batch of `N1 x ... x Nn` different `k`
-    class Dirichlet multinomial distributions.
-
-
-*  <b>`Examples`</b>: 
-
-```python
-# Define 1-batch of 2-class Dirichlet multinomial distribution,
-# also known as a beta-binomial.
-dist = DirichletMultinomial([1.1, 2.0])
-
-# Define a 2-batch of 3-class distributions.
-dist = DirichletMultinomial([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
-```
-
-
-- - -
-
-#### `tf.contrib.distributions.DirichletMultinomial.alpha` {#DirichletMultinomial.alpha}
-
-Parameters defining this distribution.
-
-
-- - -
-
-#### `tf.contrib.distributions.DirichletMultinomial.cdf(x)` {#DirichletMultinomial.cdf}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.DirichletMultinomial.dtype` {#DirichletMultinomial.dtype}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(x)` {#DirichletMultinomial.log_cdf}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(counts, name=None)` {#DirichletMultinomial.log_pmf}
-
-`Log(P[counts])`, computed for every batch member.
-
-For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
-that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
-distribution, the number of draws falling in class `j` is `c_j`.  Note that
-different sequences of draws can result in the same counts, thus the
-probability includes a combinatorial coefficient.
-
-##### Args:
-
-
-*  <b>`counts`</b>: Non-negative `float`, `double`, or `int` tensor whose shape can
-    be broadcast with `self.alpha`.  For fixed leading dimensions, the last
-    dimension represents counts for the corresponding Dirichlet Multinomial
-    distribution in `self.alpha`.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_pmf".
-
-##### Returns:
-
-  Log probabilities for each record, shape `[N1,...,Nn]`.
-
-
-- - -
-
-#### `tf.contrib.distributions.DirichletMultinomial.mean` {#DirichletMultinomial.mean}
-
-Class means for every batch member.
-
-
-- - -
-
-#### `tf.contrib.distributions.DirichletMultinomial.num_classes` {#DirichletMultinomial.num_classes}
-
-Tensor providing number of classes in each batch member.
-
-
-- - -
-
-#### `tf.contrib.distributions.DirichletMultinomial.pmf(counts, name=None)` {#DirichletMultinomial.pmf}
-
-`P[counts]`, computed for every batch member.
-
-For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
-that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
-distribution, the number of draws falling in class `j` is `c_j`.  Note that
-different sequences of draws can result in the same counts, thus the
-probability includes a combinatorial coefficient.
-
-##### Args:
-
-
-*  <b>`counts`</b>: Non-negative `float`, `double`, or `int` tensor whose shape can
-    be broadcast with `self.alpha`.  For fixed leading dimensions, the last
-    dimension represents counts for the corresponding Dirichlet Multinomial
-    distribution in `self.alpha`.
-*  <b>`name`</b>: Name to give this Op, defaults to "pmf".
-
-##### Returns:
-
-  Probabilities for each record, shape `[N1,...,Nn]`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Exponential.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Exponential.md
new file mode 100644
index 0000000000..62181034b9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Exponential.md
@@ -0,0 +1,260 @@
+The Exponential distribution with rate parameter lam.
+
+The PDF of this distribution is:
+
+```pdf(x) = (lam * e^(-lam * x)), x > 0```
+
+Note that the Exponential distribution is a special case of the Gamma
+distribution, with Exponential(lam) = Gamma(1, lam).
+- - -
+
+#### `tf.contrib.distributions.Exponential.__init__(lam, name='Exponential')` {#Exponential.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.alpha` {#Exponential.alpha}
+
+Shape parameter.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.batch_shape(name='batch_shape')` {#Exponential.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.beta` {#Exponential.beta}
+
+Inverse scale parameter.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.cdf(x, name='cdf')` {#Exponential.cdf}
+
+CDF of observations `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.dtype` {#Exponential.dtype}
+
+dtype of samples from this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.entropy(name='entropy')` {#Exponential.entropy}
+
+The entropy of Gamma distribution(s).
+
+This is defined to be
+
+```
+entropy = alpha - log(beta) + log(Gamma(alpha))
+             + (1-alpha)digamma(alpha)
+```
+
+where digamma(alpha) is the digamma function.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.event_shape(name='event_shape')` {#Exponential.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.get_batch_shape()` {#Exponential.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+  `TensorShape` object.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.get_event_shape()` {#Exponential.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+  `TensorShape` object.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.is_reparameterized` {#Exponential.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.lam` {#Exponential.lam}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.log_cdf(x, name='log_cdf')` {#Exponential.log_cdf}
+
+Log CDF of observations `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.log_pdf(x, name='log_pdf')` {#Exponential.log_pdf}
+
+Log pdf of observations in `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.mean` {#Exponential.mean}
+
+Mean of each batch member.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.name` {#Exponential.name}
+
+Name to prepend to all ops.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.pdf(x, name='pdf')` {#Exponential.pdf}
+
+Pdf of observations in `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: tensor of dtype `dtype`, the PDFs of `x`
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.sample(n, seed=None, name=None)` {#Exponential.sample}
+
+Sample `n` observations from the Exponential Distributions.
+
+##### Args:
+
+
+*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`seed`</b>: Python integer, the random seed.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
+    of the distributions determined by the hyperparameters.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.variance` {#Exponential.variance}
+
+Variance of each batch member.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Gamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Gamma.md
new file mode 100644
index 0000000000..5a7bbea7ae
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Gamma.md
@@ -0,0 +1,284 @@
+The `Gamma` distribution with parameter alpha and beta.
+
+The parameters are the shape and inverse scale parameters alpha, beta.
+
+The PDF of this distribution is:
+
+```pdf(x) = (beta^alpha)(x^(alpha-1))e^(-x*beta)/Gamma(alpha), x > 0```
+
+and the CDF of this distribution is:
+
+```cdf(x) =  GammaInc(alpha, beta * x) / Gamma(alpha), x > 0```
+
+where GammaInc is the incomplete lower Gamma function.
+
+Examples:
+
+```python
+dist = Gamma(alpha=3.0, beta=2.0)
+dist2 = Gamma(alpha=[3.0, 4.0], beta=[2.0, 3.0])
+```
+- - -
+
+#### `tf.contrib.distributions.Gamma.__init__(alpha, beta, name='Gamma')` {#Gamma.__init__}
+
+Construct Gamma distributions with parameters `alpha` and `beta`.
+
+The parameters `alpha` and `beta` must be shaped in a way that supports
+broadcasting (e.g. `alpha + beta` is a valid operation).
+
+##### Args:
+
+
+*  <b>`alpha`</b>: `float` or `double` tensor, the shape params of the
+    distribution(s).
+    alpha must contain only positive values.
+*  <b>`beta`</b>: `float` or `double` tensor, the inverse scale params of the
+    distribution(s).
+    beta must contain only positive values.
+*  <b>`name`</b>: The name to prepend to all ops created by this distribution.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `alpha` and `beta` are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.alpha` {#Gamma.alpha}
+
+Shape parameter.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.batch_shape(name='batch_shape')` {#Gamma.batch_shape}
+
+Batch dimensions of this instance as a 1-D int32 `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `batch_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.beta` {#Gamma.beta}
+
+Inverse scale parameter.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.cdf(x, name='cdf')` {#Gamma.cdf}
+
+CDF of observations `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.dtype` {#Gamma.dtype}
+
+dtype of samples from this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.entropy(name='entropy')` {#Gamma.entropy}
+
+The entropy of Gamma distribution(s).
+
+This is defined to be
+
+```
+entropy = alpha - log(beta) + log(Gamma(alpha))
+             + (1-alpha)digamma(alpha)
+```
+
+where digamma(alpha) is the digamma function.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.event_shape(name='event_shape')` {#Gamma.event_shape}
+
+Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+  `Tensor` `event_shape`
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.get_batch_shape()` {#Gamma.get_batch_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+  `TensorShape` object.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.get_event_shape()` {#Gamma.get_event_shape}
+
+`TensorShape` available at graph construction time.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+  `TensorShape` object.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.is_reparameterized` {#Gamma.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.log_cdf(x, name='log_cdf')` {#Gamma.log_cdf}
+
+Log CDF of observations `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.log_pdf(x, name='log_pdf')` {#Gamma.log_pdf}
+
+Log pdf of observations in `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.mean` {#Gamma.mean}
+
+Mean of each batch member.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.name` {#Gamma.name}
+
+Name to prepend to all ops.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.pdf(x, name='pdf')` {#Gamma.pdf}
+
+Pdf of observations in `x` under these Gamma distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: tensor of dtype `dtype`, the PDFs of `x`
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.sample(n, seed=None, name=None)` {#Gamma.sample}
+
+Generate `n` samples.
+
+##### Args:
+
+
+*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
+      with values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.variance` {#Gamma.variance}
+
+Variance of each batch member.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Normal.md
deleted file mode 100644
index d15dd93a65..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.Normal.md
+++ /dev/null
@@ -1,209 +0,0 @@
-The scalar Normal distribution with mean and stddev parameters mu, sigma.
-
-#### Mathematical details
-
-The PDF of this distribution is:
-
-```f(x) = sqrt(1/(2*pi*sigma^2)) exp(-(x-mu)^2/(2*sigma^2))```
-
-#### Examples
-
-Examples of initialization of one or a batch of distributions.
-
-```python
-# Define a single scalar Normal distribution.
-dist = tf.contrib.distributions.Normal(mu=0, sigma=3)
-
-# Evaluate the cdf at 1, returning a scalar.
-dist.cdf(1)
-
-# Define a batch of two scalar valued Normals.
-# The first has mean 1 and standard deviation 11, the second 2 and 22.
-dist = tf.contrib.distributions.Normal(mu=[1, 2.], sigma=[11, 22.])
-
-# Evaluate the pdf of the first distribution on 0, and the second on 1.5,
-# returning a length two tensor.
-dist.pdf([0, 1.5])
-
-# Get 3 samples, returning a 3 x 2 tensor.
-dist.sample(3)
-```
-
-Arguments are broadcast when possible.
-
-```python
-# Define a batch of two scalar valued Normals.
-# Both have mean 1, but different standard deviations.
-dist = tf.contrib.distributions.Normal(mu=1, sigma=[11, 22.])
-
-# Evaluate the pdf of both distributions on the same point, 3.0,
-# returning a length 2 tensor.
-dist.pdf(3.0)
-```
-- - -
-
-#### `tf.contrib.distributions.Normal.__init__(mu, sigma, name=None)` {#Normal.__init__}
-
-Construct Normal distributions with mean and stddev `mu` and `sigma`.
-
-The parameters `mu` and `sigma` must be shaped in a way that supports
-broadcasting (e.g. `mu + sigma` is a valid operation).
-
-##### Args:
-
-
-*  <b>`mu`</b>: `float` or `double` tensor, the means of the distribution(s).
-*  <b>`sigma`</b>: `float` or `double` tensor, the stddevs of the distribution(s).
-    sigma must contain only positive values.
-*  <b>`name`</b>: The name to give Ops created by the initializer.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if mu and sigma are different dtypes.
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.cdf(x, name=None)` {#Normal.cdf}
-
-CDF of observations in `x` under these Normal distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.dtype` {#Normal.dtype}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.entropy(name=None)` {#Normal.entropy}
-
-The entropy of Normal distribution(s).
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.is_reparameterized` {#Normal.is_reparameterized}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.log_cdf(x, name=None)` {#Normal.log_cdf}
-
-Log CDF of observations `x` under these Normal distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.log_pdf(x, name=None)` {#Normal.log_pdf}
-
-Log pdf of observations in `x` under these Normal distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.mean` {#Normal.mean}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.mu` {#Normal.mu}
-
-
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.pdf(x, name=None)` {#Normal.pdf}
-
-The PDF of observations in `x` under these Normal distribution(s).
-
-##### Args:
-
-
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.sample(n, seed=None, name=None)` {#Normal.sample}
-
-Sample `n` observations from the Normal Distributions.
-
-##### Args:
-
-
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
-
-
-- - -
-
-#### `tf.contrib.distributions.Normal.sigma` {#Normal.sigma}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.normal_conjugates_known_sigma_posterior.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.normal_conjugates_known_sigma_posterior.md
new file mode 100644
index 0000000000..ae8eb00890
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.distributions.normal_conjugates_known_sigma_posterior.md
@@ -0,0 +1,48 @@
+### `tf.contrib.distributions.normal_conjugates_known_sigma_posterior(prior, sigma, s, n)` {#normal_conjugates_known_sigma_posterior}
+
+Posterior Normal distribution with conjugate prior on the mean.
+
+This model assumes that `n` observations (with sum `s`) come from a
+Normal with unknown mean `mu` (described by the Normal `prior`)
+and known variance `sigma^2`.  The "known sigma posterior" is
+the distribution of the unknown `mu`.
+
+Accepts a prior Normal distribution object, having parameters
+`mu0` and `sigma0`, as well as known `sigma` values of the predictive
+distribution(s) (also assumed Normal),
+and statistical estimates `s` (the sum(s) of the observations) and
+`n` (the number(s) of observations).
+
+Returns a posterior (also Normal) distribution object, with parameters
+`(mu', sigma'^2)`, where:
+
+```
+mu ~ N(mu', sigma'^2)
+sigma'^2 = 1/(1/sigma0^2 + n/sigma^2),
+mu' = (mu0/sigma0^2 + s/sigma^2) * sigma'^2.
+```
+
+Distribution parameters from `prior`, as well as `sigma`, `s`, and `n`.
+will broadcast in the case of multidimensional sets of parameters.
+
+##### Args:
+
+
+*  <b>`prior`</b>: `Normal` object of type `dtype`:
+    the prior distribution having parameters `(mu0, sigma0)`.
+*  <b>`sigma`</b>: tensor of type `dtype`, taking values `sigma > 0`.
+    The known stddev parameter(s).
+*  <b>`s`</b>: Tensor of type `dtype`.  The sum(s) of observations.
+*  <b>`n`</b>: Tensor of type `int`.  The number(s) of observations.
+
+##### Returns:
+
+  A new Normal posterior distribution object for the unknown observation
+  mean `mu`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if dtype of `s` does not match `dtype`, or `prior` is not a
+    Normal object.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.ffmpeg.decode_audio.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.ffmpeg.decode_audio.md
new file mode 100644
index 0000000000..31b9cba01f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.ffmpeg.decode_audio.md
@@ -0,0 +1,25 @@
+### `tf.contrib.ffmpeg.decode_audio(contents, file_format=None, samples_per_second=None, channel_count=None)` {#decode_audio}
+
+Create an op that decodes the contents of an audio file.
+
+##### Args:
+
+
+*  <b>`contents`</b>: The binary contents of the audio file to decode. This is a
+      scalar.
+*  <b>`file_format`</b>: A string specifying which format the contents will conform
+      to. This can be mp3, ogg, or wav.
+*  <b>`samples_per_second`</b>: The number of samples per second that is assumed.
+      In some cases, resampling will occur to generate the correct sample
+      rate.
+*  <b>`channel_count`</b>: The number of channels that should be created from the
+      audio contents. If the contents have more than this number, then
+      some channels will be merged or dropped. If contents has fewer than
+      this, then additional channels will be created from the existing ones.
+
+##### Returns:
+
+  A rank 2 tensor that has time along dimension 0 and channels along
+  dimension 1. Dimension 0 will be `samples_per_second * length` wide, and
+  dimension 1 will be `channel_count` wide.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.ModeKeys.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.ModeKeys.md
new file mode 100644
index 0000000000..83e0bd4119
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.ModeKeys.md
@@ -0,0 +1,7 @@
+Standard names for model modes.
+
+The following standard keys are defined:
+
+* `TRAIN`: training mode.
+* `EVAL`: evaluation mode.
+* `INFER`: inference mode.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.extract_pandas_labels.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.extract_pandas_labels.md
deleted file mode 100644
index 521a8560e5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.extract_pandas_labels.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.contrib.learn.extract_pandas_labels(labels)` {#extract_pandas_labels}
-
-Extract data from pandas.DataFrame for labels
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.accuracy.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.accuracy.md
deleted file mode 100644
index f41fb78e31..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.accuracy.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.contrib.metrics.accuracy(predictions, labels, weights=None)` {#accuracy}
-
-Computes the percentage of times that predictions matches labels.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: the predicted values, a `Tensor` whose dtype and shape
-               matches 'labels'.
-*  <b>`labels`</b>: the ground truth values, a `Tensor` of any shape and
-          integer or string dtype.
-*  <b>`weights`</b>: None or `Tensor` of float values to reweight the accuracy.
-
-##### Returns:
-
-  Accuracy `Tensor`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if dtypes don't match or
-              if dtype is not integer or string.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.set_intersection.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.set_intersection.md
new file mode 100644
index 0000000000..bd42f3fa01
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.set_intersection.md
@@ -0,0 +1,23 @@
+### `tf.contrib.metrics.set_intersection(a, b, validate_indices=True)` {#set_intersection}
+
+Compute set intersection of elements in last dimension of `a` and `b`.
+
+All but the last dimension of `a` and `b` must match.
+
+##### Args:
+
+
+*  <b>`a`</b>: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
+      must be sorted in row-major order.
+*  <b>`b`</b>: `Tensor` or `SparseTensor` of the same type as `a`. Must be
+      `SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
+      sorted in row-major order.
+*  <b>`validate_indices`</b>: Whether to validate the order and range of sparse indices
+     in `a` and `b`.
+
+##### Returns:
+
+  A `SparseTensor` with the same rank as `a` and `b`, and all but the last
+  dimension the same. Elements along the last dimension contain the
+  intersections.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.streaming_mean.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.streaming_mean.md
new file mode 100644
index 0000000000..780ecbaa7b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.streaming_mean.md
@@ -0,0 +1,44 @@
+### `tf.contrib.metrics.streaming_mean(values, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean}
+
+Computes the (weighted) mean of the given values.
+
+The `streaming_mean` function creates two local variables, `total` and `count`
+that are used to compute the average of `values`. This average is ultimately
+returned as `mean` which is an idempotent operation that simply divides
+`total` by `count`. To facilitate the estimation of a mean over a stream
+of data, the function creates an `update_op` operation whose behavior is
+dependent on the value of `weights`. If `weights` is None, then `update_op`
+increments `total` with the reduced sum of `values` and increments `count`
+with the number of elements in `values`. If `weights` is not `None`, then
+`update_op` increments `total` with the reduced sum of the product of `values`
+and `weights` and increments `count` with the reduced sum of weights.
+In addition to performing the updates, `update_op` also returns the
+`mean`.
+
+##### Args:
+
+
+*  <b>`values`</b>: A `Tensor` of arbitrary dimensions.
+*  <b>`weights`</b>: An optional set of weights of the same shape as `values`. If
+    `weights` is not None, the function computes a weighted mean.
+*  <b>`metrics_collections`</b>: An optional list of collections that `mean`
+    should be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that `update_op`
+    should be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`mean`</b>: A tensor representing the current mean, the value of `total` divided
+    by `count`.
+*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
+    appropriately and whose value matches `mean_value`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `weights` is not `None` and its shape doesn't match `values`
+    or if either `metrics_collections` or `updates_collections` are not a list
+    or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.streaming_precision.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.streaming_precision.md
new file mode 100644
index 0000000000..77ddaead32
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.streaming_precision.md
@@ -0,0 +1,50 @@
+### `tf.contrib.metrics.streaming_precision(predictions, labels, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_precision}
+
+Computes the precision of the predictions with respect to the labels.
+
+The `streaming_precision` function creates two local variables,
+`true_positives` and `false_positives`, that are used to compute the
+precision. This value is ultimately returned as `precision`, an idempotent
+operation that simply divides `true_positives` by the sum of `true_positives`
+and `false_positives`. To facilitate the calculation of the precision over a
+stream of data, the function creates an `update_op` operation whose behavior
+is dependent on the value of `ignore_mask`. If `ignore_mask` is None, then
+`update_op` increments `true_positives` with the number of elements of
+`predictions` and `labels` that are both `True` and increments
+`false_positives` with the number of elements of `predictions` that are `True`
+whose corresponding `labels` element is `False`. If `ignore_mask` is not
+`None`, then the increments for `true_positives` and `false_positives` are
+only computed using elements of `predictions` and `labels` whose corresponding
+values in `ignore_mask` are `False`. In addition to performing the updates,
+`update_op` also returns the value of `precision`.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: The predicted values, a binary `Tensor` of arbitrary shape.
+*  <b>`labels`</b>: The ground truth values, a binary `Tensor` whose dimensions must
+    match `predictions`.
+*  <b>`ignore_mask`</b>: An optional, binary tensor whose size matches `predictions`.
+*  <b>`metrics_collections`</b>: An optional list of collections that `precision` should
+    be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
+    be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`precision`</b>: Scalar float `Tensor` with the value of `true_positives`
+    divided by the sum of `true_positives` and `false_positives`.
+*  <b>`update_op`</b>: `Operation` that increments `true_positives` and
+    `false_positives` variables appropriately and whose value matches
+    `precision`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the dimensions of `predictions` and `labels` don't match or
+    if `ignore_mask` is not `None` and its shape doesn't match `predictions`
+    or if either `metrics_collections` or `updates_collections` are not a list
+    or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.streaming_recall.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.streaming_recall.md
new file mode 100644
index 0000000000..26308e2f5f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.streaming_recall.md
@@ -0,0 +1,50 @@
+### `tf.contrib.metrics.streaming_recall(predictions, labels, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_recall}
+
+Computes the recall of the predictions with respect to the labels.
+
+The `streaming_recall` function creates two local variables,
+`true_positives` and `false_negatives`, that are used to compute the
+recall. This value is ultimately returned as `recall`, an idempotent
+operation that simply divides `true_positives` by the sum of `true_positives`
+and `false_negatives`. To facilitate the calculation of the recall over a
+stream of data, the function creates an `update_op` operation whose behavior
+is dependent on the value of `ignore_mask`. If `ignore_mask` is None, then
+`update_op` increments `true_positives` with the number of elements of
+`predictions` and `labels` that are both `True` and increments
+`false_negatives` with the number of elements of `predictions` that are
+`False` whose corresponding `labels` element is `False`. If `ignore_mask` is
+not `None`, then the increments for `true_positives` and `false_negatives` are
+only computed using elements of `predictions` and `labels` whose corresponding
+values in `ignore_mask` are `False`. In addition to performing the updates,
+`update_op` also returns the value of `recall`.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: The predicted values, a binary `Tensor` of arbitrary shape.
+*  <b>`labels`</b>: The ground truth values, a binary `Tensor` whose dimensions must
+    match `predictions`.
+*  <b>`ignore_mask`</b>: An optional, binary tensor whose size matches `predictions`.
+*  <b>`metrics_collections`</b>: An optional list of collections that `precision` should
+    be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
+    be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`recall`</b>: Scalar float `Tensor` with the value of `true_positives` divided
+    by the sum of `true_positives` and `false_negatives`.
+*  <b>`update_op`</b>: `Operation` that increments `true_positives` and
+    `false_negatives` variables appropriately and whose value matches
+    `recall`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the dimensions of `predictions` and `labels` don't match or
+    if `ignore_mask` is not `None` and its shape doesn't match `predictions`
+    or if either `metrics_collections` or `updates_collections` are not a list
+    or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.convert_to_tensor_or_indexed_slices.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.convert_to_tensor_or_indexed_slices.md
new file mode 100644
index 0000000000..0c65e8327c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.convert_to_tensor_or_indexed_slices.md
@@ -0,0 +1,27 @@
+### `tf.convert_to_tensor_or_indexed_slices(value, dtype=None, name=None, as_ref=False)` {#convert_to_tensor_or_indexed_slices}
+
+Converts the given object to a `Tensor` or an `IndexedSlices`.
+
+If `value` is an `IndexedSlices` or `SparseTensor` it is returned
+unmodified. Otherwise, it is converted to a `Tensor` using
+`convert_to_tensor()`.
+
+##### Args:
+
+
+*  <b>`value`</b>: An `IndexedSlices`, `SparseTensor`, or an object that can be consumed
+    by `convert_to_tensor()`.
+*  <b>`dtype`</b>: (Optional.) The required `DType` of the returned `Tensor` or
+    `IndexedSlices`.
+*  <b>`name`</b>: (Optional.) A name to use if a new `Tensor` is created.
+*  <b>`as_ref`</b>: True if the caller wants the results as ref tensors.
+
+##### Returns:
+
+  An `Tensor`, `IndexedSlices`, or `SparseTensor` based on `value`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `dtype` does not match the element type of `value`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.cos.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.cos.md
new file mode 100644
index 0000000000..b4f6f89933
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.cos.md
@@ -0,0 +1,14 @@
+### `tf.cos(x, name=None)` {#cos}
+
+Computes cos of x element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.count_up_to.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.count_up_to.md
new file mode 100644
index 0000000000..da86e52f07
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.count_up_to.md
@@ -0,0 +1,23 @@
+### `tf.count_up_to(ref, limit, name=None)` {#count_up_to}
+
+Increments 'ref' until it reaches 'limit'.
+
+This operation outputs "ref" after the update is done.  This makes it
+easier to chain operations that need to use the updated value.
+
+##### Args:
+
+
+*  <b>`ref`</b>: A mutable `Tensor`. Must be one of the following types: `int32`, `int64`.
+    Should be from a scalar `Variable` node.
+*  <b>`limit`</b>: An `int`.
+    If incrementing ref would bring it above limit, instead generates an
+    'OutOfRange' error.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `ref`.
+  A copy of the input before increment. If nothing else modifies the
+  input, the values produced will all be distinct.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.cross.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.cross.md
deleted file mode 100644
index eecf2e869b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.cross.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.cross(a, b, name=None)` {#cross}
-
-Compute the pairwise cross product.
-
-`a` and `b` must be the same shape; they can either be simple 3-element vectors,
-or any shape where the innermost dimension is 3. In the latter case, each pair
-of corresponding 3-element vectors is cross-multiplied independently.
-
-##### Args:
-
-
-*  <b>`a`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-    A tensor containing 3-element vectors.
-*  <b>`b`</b>: A `Tensor`. Must have the same type as `a`.
-    Another tensor, of same type and shape as `a`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `a`.
-  Pairwise cross product of the vectors in `a` and `b`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.decode_raw.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.decode_raw.md
deleted file mode 100644
index 125c15d9a8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.decode_raw.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.decode_raw(bytes, out_type, little_endian=None, name=None)` {#decode_raw}
-
-Reinterpret the bytes of a string as a vector of numbers.
-
-##### Args:
-
-
-*  <b>`bytes`</b>: A `Tensor` of type `string`.
-    All the elements must have the same length.
-*  <b>`out_type`</b>: A `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.int64`.
-*  <b>`little_endian`</b>: An optional `bool`. Defaults to `True`.
-    Whether the input `bytes` are in little-endian order.
-    Ignored for `out_type` values that are stored in a single byte like
-    `uint8`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `out_type`.
-  A Tensor with one more dimension than the input `bytes`.  The
-  added dimension will have size equal to the length of the elements
-  of `bytes` divided by the number of bytes to represent `out_type`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.diag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.diag.md
deleted file mode 100644
index 94eb6a6717..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.diag.md
+++ /dev/null
@@ -1,33 +0,0 @@
-### `tf.diag(diagonal, name=None)` {#diag}
-
-Returns a diagonal tensor with a given diagonal values.
-
-Given a `diagonal`, this operation returns a tensor with the `diagonal` and
-everything else padded with zeros. The diagonal is computed as follows:
-
-Assume `diagonal` has dimensions [D1,..., Dk], then the output is a tensor of
-rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where:
-
-`output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik]` and 0 everywhere else.
-
-For example:
-
-```prettyprint
-# 'diagonal' is [1, 2, 3, 4]
-tf.diag(diagonal) ==> [[1, 0, 0, 0]
-                       [0, 2, 0, 0]
-                       [0, 0, 3, 0]
-                       [0, 0, 0, 4]]
-```
-
-##### Args:
-
-
-*  <b>`diagonal`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `complex64`.
-    Rank k tensor where k is at most 3.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `diagonal`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.errors.AbortedError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.errors.AbortedError.md
deleted file mode 100644
index f2bc775dcb..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.errors.AbortedError.md
+++ /dev/null
@@ -1,15 +0,0 @@
-The operation was aborted, typically due to a concurrent action.
-
-For example, running a
-[`queue.enqueue()`](../../api_docs/python/io_ops.md#QueueBase.enqueue)
-operation may raise `AbortedError` if a
-[`queue.close()`](../../api_docs/python/io_ops.md#QueueBase.close) operation
-previously ran.
-
-- - -
-
-#### `tf.errors.AbortedError.__init__(node_def, op, message)` {#AbortedError.__init__}
-
-Creates an `AbortedError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.errors.FailedPreconditionError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.errors.FailedPreconditionError.md
new file mode 100644
index 0000000000..1cbd338bf9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.errors.FailedPreconditionError.md
@@ -0,0 +1,13 @@
+Operation was rejected because the system is not in a state to execute it.
+
+This exception is most commonly raised when running an operation
+that reads a [`tf.Variable`](../../api_docs/python/state_ops.md#Variable)
+before it has been initialized.
+
+- - -
+
+#### `tf.errors.FailedPreconditionError.__init__(node_def, op, message)` {#FailedPreconditionError.__init__}
+
+Creates a `FailedPreconditionError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.get_default_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.get_default_graph.md
new file mode 100644
index 0000000000..bd734d1b98
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.get_default_graph.md
@@ -0,0 +1,17 @@
+### `tf.get_default_graph()` {#get_default_graph}
+
+Returns the default graph for the current thread.
+
+The returned graph will be the innermost graph on which a
+`Graph.as_default()` context has been entered, or a global default
+graph if none has been explicitly created.
+
+NOTE: The default graph is a property of the current thread. If you
+create a new thread, and wish to use the default graph in that
+thread, you must explicitly add a `with g.as_default():` in that
+thread's function.
+
+##### Returns:
+
+  The default `Graph` being used in the current thread.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.gradients.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.gradients.md
new file mode 100644
index 0000000000..ea710b2a15
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.gradients.md
@@ -0,0 +1,48 @@
+### `tf.gradients(ys, xs, grad_ys=None, name='gradients', colocate_gradients_with_ops=False, gate_gradients=False, aggregation_method=None)` {#gradients}
+
+Constructs symbolic partial derivatives of sum of `ys` w.r.t. x in `xs`.
+
+`ys` and `xs` are each a `Tensor` or a list of tensors.  `grad_ys`
+is a list of `Tensor`, holding the gradients received by the
+`ys`. The list must be the same length as `ys`.
+
+`gradients()` adds ops to the graph to output the partial
+derivatives of `ys` with respect to `xs`.  It returns a list of
+`Tensor` of length `len(xs)` where each tensor is the `sum(dy/dx)`
+for y in `ys`.
+
+`grad_ys` is a list of tensors of the same length as `ys` that holds
+the initial gradients for each y in `ys`.  When `grad_ys` is None,
+we fill in a tensor of '1's of the shape of y for each y in `ys`.  A
+user can provide their own initial `grad_ys` to compute the
+derivatives using a different initial gradient for each y (e.g., if
+one wanted to weight the gradient differently for each value in
+each y).
+
+##### Args:
+
+
+*  <b>`ys`</b>: A `Tensor` or list of tensors to be differentiated.
+*  <b>`xs`</b>: A `Tensor` or list of tensors to be used for differentiation.
+*  <b>`grad_ys`</b>: Optional. A `Tensor` or list of tensors the same size as
+    `ys` and holding the gradients computed for each y in `ys`.
+*  <b>`name`</b>: Optional name to use for grouping all the gradient ops together.
+    defaults to 'gradients'.
+*  <b>`colocate_gradients_with_ops`</b>: If True, try colocating gradients with
+    the corresponding op.
+*  <b>`gate_gradients`</b>: If True, add a tuple around the gradients returned
+    for an operations.  This avoids some race conditions.
+*  <b>`aggregation_method`</b>: Specifies the method used to combine gradient terms.
+    Accepted values are constants defined in the class `AggregationMethod`.
+
+##### Returns:
+
+  A list of `sum(dy/dx)` for each x in `xs`.
+
+##### Raises:
+
+
+*  <b>`LookupError`</b>: if one of the operations between `x` and `y` does not
+    have a registered gradient function.
+*  <b>`ValueError`</b>: if the arguments are invalid.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.histogram_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.histogram_summary.md
new file mode 100644
index 0000000000..1ede11e820
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.histogram_summary.md
@@ -0,0 +1,25 @@
+### `tf.histogram_summary(tag, values, collections=None, name=None)` {#histogram_summary}
+
+Outputs a `Summary` protocol buffer with a histogram.
+
+The generated
+[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
+has one summary value containing a histogram for `values`.
+
+This op reports an `InvalidArgument` error if any value is not finite.
+
+##### Args:
+
+
+*  <b>`tag`</b>: A `string` `Tensor`. 0-D.  Tag to use for the summary value.
+*  <b>`values`</b>: A real numeric `Tensor`. Any shape. Values to use to
+    build the histogram.
+*  <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
+    added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A scalar `Tensor` of type `string`. The serialized `Summary` protocol
+  buffer.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.adjust_contrast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.adjust_contrast.md
new file mode 100644
index 0000000000..2fbf1b3e2a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.adjust_contrast.md
@@ -0,0 +1,29 @@
+### `tf.image.adjust_contrast(images, contrast_factor)` {#adjust_contrast}
+
+Adjust contrast of RGB or grayscale images.
+
+This is a convenience method that converts an RGB image to float
+representation, adjusts its contrast, and then converts it back to the
+original data type. If several adjustments are chained it is advisable to
+minimize the number of redundant conversions.
+
+`images` is a tensor of at least 3 dimensions.  The last 3 dimensions are
+interpreted as `[height, width, channels]`.  The other dimensions only
+represent a collection of images, such as `[batch, height, width, channels].`
+
+Contrast is adjusted independently for each channel of each image.
+
+For each channel, this Op computes the mean of the image pixels in the
+channel and then adjusts each component `x` of each pixel to
+`(x - mean) * contrast_factor + mean`.
+
+##### Args:
+
+
+*  <b>`images`</b>: Images to adjust.  At least 3-D.
+*  <b>`contrast_factor`</b>: A float multiplier for adjusting contrast.
+
+##### Returns:
+
+  The contrast-adjusted image or images.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.adjust_hue.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.adjust_hue.md
deleted file mode 100644
index e334e26184..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.adjust_hue.md
+++ /dev/null
@@ -1,26 +0,0 @@
-### `tf.image.adjust_hue(image, delta, name=None)` {#adjust_hue}
-
-Adjust hue of an RGB image.
-
-This is a convenience method that converts an RGB image to float
-representation, converts it to HSV, add an offset to the hue channel, converts
-back to RGB and then back to the original data type. If several adjustments
-are chained it is advisable to minimize the number of redundant conversions.
-
-`image` is an RGB image.  The image hue is adjusted by converting the
-image to HSV and rotating the hue channel (H) by
-`delta`.  The image is then converted back to RGB.
-
-`delta` must be in the interval `[-1, 1]`.
-
-##### Args:
-
-
-*  <b>`image`</b>: RGB image or images. Size of the last dimension must be 3.
-*  <b>`delta`</b>: float.  How much to add to the hue channel.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  Adjusted image(s), same shape and DType as `image`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.central_crop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.central_crop.md
deleted file mode 100644
index 4e6b6115f8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.central_crop.md
+++ /dev/null
@@ -1,30 +0,0 @@
-### `tf.image.central_crop(image, central_fraction)` {#central_crop}
-
-Crop the central region of the image.
-
-Remove the outer parts of an image but retain the central region of the image
-along each dimension. If we specify central_fraction = 0.5, this function
-returns the region marked with "X" in the below diagram.
-
-     --------
-    |        |
-    |  XXXX  |
-    |  XXXX  |
-    |        |   where "X" is the central 50% of the image.
-     --------
-
-##### Args:
-
-
-*  <b>`image`</b>: 3-D float Tensor of shape [height, width, depth]
-*  <b>`central_fraction`</b>: float (0, 1], fraction of size to crop
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if central_crop_fraction is not within (0, 1].
-
-##### Returns:
-
-  3-D float Tensor
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.crop_to_bounding_box.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.crop_to_bounding_box.md
new file mode 100644
index 0000000000..4724ff5eb9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.crop_to_bounding_box.md
@@ -0,0 +1,30 @@
+### `tf.image.crop_to_bounding_box(image, offset_height, offset_width, target_height, target_width)` {#crop_to_bounding_box}
+
+Crops an image to a specified bounding box.
+
+This op cuts a rectangular part out of `image`. The top-left corner of the
+returned image is at `offset_height, offset_width` in `image`, and its
+lower-right corner is at
+`offset_height + target_height, offset_width + target_width`.
+
+##### Args:
+
+
+*  <b>`image`</b>: 3-D tensor with shape `[height, width, channels]`
+*  <b>`offset_height`</b>: Vertical coordinate of the top-left corner of the result in
+                 the input.
+*  <b>`offset_width`</b>: Horizontal coordinate of the top-left corner of the result in
+                the input.
+*  <b>`target_height`</b>: Height of the result.
+*  <b>`target_width`</b>: Width of the result.
+
+##### Returns:
+
+  3-D tensor of image with shape `[target_height, target_width, channels]`
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the shape of `image` is incompatible with the `offset_*` or
+  `target_*` arguments
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.decode_jpeg.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.decode_jpeg.md
deleted file mode 100644
index f4c6f1340a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.decode_jpeg.md
+++ /dev/null
@@ -1,41 +0,0 @@
-### `tf.image.decode_jpeg(contents, channels=None, ratio=None, fancy_upscaling=None, try_recover_truncated=None, acceptable_fraction=None, name=None)` {#decode_jpeg}
-
-Decode a JPEG-encoded image to a uint8 tensor.
-
-The attr `channels` indicates the desired number of color channels for the
-decoded image.
-
-Accepted values are:
-
-*   0: Use the number of channels in the JPEG-encoded image.
-*   1: output a grayscale image.
-*   3: output an RGB image.
-
-If needed, the JPEG-encoded image is transformed to match the requested number
-of color channels.
-
-The attr `ratio` allows downscaling the image by an integer factor during
-decoding.  Allowed values are: 1, 2, 4, and 8.  This is much faster than
-downscaling the image later.
-
-##### Args:
-
-
-*  <b>`contents`</b>: A `Tensor` of type `string`. 0-D.  The JPEG-encoded image.
-*  <b>`channels`</b>: An optional `int`. Defaults to `0`.
-    Number of color channels for the decoded image.
-*  <b>`ratio`</b>: An optional `int`. Defaults to `1`. Downscaling ratio.
-*  <b>`fancy_upscaling`</b>: An optional `bool`. Defaults to `True`.
-    If true use a slower but nicer upscaling of the
-    chroma planes (yuv420/422 only).
-*  <b>`try_recover_truncated`</b>: An optional `bool`. Defaults to `False`.
-    If true try to recover an image from truncated input.
-*  <b>`acceptable_fraction`</b>: An optional `float`. Defaults to `1`.
-    The minimum required fraction of lines before a truncated
-    input is accepted.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `uint8`. 3-D with shape `[height, width, channels]`..
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.encode_png.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.encode_png.md
deleted file mode 100644
index fa073a771f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.encode_png.md
+++ /dev/null
@@ -1,28 +0,0 @@
-### `tf.image.encode_png(image, compression=None, name=None)` {#encode_png}
-
-PNG-encode an image.
-
-`image` is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]`
-where `channels` is:
-
-*   1: for grayscale.
-*   2: for grayscale + alpha.
-*   3: for RGB.
-*   4: for RGBA.
-
-The ZLIB compression level, `compression`, can be -1 for the PNG-encoder
-default or a value from 0 to 9.  9 is the highest compression level, generating
-the smallest output, but is slower.
-
-##### Args:
-
-
-*  <b>`image`</b>: A `Tensor`. Must be one of the following types: `uint8`, `uint16`.
-    3-D with shape `[height, width, channels]`.
-*  <b>`compression`</b>: An optional `int`. Defaults to `-1`. Compression level.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `string`. 0-D. PNG-encoded image.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.hsv_to_rgb.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.hsv_to_rgb.md
new file mode 100644
index 0000000000..3193dd9c60
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.hsv_to_rgb.md
@@ -0,0 +1,21 @@
+### `tf.image.hsv_to_rgb(images, name=None)` {#hsv_to_rgb}
+
+Convert one or more images from HSV to RGB.
+
+Outputs a tensor of the same shape as the `images` tensor, containing the RGB
+value of the pixels. The output is only well defined if the value in `images`
+are in `[0,1]`.
+
+See `rgb_to_hsv` for a description of the HSV encoding.
+
+##### Args:
+
+
+*  <b>`images`</b>: A `Tensor` of type `float32`.
+    1-D or higher rank. HSV data to convert. Last dimension must be size 3.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `float32`. `images` converted to RGB.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.resize_bicubic.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.resize_bicubic.md
deleted file mode 100644
index 1805c7423d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.resize_bicubic.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.image.resize_bicubic(images, size, align_corners=None, name=None)` {#resize_bicubic}
-
-Resize `images` to `size` using bicubic interpolation.
-
-Input images can be of different types but output images are always float.
-
-##### Args:
-
-
-*  <b>`images`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
-    4-D with shape `[batch, height, width, channels]`.
-*  <b>`size`</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
-    new size for the images.
-*  <b>`align_corners`</b>: An optional `bool`. Defaults to `False`.
-    If true, rescale input by (new_height - 1) / (height - 1), which
-    exactly aligns the 4 corners of images and resized images. If false, rescale
-    by new_height / height. Treat similarly the width dimension.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `float32`. 4-D with shape
-  `[batch, new_height, new_width, channels]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.resize_bilinear.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.resize_bilinear.md
deleted file mode 100644
index a9580ca199..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.resize_bilinear.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.image.resize_bilinear(images, size, align_corners=None, name=None)` {#resize_bilinear}
-
-Resize `images` to `size` using bilinear interpolation.
-
-Input images can be of different types but output images are always float.
-
-##### Args:
-
-
-*  <b>`images`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
-    4-D with shape `[batch, height, width, channels]`.
-*  <b>`size`</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
-    new size for the images.
-*  <b>`align_corners`</b>: An optional `bool`. Defaults to `False`.
-    If true, rescale input by (new_height - 1) / (height - 1), which
-    exactly aligns the 4 corners of images and resized images. If false, rescale
-    by new_height / height. Treat similarly the width dimension.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `float32`. 4-D with shape
-  `[batch, new_height, new_width, channels]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.sample_distorted_bounding_box.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.sample_distorted_bounding_box.md
new file mode 100644
index 0000000000..2831492f54
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image.sample_distorted_bounding_box.md
@@ -0,0 +1,85 @@
+### `tf.image.sample_distorted_bounding_box(image_size, bounding_boxes, seed=None, seed2=None, min_object_covered=None, aspect_ratio_range=None, area_range=None, max_attempts=None, use_image_if_no_bounding_boxes=None, name=None)` {#sample_distorted_bounding_box}
+
+Generate a single randomly distorted bounding box for an image.
+
+Bounding box annotations are often supplied in addition to ground-truth labels
+in image recognition or object localization tasks. A common technique for
+training such a system is to randomly distort an image while preserving
+its content, i.e. *data augmentation*. This Op outputs a randomly distorted
+localization of an object, i.e. bounding box, given an `image_size`,
+`bounding_boxes` and a series of constraints.
+
+The output of this Op is a single bounding box that may be used to crop the
+original image. The output is returned as 3 tensors: `begin`, `size` and
+`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
+image. The latter may be supplied to `tf.image.draw_bounding_box` to visualize
+what the bounding box looks like.
+
+Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
+bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
+height of the underlying image.
+
+For example,
+
+    # Generate a single distorted bounding box.
+    begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
+        tf.shape(image),
+        bounding_boxes=bounding_boxes)
+
+    # Draw the bounding box in an image summary.
+    image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
+                                                  bbox_for_draw)
+    tf.image_summary('images_with_box', image_with_box)
+
+    # Employ the bounding box to distort the image.
+    distorted_image = tf.slice(image, begin, size)
+
+Note that if no bounding box information is available, setting
+`use_image_if_no_bounding_boxes = true` will assume there is a single implicit
+bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
+false and no bounding boxes are supplied, an error is raised.
+
+##### Args:
+
+
+*  <b>`image_size`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`.
+    1-D, containing `[height, width, channels]`.
+*  <b>`bounding_boxes`</b>: A `Tensor` of type `float32`.
+    3-D with shape `[batch, N, 4]` describing the N bounding boxes
+    associated with the image.
+*  <b>`seed`</b>: An optional `int`. Defaults to `0`.
+    If either `seed` or `seed2` are set to non-zero, the random number
+    generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
+    seed.
+*  <b>`seed2`</b>: An optional `int`. Defaults to `0`.
+    A second seed to avoid seed collision.
+*  <b>`min_object_covered`</b>: An optional `float`. Defaults to `0.1`.
+    The cropped area of the image must contain at least this
+    fraction of any bounding box supplied.
+*  <b>`aspect_ratio_range`</b>: An optional list of `floats`. Defaults to `[0.75, 1.33]`.
+    The cropped area of the image must have an aspect ratio =
+    width / height within this range.
+*  <b>`area_range`</b>: An optional list of `floats`. Defaults to `[0.05, 1]`.
+    The cropped area of the image must contain a fraction of the
+    supplied image within in this range.
+*  <b>`max_attempts`</b>: An optional `int`. Defaults to `100`.
+    Number of attempts at generating a cropped region of the image
+    of the specified constraints. After `max_attempts` failures, return the entire
+    image.
+*  <b>`use_image_if_no_bounding_boxes`</b>: An optional `bool`. Defaults to `False`.
+    Controls behavior if no bounding boxes supplied.
+    If true, assume an implicit bounding box covering the whole input. If false,
+    raise an error.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tuple of `Tensor` objects (begin, size, bboxes).
+
+*  <b>`begin`</b>: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
+    `tf.slice`.
+*  <b>`size`</b>: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[target_height, target_width, -1]`. Provide as input to
+    `tf.slice`.
+*  <b>`bboxes`</b>: A `Tensor` of type `float32`. 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
+    Provide as input to `tf.image.draw_bounding_boxes`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image_summary.md
deleted file mode 100644
index 5df729544b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.image_summary.md
+++ /dev/null
@@ -1,48 +0,0 @@
-### `tf.image_summary(tag, tensor, max_images=3, collections=None, name=None)` {#image_summary}
-
-Outputs a `Summary` protocol buffer with images.
-
-The summary has up to `max_images` summary values containing images. The
-images are built from `tensor` which must be 4-D with shape `[batch_size,
-height, width, channels]` and where `channels` can be:
-
-*  1: `tensor` is interpreted as Grayscale.
-*  3: `tensor` is interpreted as RGB.
-*  4: `tensor` is interpreted as RGBA.
-
-The images have the same number of channels as the input tensor. For float
-input, the values are normalized one image at a time to fit in the range
-`[0, 255]`.  `uint8` values are unchanged.  The op uses two different
-normalization algorithms:
-
-*  If the input values are all positive, they are rescaled so the largest one
-   is 255.
-
-*  If any input value is negative, the values are shifted so input value 0.0
-   is at 127.  They are then rescaled so that either the smallest value is 0,
-   or the largest one is 255.
-
-The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
-build the `tag` of the summary values:
-
-*  If `max_images` is 1, the summary value tag is '*tag*/image'.
-*  If `max_images` is greater than 1, the summary value tags are
-   generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
-
-##### Args:
-
-
-*  <b>`tag`</b>: A scalar `Tensor` of type `string`. Used to build the `tag`
-    of the summary values.
-*  <b>`tensor`</b>: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
-    width, channels]` where `channels` is 1, 3, or 4.
-*  <b>`max_images`</b>: Max number of batch elements to generate images for.
-*  <b>`collections`</b>: Optional list of ops.GraphKeys.  The collections to add the
-    summary to.  Defaults to [ops.GraphKeys.SUMMARIES]
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A scalar `Tensor` of type `string`. The serialized `Summary` protocol
-  buffer.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.initialize_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.initialize_variables.md
new file mode 100644
index 0000000000..8941ab4853
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.initialize_variables.md
@@ -0,0 +1,24 @@
+### `tf.initialize_variables(var_list, name='init')` {#initialize_variables}
+
+Returns an Op that initializes a list of variables.
+
+After you launch the graph in a session, you can run the returned Op to
+initialize all the variables in `var_list`. This Op runs all the
+initializers of the variables in `var_list` in parallel.
+
+Calling `initialize_variables()` is equivalent to passing the list of
+initializers to `Group()`.
+
+If `var_list` is empty, however, the function still returns an Op that can
+be run. That Op just has no effect.
+
+##### Args:
+
+
+*  <b>`var_list`</b>: List of `Variable` objects to initialize.
+*  <b>`name`</b>: Optional name for the returned operation.
+
+##### Returns:
+
+  An Op that run the initializers of all the specified variables.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.less.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.less.md
deleted file mode 100644
index 8791d0366a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.less.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.less(x, y, name=None)` {#less}
-
-Returns the truth value of (x < y) element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `bool`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.less_equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.less_equal.md
new file mode 100644
index 0000000000..65d7eb5084
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.less_equal.md
@@ -0,0 +1,15 @@
+### `tf.less_equal(x, y, name=None)` {#less_equal}
+
+Returns the truth value of (x <= y) element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.load_file_system_library.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.load_file_system_library.md
deleted file mode 100644
index 60d768a624..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.load_file_system_library.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.load_file_system_library(library_filename)` {#load_file_system_library}
-
-Loads a TensorFlow plugin, containing file system implementation.
-
-Pass `library_filename` to a platform-specific mechanism for dynamically
-loading a library. The rules for determining the exact location of the
-library are platform-specific and are not documented here.
-
-##### Args:
-
-
-*  <b>`library_filename`</b>: Path to the plugin.
-    Relative or absolute filesystem path to a dynamic library file.
-
-##### Returns:
-
-  None.
-
-##### Raises:
-
-
-*  <b>`RuntimeError`</b>: when unable to load the library.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.logical_not.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.logical_not.md
new file mode 100644
index 0000000000..40a0bb2e43
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.logical_not.md
@@ -0,0 +1,14 @@
+### `tf.logical_not(x, name=None)` {#logical_not}
+
+Returns the truth value of NOT x element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` of type `bool`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.logical_xor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.logical_xor.md
new file mode 100644
index 0000000000..20db3e60a6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.logical_xor.md
@@ -0,0 +1,4 @@
+### `tf.logical_xor(x, y, name='LogicalXor')` {#logical_xor}
+
+x ^ y = (x | y) & ~(x & y).
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.make_template.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.make_template.md
new file mode 100644
index 0000000000..bb0cff57cd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.make_template.md
@@ -0,0 +1,105 @@
+### `tf.make_template(name_, func_, create_scope_now_=False, **kwargs)` {#make_template}
+
+Given an arbitrary function, wrap it so that it does variable sharing.
+
+This wraps `func_` in a Template and partially evaluates it. Templates are
+functions that create variables the first time they are called and reuse them
+thereafter. In order for `func_` to be compatible with a `Template` it must
+have the following properties:
+
+* The function should create all trainable variables and any variables that
+   should be reused by calling `tf.get_variable`. If a trainable variable is
+   created using `tf.Variable`, then a ValueError will be thrown. Variables
+   that are intended to be locals can be created by specifying
+   `tf.Variable(..., trainable=false)`.
+* The function may use variable scopes and other templates internally to
+    create and reuse variables, but it shouldn't use `tf.get_variables` to
+    capture variables that are defined outside of the scope of the function.
+* Internal scopes and variable names should not depend on any arguments that
+    are not supplied to `make_template`. In general you will get a ValueError
+    telling you that you are trying to reuse a variable that doesn't exist
+    if you make a mistake.
+
+In the following example, both `z` and `w` will be scaled by the same `y`. It
+is important to note that if we didn't assign `scalar_name` and used a
+different name for z and w that a `ValueError` would be thrown because it
+couldn't reuse the variable.
+
+```python
+def my_op(x, scalar_name):
+  var1 = tf.get_variable(scalar_name,
+                         shape=[],
+                         initializer=tf.constant_initializer(1))
+  return x * var1
+
+scale_by_y = tf.make_template('scale_by_y', my_op, scalar_name='y')
+
+z = scale_by_y(input1)
+w = scale_by_y(input2)
+```
+
+As a safe-guard, the returned function will raise a `ValueError` after the
+first call if trainable variables are created by calling `tf.Variable`.
+
+If all of these are true, then 2 properties are enforced by the template:
+
+1. Calling the same template multiple times will share all non-local
+    variables.
+2. Two different templates are guaranteed to be unique, unless you reenter the
+    same variable scope as the initial definition of a template and redefine
+    it. An examples of this exception:
+
+```python
+def my_op(x, scalar_name):
+  var1 = tf.get_variable(scalar_name,
+                         shape=[],
+                         initializer=tf.constant_initializer(1))
+  return x * var1
+
+with tf.variable_scope('scope') as vs:
+  scale_by_y = tf.make_template('scale_by_y', my_op, scalar_name='y')
+  z = scale_by_y(input1)
+  w = scale_by_y(input2)
+
+# Creates a template that reuses the variables above.
+with tf.variable_scope(vs, reuse=True):
+  scale_by_y2 = tf.make_template('scale_by_y', my_op, scalar_name='y')
+  z2 = scale_by_y2(input1)
+  w2 = scale_by_y2(input2)
+```
+
+Depending on the value of `create_scope_now_`, the full variable scope may be
+captured either at the time of first call or at the time of construction. If
+this option is set to True, then all Tensors created by repeated calls to the
+template will have an extra trailing _N+1 to their name, as the first time the
+scope is entered in the Template constructor no Tensors are created.
+
+Note: `name_`, `func_` and `create_scope_now_` have a trailing underscore to
+reduce the likelihood of collisions with kwargs.
+
+##### Args:
+
+
+*  <b>`name_`</b>: A name for the scope created by this template. If necessary, the name
+    will be made unique by appending `_N` to the name.
+*  <b>`func_`</b>: The function to wrap.
+*  <b>`create_scope_now_`</b>: Boolean controlling whether the scope should be created
+    when the template is constructed or when the template is called. Default
+    is False, meaning the scope is created when the template is called.
+*  <b>`**kwargs`</b>: Keyword arguments to apply to `func_`.
+
+##### Returns:
+
+  A function to encapsulate a set of variables which should be created once
+  and reused. An enclosing scope will created, either where `make_template`
+  is called, or wherever the result is called, depending on the value of
+  `create_scope_now_`. Regardless of the value, the first time the template
+  is called it will enter the scope with no reuse, and call `func_` to create
+  variables, which are guaranteed to be unique. All subsequent calls will
+  re-enter the scope and reuse those variables.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the name is None.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.minimum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.minimum.md
deleted file mode 100644
index bff13483f4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.minimum.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.minimum(x, y, name=None)` {#minimum}
-
-Returns the min of x and y (i.e. x < y ? x : y) element-wise, broadcasts.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.multinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.multinomial.md
new file mode 100644
index 0000000000..b5bf7a30a5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.multinomial.md
@@ -0,0 +1,28 @@
+### `tf.multinomial(logits, num_samples, seed=None, name=None)` {#multinomial}
+
+Draws samples from a multinomial distribution.
+
+Example:
+
+  samples = tf.multinomial(tf.log([[0.5, 0.5]]), 10)
+  # samples has shape [1, 10], where each value is either 0 or 1.
+
+  samples = tf.multinomial([[1, -1, -1]], 10)
+  # samples is equivalent to tf.zeros([1, 10], dtype=tf.int64).
+
+##### Args:
+
+
+*  <b>`logits`</b>: 2-D Tensor with shape `[batch_size, num_classes]`.  Each slice
+    `[i, :]` represents the unnormalized log probabilities for all classes.
+*  <b>`num_samples`</b>: 0-D.  Number of independent samples to draw for each row slice.
+*  <b>`seed`</b>: A Python integer. Used to create a random seed for the distribution.
+    See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`name`</b>: Optional name for the operation.
+
+##### Returns:
+
+  The drawn samples of shape `[batch_size, num_samples]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.name_scope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.name_scope.md
new file mode 100644
index 0000000000..a003f2327f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.name_scope.md
@@ -0,0 +1,18 @@
+### `tf.name_scope(name)` {#name_scope}
+
+Wrapper for `Graph.name_scope()` using the default graph.
+
+See
+[`Graph.name_scope()`](../../api_docs/python/framework.md#Graph.name_scope)
+for more details.
+
+##### Args:
+
+
+*  <b>`name`</b>: A name for the scope.
+
+##### Returns:
+
+  A context manager that installs `name` as a new name scope in the
+  default graph.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.avg_pool.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.avg_pool.md
new file mode 100644
index 0000000000..33da8534c2
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.avg_pool.md
@@ -0,0 +1,25 @@
+### `tf.nn.avg_pool(value, ksize, strides, padding, data_format='NHWC', name=None)` {#avg_pool}
+
+Performs the average pooling on the input.
+
+Each entry in `output` is the mean of the corresponding size `ksize`
+window in `value`.
+
+##### Args:
+
+
+*  <b>`value`</b>: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type
+    `float32`, `float64`, `qint8`, `quint8`, or `qint32`.
+*  <b>`ksize`</b>: A list of ints that has length >= 4.
+    The size of the window for each dimension of the input tensor.
+*  <b>`strides`</b>: A list of ints that has length >= 4.
+    The stride of the sliding window for each dimension of the
+    input tensor.
+*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
+*  <b>`data_format`</b>: A string. 'NHWC' and 'NCHW' are supported.
+*  <b>`name`</b>: Optional name for the operation.
+
+##### Returns:
+
+  A `Tensor` with the same type as `value`.  The average pooled output tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bias_add.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bias_add.md
new file mode 100644
index 0000000000..1eea161f23
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bias_add.md
@@ -0,0 +1,24 @@
+### `tf.nn.bias_add(value, bias, data_format=None, name=None)` {#bias_add}
+
+Adds `bias` to `value`.
+
+This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D.
+Broadcasting is supported, so `value` may have any number of dimensions.
+Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the
+case where both types are quantized.
+
+##### Args:
+
+
+*  <b>`value`</b>: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`,
+    `int16`, `int8`, or `complex64`.
+*  <b>`bias`</b>: A 1-D `Tensor` with size matching the last dimension of `value`.
+    Must be the same type as `value` unless `value` is a quantized type,
+    in which case a different quantized type may be used.
+*  <b>`data_format`</b>: A string. 'NHWC' and 'NCHW' are supported.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` with the same type as `value`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.compute_accidental_hits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.compute_accidental_hits.md
deleted file mode 100644
index 9d5bb30303..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.compute_accidental_hits.md
+++ /dev/null
@@ -1,45 +0,0 @@
-### `tf.nn.compute_accidental_hits(true_classes, sampled_candidates, num_true, seed=None, name=None)` {#compute_accidental_hits}
-
-Compute the position ids in `sampled_candidates` matching `true_classes`.
-
-In Candidate Sampling, this operation facilitates virtually removing
-sampled classes which happen to match target classes.  This is done
-in Sampled Softmax and Sampled Logistic.
-
-See our [Candidate Sampling Algorithms
-Reference](http://www.tensorflow.org/extras/candidate_sampling.pdf).
-
-We presuppose that the `sampled_candidates` are unique.
-
-We call it an 'accidental hit' when one of the target classes
-matches one of the sampled classes.  This operation reports
-accidental hits as triples `(index, id, weight)`, where `index`
-represents the row number in `true_classes`, `id` represents the
-position in `sampled_candidates`, and weight is `-FLOAT_MAX`.
-
-The result of this op should be passed through a `sparse_to_dense`
-operation, then added to the logits of the sampled classes. This
-removes the contradictory effect of accidentally sampling the true
-target classes as noise classes for the same example.
-
-##### Args:
-
-
-*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
-    num_true]`. The target classes.
-*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
-    The sampled_candidates output of CandidateSampler.
-*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
-*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-
-*  <b>`indices`</b>: A `Tensor` of type `int32` and shape `[num_accidental_hits]`.
-    Values indicate rows in `true_classes`.
-*  <b>`ids`</b>: A `Tensor` of type `int64` and shape `[num_accidental_hits]`.
-    Values indicate positions in `sampled_candidates`.
-*  <b>`weights`</b>: A `Tensor` of type `float` and shape `[num_accidental_hits]`.
-    Each value is `-FLOAT_MAX`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.depthwise_conv2d_native.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.depthwise_conv2d_native.md
new file mode 100644
index 0000000000..c2736f1ba9
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.depthwise_conv2d_native.md
@@ -0,0 +1,37 @@
+### `tf.nn.depthwise_conv2d_native(input, filter, strides, padding, name=None)` {#depthwise_conv2d_native}
+
+Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
+
+Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
+and a filter / kernel tensor of shape
+`[filter_height, filter_width, in_channels, channel_multiplier]`, containing
+`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
+a different filter to each input channel (expanding from 1 channel to
+`channel_multiplier` channels for each), then concatenates the results
+together. Thus, the output has `in_channels * channel_multiplier` channels.
+
+for k in 0..in_channels-1
+  for q in 0..channel_multiplier-1
+    output[b, i, j, k * channel_multiplier + q] =
+      sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+                        filter[di, dj, k, q]
+
+Must have `strides[0] = strides[3] = 1`.  For the most common case of the same
+horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`filter`</b>: A `Tensor`. Must have the same type as `input`.
+*  <b>`strides`</b>: A list of `ints`.
+    1-D of length 4.  The stride of the sliding window for each dimension
+    of `input`.
+*  <b>`padding`</b>: A `string` from: `"SAME", "VALID"`.
+    The type of padding algorithm to use.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.elu.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.elu.md
new file mode 100644
index 0000000000..cef8dedb50
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.elu.md
@@ -0,0 +1,17 @@
+### `tf.nn.elu(features, name=None)` {#elu}
+
+Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
+
+See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
+](http://arxiv.org/abs/1511.07289)
+
+##### Args:
+
+
+*  <b>`features`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `features`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.fixed_unigram_candidate_sampler.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.fixed_unigram_candidate_sampler.md
deleted file mode 100644
index ad9b059e42..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.fixed_unigram_candidate_sampler.md
+++ /dev/null
@@ -1,75 +0,0 @@
-### `tf.nn.fixed_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, vocab_file='', distortion=1.0, num_reserved_ids=0, num_shards=1, shard=0, unigrams=(), seed=None, name=None)` {#fixed_unigram_candidate_sampler}
-
-Samples a set of classes using the provided (fixed) base distribution.
-
-This operation randomly samples a tensor of sampled classes
-(`sampled_candidates`) from the range of integers `[0, range_max)`.
-
-The elements of `sampled_candidates` are drawn without replacement
-(if `unique=True`) or with replacement (if `unique=False`) from
-the base distribution.
-
-The base distribution is read from a file or passed in as an
-in-memory array. There is also an option to skew the distribution by
-applying a distortion power to the weights.
-
-In addition, this operation returns tensors `true_expected_count`
-and `sampled_expected_count` representing the number of times each
-of the target classes (`true_classes`) and the sampled
-classes (`sampled_candidates`) is expected to occur in an average
-tensor of sampled classes.  These values correspond to `Q(y|x)`
-defined in [this
-document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
-If `unique=True`, then these are post-rejection probabilities and we
-compute them approximately.
-
-##### Args:
-
-
-*  <b>`true_classes`</b>: A `Tensor` of type `int64` and shape `[batch_size,
-    num_true]`. The target classes.
-*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
-*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
-*  <b>`unique`</b>: A `bool`. Determines whether all sampled classes in a batch are
-    unique.
-*  <b>`range_max`</b>: An `int`. The number of possible classes.
-*  <b>`vocab_file`</b>: Each valid line in this file (which should have a CSV-like
-    format) corresponds to a valid word ID. IDs are in sequential order,
-    starting from num_reserved_ids. The last entry in each line is expected
-    to be a value corresponding to the count or relative probability. Exactly
-    one of `vocab_file` and `unigrams` needs to be passed to this operation.
-*  <b>`distortion`</b>: The distortion is used to skew the unigram probability
-    distribution.  Each weight is first raised to the distortion's power
-    before adding to the internal unigram distribution. As a result,
-    `distortion = 1.0` gives regular unigram sampling (as defined by the vocab
-    file), and `distortion = 0.0` gives a uniform distribution.
-*  <b>`num_reserved_ids`</b>: Optionally some reserved IDs can be added in the range
-    `[0, num_reserved_ids]` by the users. One use case is that a special
-    unknown word token is used as ID 0. These IDs will have a sampling
-    probability of 0.
-*  <b>`num_shards`</b>: A sampler can be used to sample from a subset of the original
-    range in order to speed up the whole computation through parallelism. This
-    parameter (together with `shard`) indicates the number of partitions that
-    are being used in the overall computation.
-*  <b>`shard`</b>: A sampler can be used to sample from a subset of the original range
-    in order to speed up the whole computation through parallelism. This
-    parameter (together with `num_shards`) indicates the particular partition
-    number of the operation, when partitioning is being used.
-*  <b>`unigrams`</b>: A list of unigram counts or probabilities, one per ID in
-    sequential order. Exactly one of `vocab_file` and `unigrams` should be
-    passed to this operation.
-*  <b>`seed`</b>: An `int`. An operation-specific seed. Default is 0.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-
-*  <b>`sampled_candidates`</b>: A tensor of type `int64` and shape `[num_sampled]`.
-    The sampled classes.
-*  <b>`true_expected_count`</b>: A tensor of type `float`.  Same shape as
-    `true_classes`. The expected counts under the sampling distribution
-    of each of `true_classes`.
-*  <b>`sampled_expected_count`</b>: A tensor of type `float`. Same shape as
-    `sampled_candidates`. The expected counts under the sampling distribution
-    of each of `sampled_candidates`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.local_response_normalization.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.local_response_normalization.md
new file mode 100644
index 0000000000..349e34fa73
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.local_response_normalization.md
@@ -0,0 +1,34 @@
+### `tf.nn.local_response_normalization(input, depth_radius=None, bias=None, alpha=None, beta=None, name=None)` {#local_response_normalization}
+
+Local Response Normalization.
+
+The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
+dimension), and each vector is normalized independently.  Within a given vector,
+each component is divided by the weighted, squared sum of inputs within
+`depth_radius`.  In detail,
+
+    sqr_sum[a, b, c, d] =
+        sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
+    output = input / (bias + alpha * sqr_sum) ** beta
+
+For details, see [Krizhevsky et al., ImageNet classification with deep
+convolutional neural networks (NIPS 2012)]
+(http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `float32`. 4-D.
+*  <b>`depth_radius`</b>: An optional `int`. Defaults to `5`.
+    0-D.  Half-width of the 1-D normalization window.
+*  <b>`bias`</b>: An optional `float`. Defaults to `1`.
+    An offset (usually positive to avoid dividing by 0).
+*  <b>`alpha`</b>: An optional `float`. Defaults to `1`.
+    A scale factor, usually positive.
+*  <b>`beta`</b>: An optional `float`. Defaults to `0.5`. An exponent.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `float32`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.separable_conv2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.separable_conv2d.md
deleted file mode 100644
index f4be03303f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.separable_conv2d.md
+++ /dev/null
@@ -1,40 +0,0 @@
-### `tf.nn.separable_conv2d(input, depthwise_filter, pointwise_filter, strides, padding, name=None)` {#separable_conv2d}
-
-2-D convolution with separable filters.
-
-Performs a depthwise convolution that acts separately on channels followed by
-a pointwise convolution that mixes channels.  Note that this is separability
-between dimensions `[1, 2]` and `3`, not spatial separability between
-dimensions `1` and `2`.
-
-In detail,
-
-    output[b, i, j, k] = sum_{di, dj, q, r]
-        input[b, strides[1] * i + di, strides[2] * j + dj, q] *
-        depthwise_filter[di, dj, q, r] *
-        pointwise_filter[0, 0, q * channel_multiplier + r, k]
-
-`strides` controls the strides for the depthwise convolution only, since
-the pointwise convolution has implicit strides of `[1, 1, 1, 1]`.  Must have
-`strides[0] = strides[3] = 1`.  For the most common case of the same
-horizontal and vertical strides, `strides = [1, stride, stride, 1]`.
-
-##### Args:
-
-
-*  <b>`input`</b>: 4-D `Tensor` with shape `[batch, in_height, in_width, in_channels]`.
-*  <b>`depthwise_filter`</b>: 4-D `Tensor` with shape
-    `[filter_height, filter_width, in_channels, channel_multiplier]`.
-    Contains `in_channels` convolutional filters of depth 1.
-*  <b>`pointwise_filter`</b>: 4-D `Tensor` with shape
-    `[1, 1, channel_multiplier * in_channels, out_channels]`.  Pointwise
-    filter to mix channels after `depthwise_filter` has convolved spatially.
-*  <b>`strides`</b>: 1-D of size 4.  The strides for the depthwise convolution for
-    each dimension of `input`.
-*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`.  The padding algorithm.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  A 4-D `Tensor` of shape `[batch, out_height, out_width, out_channels]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.sigmoid_cross_entropy_with_logits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.sigmoid_cross_entropy_with_logits.md
deleted file mode 100644
index c449554fb8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.sigmoid_cross_entropy_with_logits.md
+++ /dev/null
@@ -1,48 +0,0 @@
-### `tf.nn.sigmoid_cross_entropy_with_logits(logits, targets, name=None)` {#sigmoid_cross_entropy_with_logits}
-
-Computes sigmoid cross entropy given `logits`.
-
-Measures the probability error in discrete classification tasks in which each
-class is independent and not mutually exclusive.  For instance, one could
-perform multilabel classification where a picture can contain both an elephant
-and a dog at the same time.
-
-For brevity, let `x = logits`, `z = targets`.  The logistic loss is
-
-      z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
-    = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
-    = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
-    = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
-    = (1 - z) * x + log(1 + exp(-x))
-    = x - x * z + log(1 + exp(-x))
-
-For x < 0, to avoid overflow in exp(-x), we reformulate the above
-
-      x - x * z + log(1 + exp(-x))
-    = log(exp(x)) - x * z + log(1 + exp(-x))
-    = - x * z + log(1 + exp(x))
-
-Hence, to ensure stability and avoid overflow, the implementation uses this
-equivalent formulation
-
-    max(x, 0) - x * z + log(1 + exp(-abs(x)))
-
-`logits` and `targets` must have the same type and shape.
-
-##### Args:
-
-
-*  <b>`logits`</b>: A `Tensor` of type `float32` or `float64`.
-*  <b>`targets`</b>: A `Tensor` of the same type and shape as `logits`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of the same shape as `logits` with the componentwise
-  logistic losses.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `logits` and `targets` do not have the same shape.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.softsign.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.softsign.md
new file mode 100644
index 0000000000..971b2a8134
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.softsign.md
@@ -0,0 +1,14 @@
+### `tf.nn.softsign(features, name=None)` {#softsign}
+
+Computes softsign: `features / (abs(features) + 1)`.
+
+##### Args:
+
+
+*  <b>`features`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `features`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.sparse_softmax_cross_entropy_with_logits.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.sparse_softmax_cross_entropy_with_logits.md
deleted file mode 100644
index 6d53d84c5b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.sparse_softmax_cross_entropy_with_logits.md
+++ /dev/null
@@ -1,38 +0,0 @@
-### `tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels, name=None)` {#sparse_softmax_cross_entropy_with_logits}
-
-Computes sparse softmax cross entropy between `logits` and `labels`.
-
-Measures the probability error in discrete classification tasks in which the
-classes are mutually exclusive (each entry is in exactly one class).  For
-example, each CIFAR-10 image is labeled with one and only one label: an image
-can be a dog or a truck, but not both.
-
-**NOTE:**  For this operation, the probability of a given label is considered
-exclusive.  That is, soft classes are not allowed, and the `labels` vector
-must provide a single specific index for the true class for each row of
-`logits` (each minibatch entry).  For soft softmax classification with
-a probability distribution for each entry, see
-`softmax_cross_entropy_with_logits`.
-
-**WARNING:** This op expects unscaled logits, since it performs a softmax
-on `logits` internally for efficiency.  Do not call this op with the
-output of `softmax`, as it will produce incorrect results.
-
-`logits` must have the shape `[batch_size, num_classes]`
-and dtype `float32` or `float64`.
-
-`labels` must have the shape `[batch_size]` and dtype `int32` or `int64`.
-
-##### Args:
-
-
-*  <b>`logits`</b>: Unscaled log probabilities.
-*  <b>`labels`</b>: Each entry `labels[i]` must be an index in `[0, num_classes)`. Other
-    values will result in a loss of 0, but incorrect gradient computations.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A 1-D `Tensor` of length `batch_size` of the same type as `logits` with the
-  softmax cross entropy loss.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.no_regularizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.no_regularizer.md
new file mode 100644
index 0000000000..cb55675641
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.no_regularizer.md
@@ -0,0 +1,4 @@
+### `tf.no_regularizer(_)` {#no_regularizer}
+
+Use this function to prevent regularization of variables.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.one_hot.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.one_hot.md
deleted file mode 100644
index eebb6ab643..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.one_hot.md
+++ /dev/null
@@ -1,129 +0,0 @@
-### `tf.one_hot(indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None)` {#one_hot}
-
-Returns a one-hot tensor.
-
-The locations represented by indices in `indices` take value `on_value`,
-while all other locations take value `off_value`.
-
-`on_value` and `off_value` must have matching data types. If `dtype` is also
-provided, they must be the same data type as specified by `dtype`.
-
-If `on_value` is not provided, it will default to the value `1` with type
-`dtype`
-
-If `off_value` is not provided, it will default to the value `0` with type
-`dtype`
-
-If the input `indices` is rank `N`, the output will have rank `N+1`. The
-new axis is created at dimension `axis` (default: the new axis is appended
-at the end).
-
-If `indices` is a scalar the output shape will be a vector of length `depth`
-
-If `indices` is a vector of length `features`, the output shape will be:
-```
-  features x depth if axis == -1
-  depth x features if axis == 0
-```
-
-If `indices` is a matrix (batch) with shape `[batch, features]`, the output
-shape will be:
-```
-  batch x features x depth if axis == -1
-  batch x depth x features if axis == 1
-  depth x batch x features if axis == 0
-```
-
-If `dtype` is not provided, it will attempt to assume the data type of
-`on_value` or `off_value`, if one or both are passed in. If none of
-`on_value`, `off_value`, or `dtype` are provided, `dtype` will default to the
-value `tf.float32`
-
-Note: If a non-numeric data type output is desired (tf.string, tf.bool, etc.),
-both `on_value` and `off_value` _must_ be provided to `one_hot`
-
-Examples
-=========
-
-Suppose that
-
-```
-  indices = [0, 2, -1, 1]
-  depth = 3
-  on_value = 5.0
-  off_value = 0.0
-  axis = -1
-```
-
-Then output is `[4 x 3]`:
-
-```
-  output =
-  [5.0 0.0 0.0]  // one_hot(0)
-  [0.0 0.0 5.0]  // one_hot(2)
-  [0.0 0.0 0.0]  // one_hot(-1)
-  [0.0 5.0 0.0]  // one_hot(1)
-```
-
-Suppose that
-
-```
-  indices = [[0, 2], [1, -1]]
-  depth = 3
-  on_value = 1.0
-  off_value = 0.0
-  axis = -1
-```
-
-Then output is `[2 x 2 x 3]`:
-
-```
-  output =
-  [
-    [1.0, 0.0, 0.0]  // one_hot(0)
-    [0.0, 0.0, 1.0]  // one_hot(2)
-  ][
-    [0.0, 1.0, 0.0]  // one_hot(1)
-    [0.0, 0.0, 0.0]  // one_hot(-1)
-  ]
-```
-
-Using default values for `on_value` and `off_value`:
-
-```
-  indices = [0, 1, 2]
-  depth = 3
-```
-
-The output will be
-
-```
-  output =
-  [[1., 0., 0.],
-   [0., 1., 0.],
-   [0., 0., 1.]]
-```
-
-##### Args:
-
-
-*  <b>`indices`</b>: A `Tensor` of indices.
-*  <b>`depth`</b>: A scalar defining the depth of the one hot dimension.
-*  <b>`on_value`</b>: A scalar defining the value to fill in output when `indices[j]
-    = i`. (default: 1)
-*  <b>`off_value`</b>: A scalar defining the value to fill in output when `indices[j]
-    != i`. (default: 0)
-*  <b>`axis`</b>: The axis to fill (default: -1, a new inner-most axis).
-*  <b>`dtype`</b>: The data type of the output tensor.
-
-##### Returns:
-
-
-*  <b>`output`</b>: The one-hot tensor.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If dtype of either `on_value` or `off_value` don't match `dtype`
-*  <b>`TypeError`</b>: If dtype of `on_value` and `off_value` don't match one another
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.ones_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.ones_initializer.md
new file mode 100644
index 0000000000..0ddbc8b801
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.ones_initializer.md
@@ -0,0 +1,4 @@
+### `tf.ones_initializer(shape, dtype=tf.float32)` {#ones_initializer}
+
+An adaptor for ones() to match the Initializer spec.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.op_scope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.op_scope.md
new file mode 100644
index 0000000000..c1002fd125
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.op_scope.md
@@ -0,0 +1,36 @@
+### `tf.op_scope(values, name, default_name=None)` {#op_scope}
+
+Returns a context manager for use when defining a Python op.
+
+This context manager validates that the given `values` are from the
+same graph, ensures that graph is the default graph, and pushes a
+name scope.
+
+For example, to define a new Python op called `my_op`:
+
+```python
+def my_op(a, b, c, name=None):
+  with tf.op_scope([a, b, c], name, "MyOp") as scope:
+    a = tf.convert_to_tensor(a, name="a")
+    b = tf.convert_to_tensor(b, name="b")
+    c = tf.convert_to_tensor(c, name="c")
+    # Define some computation that uses `a`, `b`, and `c`.
+    return foo_op(..., name=scope)
+```
+
+##### Args:
+
+
+*  <b>`values`</b>: The list of `Tensor` arguments that are passed to the op function.
+*  <b>`name`</b>: The name argument that is passed to the op function.
+*  <b>`default_name`</b>: The default name to use if the `name` argument is `None`.
+
+##### Returns:
+
+  A context manager for use in defining Python ops. Yields the name scope.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if neither `name` nor `default_name` is provided.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.py_func.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.py_func.md
deleted file mode 100644
index c115d21781..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.py_func.md
+++ /dev/null
@@ -1,31 +0,0 @@
-### `tf.py_func(func, inp, Tout, name=None)` {#py_func}
-
-Wraps a python function and uses it as a tensorflow op.
-
-Given a python function `func`, which takes numpy arrays as its
-inputs and returns numpy arrays as its outputs. E.g.,
-
-```python
-def my_func(x):
-  # x will be a numpy array with the contents of the placeholder below
-  return np.sinh(x)
-inp = tf.placeholder(tf.float32, [...])
-y = py_func(my_func, [inp], [tf.float32])
-```
-
-The above snippet constructs a tf graph which invokes a numpy
-sinh(x) as an op in the graph.
-
-##### Args:
-
-
-*  <b>`func`</b>: A python function.
-*  <b>`inp`</b>: A list of `Tensor`.
-*  <b>`Tout`</b>: A list of tensorflow data types indicating what `func`
-        returns.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A list of `Tensor` which `func` computes.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.random_normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.random_normal.md
new file mode 100644
index 0000000000..1344423202
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.random_normal.md
@@ -0,0 +1,23 @@
+### `tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)` {#random_normal}
+
+Outputs random values from a normal distribution.
+
+##### Args:
+
+
+*  <b>`shape`</b>: A 1-D integer Tensor or Python array. The shape of the output tensor.
+*  <b>`mean`</b>: A 0-D Tensor or Python value of type `dtype`. The mean of the normal
+    distribution.
+*  <b>`stddev`</b>: A 0-D Tensor or Python value of type `dtype`. The standard deviation
+    of the normal distribution.
+*  <b>`dtype`</b>: The type of the output.
+*  <b>`seed`</b>: A Python integer. Used to create a random seed for the distribution.
+    See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A tensor of the specified shape filled with random normal values.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.random_normal_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.random_normal_initializer.md
deleted file mode 100644
index 9f229e3b1c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.random_normal_initializer.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### `tf.random_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)` {#random_normal_initializer}
-
-Returns an initializer that generates tensors with a normal distribution.
-
-##### Args:
-
-
-*  <b>`mean`</b>: a python scalar or a scalar tensor. Mean of the random values
-    to generate.
-*  <b>`stddev`</b>: a python scalar or a scalar tensor. Standard deviation of the
-    random values to generate.
-*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`dtype`</b>: The data type. Only floating point types are supported.
-
-##### Returns:
-
-  An initializer that generates tensors with a normal distribution.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.reduce_all.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.reduce_all.md
new file mode 100644
index 0000000000..3137d5c49e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.reduce_all.md
@@ -0,0 +1,35 @@
+### `tf.reduce_all(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_all}
+
+Computes the "logical and" of elements across dimensions of a tensor.
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+For example:
+
+```python
+# 'x' is [[True,  True]
+#         [False, False]]
+tf.reduce_all(x) ==> False
+tf.reduce_all(x, 0) ==> [False, False]
+tf.reduce_all(x, 1) ==> [True, False]
+```
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The boolean tensor to reduce.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.reduce_mean.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.reduce_mean.md
deleted file mode 100644
index af446b6c53..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.reduce_mean.md
+++ /dev/null
@@ -1,35 +0,0 @@
-### `tf.reduce_mean(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_mean}
-
-Computes the mean of elements across dimensions of a tensor.
-
-Reduces `input_tensor` along the dimensions given in `reduction_indices`.
-Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
-entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
-are retained with length 1.
-
-If `reduction_indices` has no entries, all dimensions are reduced, and a
-tensor with a single element is returned.
-
-For example:
-
-```python
-# 'x' is [[1., 1.]
-#         [2., 2.]]
-tf.reduce_mean(x) ==> 1.5
-tf.reduce_mean(x, 0) ==> [1.5, 1.5]
-tf.reduce_mean(x, 1) ==> [1.,  2.]
-```
-
-##### Args:
-
-
-*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
-*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
-    reduces all dimensions.
-*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The reduced tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.register_tensor_conversion_function.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.register_tensor_conversion_function.md
new file mode 100644
index 0000000000..e15dcf7b40
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.register_tensor_conversion_function.md
@@ -0,0 +1,42 @@
+### `tf.register_tensor_conversion_function(base_type, conversion_func, priority=100)` {#register_tensor_conversion_function}
+
+Registers a function for converting objects of `base_type` to `Tensor`.
+
+The conversion function must have the following signature:
+
+    def conversion_func(value, dtype=None, name=None, as_ref=False):
+      # ...
+
+It must return a `Tensor` with the given `dtype` if specified. If the
+conversion function creates a new `Tensor`, it should use the given
+`name` if specified. All exceptions will be propagated to the caller.
+
+The conversion function may return `NotImplemented` for some
+inputs. In this case, the conversion process will continue to try
+subsequent conversion functions.
+
+If `as_ref` is true, the function must return a `Tensor` reference,
+such as a `Variable`.
+
+NOTE: The conversion functions will execute in order of priority,
+followed by order of registration. To ensure that a conversion function
+`F` runs before another conversion function `G`, ensure that `F` is
+registered with a smaller priority than `G`.
+
+##### Args:
+
+
+*  <b>`base_type`</b>: The base type or tuple of base types for all objects that
+    `conversion_func` accepts.
+*  <b>`conversion_func`</b>: A function that converts instances of `base_type` to
+    `Tensor`.
+*  <b>`priority`</b>: Optional integer that indicates the priority for applying this
+    conversion function. Conversion functions with smaller priority values
+    run earlier than conversion functions with larger priority values.
+    Defaults to 100.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If the arguments do not have the appropriate type.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.rsqrt.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.rsqrt.md
deleted file mode 100644
index 5e8b1bc917..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.rsqrt.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.rsqrt(x, name=None)` {#rsqrt}
-
-Computes reciprocal of square root of x element-wise.
-
-I.e., \\(y = 1 / \sqrt{x}\\).
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.scan.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.scan.md
deleted file mode 100644
index 6ea0ac677b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.scan.md
+++ /dev/null
@@ -1,44 +0,0 @@
-### `tf.scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#scan}
-
-scan on the list of tensors unpacked from `elems` on dimension 0.
-
-This scan operator repeatedly applies the callable `fn` to a sequence
-of elements from first to last. The elements are made of the tensors
-unpacked from `elems` on dimension 0. The callable fn takes two tensors as
-arguments. The first argument is the accumulated value computed from the
-preceding invocation of fn. If `initializer` is None, `elems` must contain
-at least one element, and its first element is used as the initializer.
-
-Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
-of the result tensor is `[len(values)] + fn(initializer, values[0]).shape`.
-
-##### Args:
-
-
-*  <b>`fn`</b>: The callable to be performed.
-*  <b>`elems`</b>: A tensor to be unpacked on dimension 0.
-*  <b>`initializer`</b>: (optional) The initial value for the accumulator.
-*  <b>`parallel_iterations`</b>: (optional) The number of iterations allowed to run
-                       in parallel.
-*  <b>`back_prop`</b>: (optional) True enables back propagation.
-*  <b>`swap_memory`</b>: (optional) True enables GPU-CPU memory swapping.
-*  <b>`name`</b>: (optional) Name prefix for the returned tensors.
-
-##### Returns:
-
-  A tensor that packs the results of applying `fn` to the list of tensors
-  unpacked from `elems`, from first to last.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `fn` is not callable.
-
-##### Example:
-
-  ```python
-  elems = [1, 2, 3, 4, 5, 6]
-  sum = scan(lambda a, x: a + x, elems)
-  # sum == [1, 3, 6, 10, 15, 21]
-  ```
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.scatter_sub.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.scatter_sub.md
new file mode 100644
index 0000000000..8f1afc42f6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.scatter_sub.md
@@ -0,0 +1,44 @@
+### `tf.scatter_sub(ref, indices, updates, use_locking=None, name=None)` {#scatter_sub}
+
+Subtracts sparse updates to a variable reference.
+
+    # Scalar indices
+    ref[indices, ...] -= updates[...]
+
+    # Vector indices (for each i)
+    ref[indices[i], ...] -= updates[i, ...]
+
+    # High rank indices (for each i, ..., j)
+    ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
+
+This operation outputs `ref` after the update is done.
+This makes it easier to chain operations that need to use the reset value.
+
+Duplicate entries are handled correctly: if multiple `indices` reference
+the same location, their (negated) contributions add.
+
+Requires `updates.shape = indices.shape + ref.shape[1:]`.
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/ScatterSub.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`ref`</b>: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+    Should be from a `Variable` node.
+*  <b>`indices`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    A tensor of indices into the first dimension of `ref`.
+*  <b>`updates`</b>: A `Tensor`. Must have the same type as `ref`.
+    A tensor of updated values to subtract from `ref`.
+*  <b>`use_locking`</b>: An optional `bool`. Defaults to `False`.
+    If True, the subtraction will be protected by a lock;
+    otherwise the behavior is undefined, but may exhibit less contention.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  Same as `ref`.  Returned as a convenience for operations that want
+  to use the updated values after the update is done.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.segment_min.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.segment_min.md
deleted file mode 100644
index 5cacf2cf72..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.segment_min.md
+++ /dev/null
@@ -1,31 +0,0 @@
-### `tf.segment_min(data, segment_ids, name=None)` {#segment_min}
-
-Computes the minimum along segments of a tensor.
-
-Read [the section on
-Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
-of segments.
-
-Computes a tensor such that
-\\(output_i = \min_j(data_j)\\) where `min` is over `j` such
-that `segment_ids[j] == i`.
-
-<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-<img style="width:100%" src="../../images/SegmentMin.png" alt>
-</div>
-
-##### Args:
-
-
-*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
-    A 1-D tensor whose rank is equal to the rank of `data`'s
-    first dimension.  Values should be sorted and can be repeated.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `data`.
-  Has same shape as data, except for dimension 0 which
-  has size `k`, the number of segments.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.segment_sum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.segment_sum.md
new file mode 100644
index 0000000000..eeffe1601a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.segment_sum.md
@@ -0,0 +1,30 @@
+### `tf.segment_sum(data, segment_ids, name=None)` {#segment_sum}
+
+Computes the sum along segments of a tensor.
+
+Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation)
+for an explanation of segments.
+
+Computes a tensor such that
+\\(output_i = \sum_j data_j\\) where sum is over `j` such
+that `segment_ids[j] == i`.
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/SegmentSum.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`segment_ids`</b>: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+    A 1-D tensor whose rank is equal to the rank of `data`'s
+    first dimension.  Values should be sorted and can be repeated.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `data`.
+  Has same shape as data, except for dimension 0 which
+  has size `k`, the number of segments.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.shape_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.shape_n.md
deleted file mode 100644
index a229253406..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.shape_n.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.shape_n(input, name=None)` {#shape_n}
-
-Returns shape of tensors.
-
-This operation returns N 1-D integer tensors representing shape of `input[i]s`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A list of at least 1 `Tensor` objects of the same type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A list with the same number of `Tensor` objects as `input` of `Tensor` objects of type `int32`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_fill_empty_rows.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_fill_empty_rows.md
new file mode 100644
index 0000000000..3ea1697f3d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_fill_empty_rows.md
@@ -0,0 +1,54 @@
+### `tf.sparse_fill_empty_rows(sp_input, default_value, name=None)` {#sparse_fill_empty_rows}
+
+Fills empty rows in the input 2-D `SparseTensor` with a default value.
+
+This op adds entries with the specified `default_value` at index
+`[row, 0]` for any row in the input that does not already have a value.
+
+For example, suppose `sp_input` has shape `[5, 6]` and non-empty values:
+
+    [0, 1]: a
+    [0, 3]: b
+    [2, 0]: c
+    [3, 1]: d
+
+Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values:
+
+    [0, 1]: a
+    [0, 3]: b
+    [1, 0]: default_value
+    [2, 0]: c
+    [3, 1]: d
+    [4, 0]: default_value
+
+Note that the input may have empty columns at the end, with no effect on
+this op.
+
+The output `SparseTensor` will be in row-major order and will have the
+same shape as the input.
+
+This op also returns an indicator vector such that
+
+    empty_row_indicator[i] = True iff row i was an empty row.
+
+##### Args:
+
+
+*  <b>`sp_input`</b>: A `SparseTensor` with shape `[N, M]`.
+*  <b>`default_value`</b>: The value to fill for empty rows, with the same type as
+    `sp_input.`
+*  <b>`name`</b>: A name prefix for the returned tensors (optional)
+
+##### Returns:
+
+
+*  <b>`sp_ordered_output`</b>: A `SparseTensor` with shape `[N, M]`, and with all empty
+    rows filled in with `default_value`.
+*  <b>`empty_row_indicator`</b>: A bool vector of length `N` indicating whether each
+    input row was empty.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_placeholder.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_placeholder.md
new file mode 100644
index 0000000000..def6c8329d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_placeholder.md
@@ -0,0 +1,43 @@
+### `tf.sparse_placeholder(dtype, shape=None, name=None)` {#sparse_placeholder}
+
+Inserts a placeholder for a sparse tensor that will be always fed.
+
+**Important**: This sparse tensor will produce an error if evaluated.
+Its value must be fed using the `feed_dict` optional argument to
+`Session.run()`, `Tensor.eval()`, or `Operation.run()`.
+
+For example:
+
+```python
+x = tf.sparse_placeholder(tf.float32)
+y = tf.sparse_reduce_sum(x)
+
+with tf.Session() as sess:
+  print(sess.run(y))  # ERROR: will fail because x was not fed.
+
+  indices = np.array([[3, 2, 0], [4, 5, 1]], dtype=np.int64)
+  values = np.array([1.0, 2.0], dtype=np.float32)
+  shape = np.array([7, 9, 2], dtype=np.int64)
+  print(sess.run(y, feed_dict={
+    x: tf.SparseTensorValue(indices, values, shape)}))  # Will succeed.
+  print(sess.run(y, feed_dict={
+    x: (indices, values, shape)}))  # Will succeed.
+
+  sp = tf.SparseTensor(indices=indices, values=values, shape=shape)
+  sp_value = sp.eval(session)
+  print(sess.run(y, feed_dict={x: sp_value}))  # Will succeed.
+```
+
+##### Args:
+
+
+*  <b>`dtype`</b>: The type of `values` elements in the tensor to be fed.
+*  <b>`shape`</b>: The shape of the tensor to be fed (optional). If the shape is not
+    specified, you can feed a sparse tensor of any shape.
+*  <b>`name`</b>: A name for prefixing the operations (optional).
+
+##### Returns:
+
+  A `SparseTensor` that may be used as a handle for feeding a value, but not
+  evaluated directly.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_reorder.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_reorder.md
new file mode 100644
index 0000000000..1e7b8fd857
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_reorder.md
@@ -0,0 +1,41 @@
+### `tf.sparse_reorder(sp_input, name=None)` {#sparse_reorder}
+
+Reorders a `SparseTensor` into the canonical, row-major ordering.
+
+Note that by convention, all sparse ops preserve the canonical ordering
+along increasing dimension number. The only time ordering can be violated
+is during manual manipulation of the indices and values to add entries.
+
+Reordering does not affect the shape of the `SparseTensor`.
+
+For example, if `sp_input` has shape `[4, 5]` and `indices` / `values`:
+
+    [0, 3]: b
+    [0, 1]: a
+    [3, 1]: d
+    [2, 0]: c
+
+then the output will be a `SparseTensor` of shape `[4, 5]` and
+`indices` / `values`:
+
+    [0, 1]: a
+    [0, 3]: b
+    [2, 0]: c
+    [3, 1]: d
+
+##### Args:
+
+
+*  <b>`sp_input`</b>: The input `SparseTensor`.
+*  <b>`name`</b>: A name prefix for the returned tensors (optional)
+
+##### Returns:
+
+  A `SparseTensor` with the same shape and non-empty values, but in
+  canonical ordering.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_segment_sum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_segment_sum.md
new file mode 100644
index 0000000000..6691a6b7bc
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_segment_sum.md
@@ -0,0 +1,50 @@
+### `tf.sparse_segment_sum(data, indices, segment_ids, name=None)` {#sparse_segment_sum}
+
+Computes the sum along sparse segments of a tensor.
+
+Read [the section on
+Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
+of segments.
+
+Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first
+dimension, selecting a subset of dimension 0, specified by `indices`.
+
+For example:
+
+```prettyprint
+c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
+
+# Select two rows, one segment.
+tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
+  ==> [[0 0 0 0]]
+
+# Select two rows, two segment.
+tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
+  ==> [[ 1  2  3  4]
+       [-1 -2 -3 -4]]
+
+# Select all rows, two segments.
+tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
+  ==> [[0 0 0 0]
+       [5 6 7 8]]
+
+# Which is equivalent to:
+tf.segment_sum(c, tf.constant([0, 0, 1]))
+```
+
+##### Args:
+
+
+*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`indices`</b>: A `Tensor` of type `int32`.
+    A 1-D tensor. Has same rank as `segment_ids`.
+*  <b>`segment_ids`</b>: A `Tensor` of type `int32`.
+    A 1-D tensor. Values should be sorted and can be repeated.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `data`.
+  Has same shape as data, except for dimension 0 which
+  has size `k`, the number of segments.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_split.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_split.md
deleted file mode 100644
index e3e608a9e2..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.sparse_split.md
+++ /dev/null
@@ -1,40 +0,0 @@
-### `tf.sparse_split(split_dim, num_split, sp_input, name=None)` {#sparse_split}
-
-Split a `SparseTensor` into `num_split` tensors along `split_dim`.
-
-If the `sp_input.shape[split_dim]` is not an integer multiple of `num_split`
-each slice starting from 0:`shape[split_dim] % num_split` gets extra one
-dimension. For example, if `split_dim = 1` and `num_split = 2` and the
-input is:
-
-    input_tensor = shape = [2, 7]
-    [    a   d e  ]
-    [b c          ]
-
-Graphically the output tensors are:
-
-    output_tensor[0] =
-    [    a ]
-    [b c   ]
-
-    output_tensor[1] =
-    [ d e  ]
-    [      ]
-
-##### Args:
-
-
-*  <b>`split_dim`</b>: A 0-D `int32` `Tensor`. The dimension along which to split.
-*  <b>`num_split`</b>: A Python integer. The number of ways to split.
-*  <b>`sp_input`</b>: The `SparseTensor` to split.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  `num_split` `SparseTensor` objects resulting from splitting `value`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.string_to_hash_bucket.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.string_to_hash_bucket.md
new file mode 100644
index 0000000000..941d50e139
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.string_to_hash_bucket.md
@@ -0,0 +1,21 @@
+### `tf.string_to_hash_bucket(string_tensor, num_buckets, name=None)` {#string_to_hash_bucket}
+
+Converts each string in the input Tensor to its hash mod by a number of buckets.
+
+The hash function is deterministic on the content of the string within the
+process.
+
+Note that the hash function may change from time to time.
+
+##### Args:
+
+
+*  <b>`string_tensor`</b>: A `Tensor` of type `string`.
+*  <b>`num_buckets`</b>: An `int` that is `>= 1`. The number of buckets.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `int64`.
+  A Tensor of the same shape as the input `string_tensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.test.get_temp_dir.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.test.get_temp_dir.md
new file mode 100644
index 0000000000..e36d6163a7
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.test.get_temp_dir.md
@@ -0,0 +1,10 @@
+### `tf.test.get_temp_dir()` {#get_temp_dir}
+
+Returns a temporary directory for use during tests.
+
+There is no need to delete the directory after the test.
+
+##### Returns:
+
+  The temporary directory.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.test.is_built_with_cuda.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.test.is_built_with_cuda.md
deleted file mode 100644
index 51e3d97d8c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.test.is_built_with_cuda.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.test.is_built_with_cuda()` {#is_built_with_cuda}
-
-Returns whether TensorFlow was built with CUDA (GPU) support.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.tile.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.tile.md
new file mode 100644
index 0000000000..650f1f7eb8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.tile.md
@@ -0,0 +1,22 @@
+### `tf.tile(input, multiples, name=None)` {#tile}
+
+Constructs a tensor by tiling a given tensor.
+
+This operation creates a new tensor by replicating `input` `multiples` times.
+The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
+and the values of `input` are replicated `multiples[i]` times along the 'i'th
+dimension. For example, tiling `[a b c d]` by `[2]` produces
+`[a b c d a b c d]`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. 1-D or higher.
+*  <b>`multiples`</b>: A `Tensor` of type `int32`.
+    1-D. Length must be the same as the number of dimensions in `input`
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.to_int64.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.to_int64.md
deleted file mode 100644
index 0762822b3d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.to_int64.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.to_int64(x, name='ToInt64')` {#to_int64}
-
-Casts a tensor to type `int64`.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor` or `SparseTensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` or `SparseTensor` with same shape as `x` with type `int64`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `x` cannot be cast to the `int64`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.AdagradOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.AdagradOptimizer.md
deleted file mode 100644
index 35e416386e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.AdagradOptimizer.md
+++ /dev/null
@@ -1,26 +0,0 @@
-Optimizer that implements the Adagrad algorithm.
-
-See this [paper](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).
-
-- - -
-
-#### `tf.train.AdagradOptimizer.__init__(learning_rate, initial_accumulator_value=0.1, use_locking=False, name='Adagrad')` {#AdagradOptimizer.__init__}
-
-Construct a new Adagrad optimizer.
-
-##### Args:
-
-
-*  <b>`learning_rate`</b>: A `Tensor` or a floating point value.  The learning rate.
-*  <b>`initial_accumulator_value`</b>: A floating point value.
-    Starting value for the accumulators, must be positive.
-*  <b>`use_locking`</b>: If `True` use locks for update operations.
-*  <b>`name`</b>: Optional name prefix for the operations created when applying
-    gradients.  Defaults to "Adagrad".
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the `initial_accumulator_value` is invalid.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.AdamOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.AdamOptimizer.md
deleted file mode 100644
index 8667ec8ed3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.AdamOptimizer.md
+++ /dev/null
@@ -1,49 +0,0 @@
-Optimizer that implements the Adam algorithm.
-
-See [Kingma et. al., 2014](http://arxiv.org/abs/1412.6980)
-([pdf](http://arxiv.org/pdf/1412.6980.pdf)).
-
-- - -
-
-#### `tf.train.AdamOptimizer.__init__(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')` {#AdamOptimizer.__init__}
-
-Construct a new Adam optimizer.
-
-Initialization:
-
-```
-m_0 <- 0 (Initialize initial 1st moment vector)
-v_0 <- 0 (Initialize initial 2nd moment vector)
-t <- 0 (Initialize timestep)
-```
-
-The update rule for `variable` with gradient `g` uses an optimization
-described at the end of section2 of the paper:
-
-```
-t <- t + 1
-lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
-
-m_t <- beta1 * m_{t-1} + (1 - beta1) * g
-v_t <- beta2 * v_{t-1} + (1 - beta2) * g * g
-variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
-```
-
-The default value of 1e-8 for epsilon might not be a good default in
-general. For example, when training an Inception network on ImageNet a
-current good choice is 1.0 or 0.1.
-
-##### Args:
-
-
-*  <b>`learning_rate`</b>: A Tensor or a floating point value.  The learning rate.
-*  <b>`beta1`</b>: A float value or a constant float tensor.
-    The exponential decay rate for the 1st moment estimates.
-*  <b>`beta2`</b>: A float value or a constant float tensor.
-    The exponential decay rate for the 2nd moment estimates.
-*  <b>`epsilon`</b>: A small constant for numerical stability.
-*  <b>`use_locking`</b>: If True use locks for update operations.
-*  <b>`name`</b>: Optional name for the operations created when applying gradients.
-    Defaults to "Adam".
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.Coordinator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.Coordinator.md
deleted file mode 100644
index f51c0721ff..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.Coordinator.md
+++ /dev/null
@@ -1,223 +0,0 @@
-A coordinator for threads.
-
-This class implements a simple mechanism to coordinate the termination of a
-set of threads.
-
-#### Usage:
-
-```python
-# Create a coordinator.
-coord = Coordinator()
-# Start a number of threads, passing the coordinator to each of them.
-...start thread 1...(coord, ...)
-...start thread N...(coord, ...)
-# Wait for all the threads to terminate.
-coord.join(threads)
-```
-
-Any of the threads can call `coord.request_stop()` to ask for all the threads
-to stop.  To cooperate with the requests, each thread must check for
-`coord.should_stop()` on a regular basis.  `coord.should_stop()` returns
-`True` as soon as `coord.request_stop()` has been called.
-
-A typical thread running with a coordinator will do something like:
-
-```python
-while not coord.should_stop():
-  ...do some work...
-```
-
-#### Exception handling:
-
-A thread can report an exception to the coordinator as part of the
-`should_stop()` call.  The exception will be re-raised from the
-`coord.join()` call.
-
-Thread code:
-
-```python
-try:
-  while not coord.should_stop():
-    ...do some work...
-except Exception as e:
-  coord.request_stop(e)
-```
-
-Main code:
-
-```python
-try:
-  ...
-  coord = Coordinator()
-  # Start a number of threads, passing the coordinator to each of them.
-  ...start thread 1...(coord, ...)
-  ...start thread N...(coord, ...)
-  # Wait for all the threads to terminate.
-  coord.join(threads)
-except Exception as e:
-  ...exception that was passed to coord.request_stop()
-```
-
-To simplify the thread implementation, the Coordinator provides a
-context handler `stop_on_exception()` that automatically requests a stop if
-an exception is raised.  Using the context handler the thread code above
-can be written as:
-
-```python
-with coord.stop_on_exception():
-  while not coord.should_stop():
-    ...do some work...
-```
-
-#### Grace period for stopping:
-
-After a thread has called `coord.request_stop()` the other threads have a
-fixed time to stop, this is called the 'stop grace period' and defaults to 2
-minutes.  If any of the threads is still alive after the grace period expires
-`coord.join()` raises a RuntimeException reporting the laggards.
-
-```python
-try:
-  ...
-  coord = Coordinator()
-  # Start a number of threads, passing the coordinator to each of them.
-  ...start thread 1...(coord, ...)
-  ...start thread N...(coord, ...)
-  # Wait for all the threads to terminate, give them 10s grace period
-  coord.join(threads, stop_grace_period_secs=10)
-except RuntimeException:
-  ...one of the threads took more than 10s to stop after request_stop()
-  ...was called.
-except Exception:
-  ...exception that was passed to coord.request_stop()
-```
-- - -
-
-#### `tf.train.Coordinator.__init__()` {#Coordinator.__init__}
-
-Create a new Coordinator.
-
-
-- - -
-
-#### `tf.train.Coordinator.clear_stop()` {#Coordinator.clear_stop}
-
-Clears the stop flag.
-
-After this is called, calls to `should_stop()` will return `False`.
-
-
-- - -
-
-#### `tf.train.Coordinator.join(threads, stop_grace_period_secs=120)` {#Coordinator.join}
-
-Wait for threads to terminate.
-
-Blocks until all `threads` have terminated or `request_stop()` is called.
-
-After the threads stop, if an `exc_info` was passed to `request_stop`, that
-exception is re-raised.
-
-Grace period handling: When `request_stop()` is called, threads are given
-'stop_grace_period_secs' seconds to terminate.  If any of them is still
-alive after that period expires, a `RuntimeError` is raised.  Note that if
-an `exc_info` was passed to `request_stop()` then it is raised instead of
-that `RuntimeError`.
-
-##### Args:
-
-
-*  <b>`threads`</b>: List of `threading.Threads`. The started threads to join.
-*  <b>`stop_grace_period_secs`</b>: Number of seconds given to threads to stop after
-    `request_stop()` has been called.
-
-##### Raises:
-
-
-*  <b>`RuntimeError`</b>: If any thread is still alive after `request_stop()`
-    is called and the grace period expires.
-
-
-- - -
-
-#### `tf.train.Coordinator.request_stop(ex=None)` {#Coordinator.request_stop}
-
-Request that the threads stop.
-
-After this is called, calls to `should_stop()` will return `True`.
-
-Note: If an exception is being passed in, in must be in the context of
-handling the exception (i.e. `try: ... except Exception as ex: ...`) and not
-a newly created one.
-
-##### Args:
-
-
-*  <b>`ex`</b>: Optional `Exception`, or Python `exc_info` tuple as returned by
-    `sys.exc_info()`.  If this is the first call to `request_stop()` the
-    corresponding exception is recorded and re-raised from `join()`.
-
-
-- - -
-
-#### `tf.train.Coordinator.should_stop()` {#Coordinator.should_stop}
-
-Check if stop was requested.
-
-##### Returns:
-
-  True if a stop was requested.
-
-
-- - -
-
-#### `tf.train.Coordinator.stop_on_exception()` {#Coordinator.stop_on_exception}
-
-Context manager to request stop when an Exception is raised.
-
-Code that uses a coordinator must catch exceptions and pass
-them to the `request_stop()` method to stop the other threads
-managed by the coordinator.
-
-This context handler simplifies the exception handling.
-Use it as follows:
-
-```python
-with coord.stop_on_exception():
-  # Any exception raised in the body of the with
-  # clause is reported to the coordinator before terminating
-  # the execution of the body.
-  ...body...
-```
-
-This is completely equivalent to the slightly longer code:
-
-```python
-try:
-  ...body...
-exception Exception as ex:
-  coord.request_stop(ex)
-```
-
-##### Yields:
-
-  nothing.
-
-
-- - -
-
-#### `tf.train.Coordinator.wait_for_stop(timeout=None)` {#Coordinator.wait_for_stop}
-
-Wait till the Coordinator is told to stop.
-
-##### Args:
-
-
-*  <b>`timeout`</b>: Float.  Sleep for up to that many seconds waiting for
-    should_stop() to become True.
-
-##### Returns:
-
-  True if the Coordinator is told stop, False if the timeout expired.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.Server.create_local_server.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.Server.create_local_server.md
deleted file mode 100644
index f349dc0748..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.Server.create_local_server.md
+++ /dev/null
@@ -1,19 +0,0 @@
-#### `tf.train.Server.create_local_server(start=True)` {#Server.create_local_server}
-
-Creates a new single-process cluster running on the local host.
-
-This method is a convenience wrapper for creating a
-`tf.train.Server` with a `tf.train.ServerDef` that specifies a
-single-process cluster containing a single task in a job called
-`"local"`.
-
-##### Args:
-
-
-*  <b>`start`</b>: (Optional.) Boolean, indicating whether to start the server after
-    creating it. Defaults to `True`.
-
-##### Returns:
-
-  A local `tf.train.Server`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.add_queue_runner.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.add_queue_runner.md
new file mode 100644
index 0000000000..f5b9549ad8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.add_queue_runner.md
@@ -0,0 +1,18 @@
+### `tf.train.add_queue_runner(qr, collection='queue_runners')` {#add_queue_runner}
+
+Adds a `QueueRunner` to a collection in the graph.
+
+When building a complex model that uses many queues it is often difficult to
+gather all the queue runners that need to be run.  This convenience function
+allows you to add a queue runner to a well known collection in the graph.
+
+The companion method `start_queue_runners()` can be used to start threads for
+all the collected queue runners.
+
+##### Args:
+
+
+*  <b>`qr`</b>: A `QueueRunner`.
+*  <b>`collection`</b>: A `GraphKey` specifying the graph collection to add
+    the queue runner to.  Defaults to `GraphKeys.QUEUE_RUNNERS`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.exponential_decay.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.exponential_decay.md
deleted file mode 100644
index 2b8e72a0a2..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.exponential_decay.md
+++ /dev/null
@@ -1,54 +0,0 @@
-### `tf.train.exponential_decay(learning_rate, global_step, decay_steps, decay_rate, staircase=False, name=None)` {#exponential_decay}
-
-Applies exponential decay to the learning rate.
-
-When training a model, it is often recommended to lower the learning rate as
-the training progresses.  This function applies an exponential decay function
-to a provided initial learning rate.  It requires a `global_step` value to
-compute the decayed learning rate.  You can just pass a TensorFlow variable
-that you increment at each training step.
-
-The function returns the decayed learning rate.  It is computed as:
-
-```python
-decayed_learning_rate = learning_rate *
-                        decay_rate ^ (global_step / decay_steps)
-```
-
-If the argument `staircase` is `True`, then `global_step /decay_steps` is an
-integer division and the decayed learning rate follows a staircase function.
-
-Example: decay every 100000 steps with a base of 0.96:
-
-```python
-...
-global_step = tf.Variable(0, trainable=False)
-starter_learning_rate = 0.1
-learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step,
-                                           100000, 0.96, staircase=True)
-# Passing global_step to minimize() will increment it at each step.
-learning_step = (
-    tf.GradientDescentOptimizer(learning_rate)
-    .minimize(...my loss..., global_step=global_step)
-)
-```
-
-##### Args:
-
-
-*  <b>`learning_rate`</b>: A scalar `float32` or `float64` `Tensor` or a
-    Python number.  The initial learning rate.
-*  <b>`global_step`</b>: A scalar `int32` or `int64` `Tensor` or a Python number.
-    Global step to use for the decay computation.  Must not be negative.
-*  <b>`decay_steps`</b>: A scalar `int32` or `int64` `Tensor` or a Python number.
-    Must be positive.  See the decay computation above.
-*  <b>`decay_rate`</b>: A scalar `float32` or `float64` `Tensor` or a
-    Python number.  The decay rate.
-*  <b>`staircase`</b>: Boolean.  It `True` decay the learning rate at discrete intervals.
-*  <b>`name`</b>: String.  Optional name of the operation.  Defaults to 'ExponentialDecay'
-
-##### Returns:
-
-  A scalar `Tensor` of the same type as `learning_rate`.  The decayed
-  learning rate.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.global_step.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.global_step.md
deleted file mode 100644
index a53175be6a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.global_step.md
+++ /dev/null
@@ -1,27 +0,0 @@
-### `tf.train.global_step(sess, global_step_tensor)` {#global_step}
-
-Small helper to get the global step.
-
-```python
-# Creates a variable to hold the global_step.
-global_step_tensor = tf.Variable(10, trainable=False, name='global_step')
-# Creates a session.
-sess = tf.Session()
-# Initializes the variable.
-sess.run(global_step_tensor.initializer)
-print('global_step: %s' % tf.train.global_step(sess, global_step_tensor))
-
-global_step: 10
-```
-
-##### Args:
-
-
-*  <b>`sess`</b>: A TensorFlow `Session` object.
-*  <b>`global_step_tensor`</b>: `Tensor` or the `name` of the operation that contains
-    the global step.
-
-##### Returns:
-
-  The global step value.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.import_meta_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.import_meta_graph.md
deleted file mode 100644
index 21b3465076..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.import_meta_graph.md
+++ /dev/null
@@ -1,65 +0,0 @@
-### `tf.train.import_meta_graph(meta_graph_or_file)` {#import_meta_graph}
-
-Recreates a Graph saved in a `MetaGraphDef` proto.
-
-This function takes a `MetaGraphDef` protocol buffer as input. If
-the argument is a file containing a `MetaGraphDef` protocol buffer ,
-it constructs a protocol buffer from the file content. The function
-then adds all the nodes from the `graph_def` field to the
-current graph, recreates all the collections, and returns a saver
-constructed from the `saver_def` field.
-
-In combination with `export_meta_graph()`, this function can be used to
-
-* Serialize a graph along with other Python objects such as `QueueRunner`,
-  `Variable` into a `MetaGraphDef`.
-
-* Restart training from a saved graph and checkpoints.
-
-* Run inference from a saved graph and checkpoints.
-
-```Python
-...
-# Create a saver.
-saver = tf.train.Saver(...variables...)
-# Remember the training_op we want to run by adding it to a collection.
-tf.add_to_collection('train_op', train_op)
-sess = tf.Session()
-for step in xrange(1000000):
-    sess.run(train_op)
-    if step % 1000 == 0:
-        # Saves checkpoint, which by default also exports a meta_graph
-        # named 'my-model-global_step.meta'.
-        saver.save(sess, 'my-model', global_step=step)
-```
-
-Later we can continue training from this saved `meta_graph` without building
-the model from scratch.
-
-```Python
-with tf.Session() as sess:
-  new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
-  new_saver.restore(sess, 'my-save-dir/my-model-10000')
-  # tf.get_collection() returns a list. In this example we only want the
-  # first one.
-  train_op = tf.get_collection('train_op')[0]
-  for step in xrange(1000000):
-    sess.run(train_op)
-```
-
-NOTE: Restarting training from saved `meta_graph` only works if the
-device assignments have not changed.
-
-##### Args:
-
-
-*  <b>`meta_graph_or_file`</b>: `MetaGraphDef` protocol buffer or filename (including
-    the path) containing a `MetaGraphDef`.
-
-##### Returns:
-
-  A saver constructed from `saver_def` in `MetaGraphDef` or None.
-
-  A None value is returned if no variables exist in the `MetaGraphDef`
-  (i.e., there are no variables to restore).
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.latest_checkpoint.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.latest_checkpoint.md
deleted file mode 100644
index b1fc87cdd7..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.latest_checkpoint.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.train.latest_checkpoint(checkpoint_dir, latest_filename=None)` {#latest_checkpoint}
-
-Finds the filename of latest saved checkpoint file.
-
-##### Args:
-
-
-*  <b>`checkpoint_dir`</b>: Directory where the variables were saved.
-*  <b>`latest_filename`</b>: Optional name for the protocol buffer file that
-    contains the list of most recent checkpoint filenames.
-    See the corresponding argument to `Saver.save()`.
-
-##### Returns:
-
-  The full path to the latest checkpoint or `None` if no checkpoint was found.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.match_filenames_once.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.match_filenames_once.md
new file mode 100644
index 0000000000..6c84221cc5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.match_filenames_once.md
@@ -0,0 +1,14 @@
+### `tf.train.match_filenames_once(pattern, name=None)` {#match_filenames_once}
+
+Save the list of files matching pattern, so it is only computed once.
+
+##### Args:
+
+
+*  <b>`pattern`</b>: A file pattern (glob).
+*  <b>`name`</b>: A name for the operations (optional).
+
+##### Returns:
+
+  A variable that is initialized to the list of files matching pattern.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.summary_iterator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.summary_iterator.md
deleted file mode 100644
index 5702571441..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.train.summary_iterator.md
+++ /dev/null
@@ -1,42 +0,0 @@
-### `tf.train.summary_iterator(path)` {#summary_iterator}
-
-An iterator for reading `Event` protocol buffers from an event file.
-
-You can use this function to read events written to an event file. It returns
-a Python iterator that yields `Event` protocol buffers.
-
-Example: Print the contents of an events file.
-
-```python
-for e in tf.train.summary_iterator(path to events file):
-    print(e)
-```
-
-Example: Print selected summary values.
-
-```python
-# This example supposes that the events file contains summaries with a
-# summary value tag 'loss'.  These could have been added by calling
-# `add_summary()`, passing the output of a scalar summary op created with
-# with: `tf.scalar_summary(['loss'], loss_tensor)`.
-for e in tf.train.summary_iterator(path to events file):
-    for v in e.summary.value:
-        if v.tag == 'loss':
-            print(v.simple_value)
-```
-
-See the protocol buffer definitions of
-[Event](https://www.tensorflow.org/code/tensorflow/core/util/event.proto)
-and
-[Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-for more information about their attributes.
-
-##### Args:
-
-
-*  <b>`path`</b>: The path to an event file created by a `SummaryWriter`.
-
-##### Yields:
-
-  `Event` protocol buffers.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.trainable_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.trainable_variables.md
deleted file mode 100644
index 894d64a2b4..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.trainable_variables.md
+++ /dev/null
@@ -1,13 +0,0 @@
-### `tf.trainable_variables()` {#trainable_variables}
-
-Returns all variables created with `trainable=True`.
-
-When passed `trainable=True`, the `Variable()` constructor automatically
-adds new variables to the graph collection
-`GraphKeys.TRAINABLE_VARIABLES`. This convenience function returns the
-contents of that collection.
-
-##### Returns:
-
-  A list of Variable objects.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.unique.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.unique.md
deleted file mode 100644
index 0929f57b0f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.unique.md
+++ /dev/null
@@ -1,33 +0,0 @@
-### `tf.unique(x, name=None)` {#unique}
-
-Finds unique elements in a 1-D tensor.
-
-This operation returns a tensor `y` containing all of the unique elements of `x`
-sorted in the same order that they occur in `x`. This operation also returns a
-tensor `idx` the same size as `x` that contains the index of each value of `x`
-in the unique output `y`. In other words:
-
-`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
-
-For example:
-
-```prettyprint
-# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
-y, idx = unique(x)
-y ==> [1, 2, 4, 7, 8]
-idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
-```
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. 1-D.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of `Tensor` objects (y, idx).
-
-*  <b>`y`</b>: A `Tensor`. Has the same type as `x`. 1-D.
-*  <b>`idx`</b>: A `Tensor` of type `int32`. 1-D.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.verify_tensor_all_finite.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.verify_tensor_all_finite.md
deleted file mode 100644
index 37fa105df5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.verify_tensor_all_finite.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.verify_tensor_all_finite(t, msg, name=None)` {#verify_tensor_all_finite}
-
-Assert that the tensor does not contain any NaN's or Inf's.
-
-##### Args:
-
-
-*  <b>`t`</b>: Tensor to check.
-*  <b>`msg`</b>: Message to log on failure.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  Same tensor as `t`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.zeta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.zeta.md
new file mode 100644
index 0000000000..ed66237d38
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.zeta.md
@@ -0,0 +1,21 @@
+### `tf.zeta(x, q, name=None)` {#zeta}
+
+Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
+
+The Hurwitz zeta function is defined as:
+
+```
+\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}
+```
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`q`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.DeviceSpec.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.DeviceSpec.md
new file mode 100644
index 0000000000..18c651a45d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.DeviceSpec.md
@@ -0,0 +1,146 @@
+Represents a (possibly partial) specification for a TensorFlow device.
+
+`DeviceSpec`s are used throughout TensorFlow to describe where state is stored
+and computations occur. Using `DeviceSpec` allows you to parse device spec
+strings to verify their validity, merge them or compose them programmatically.
+
+Example:
+```python
+# Place the operations on device "GPU:0" in the "ps" job.
+device_spec = DeviceSpec(job="ps", device_type="GPU", device_index=0)
+with tf.device(device_spec):
+  # Both my_var and squared_var will be placed on /job:ps/device:GPU:0.
+  my_var = tf.Variable(..., name="my_variable")
+  squared_var = tf.square(my_var)
+```
+
+If a `DeviceSpec` is partially specified, it will be merged with other
+`DeviceSpec`s according to the scope in which it is defined. `DeviceSpec`
+components defined in inner scopes take precedence over those defined in
+outer scopes.
+
+```python
+with tf.device(DeviceSpec(job="train", )):
+  with tf.device(DeviceSpec(job="ps", device_type="GPU", device_index=0):
+    # Nodes created here will be assigned to /job:ps/device:GPU:0.
+  with tf.device(DeviceSpec(device_type="GPU", device_index=1):
+    # Nodes created here will be assigned to /job:train/device:GPU:1.
+```
+
+A `DeviceSpec` consists of 5 components -- each of
+which is optionally specified:
+
+* Job: The job name.
+* Replica: The replica index.
+* Task: The task index.
+* Device type: The device type string (e.g. "CPU" or "GPU").
+* Device index: The device index.
+- - -
+
+#### `tf.DeviceSpec.__init__(job=None, replica=None, task=None, device_type=None, device_index=None)` {#DeviceSpec.__init__}
+
+Create a new `DeviceSpec` object.
+
+##### Args:
+
+
+*  <b>`job`</b>: string.  Optional job name.
+*  <b>`replica`</b>: int.  Optional replica index.
+*  <b>`task`</b>: int.  Optional task index.
+*  <b>`device_type`</b>: Optional device type string (e.g. "CPU" or "GPU")
+*  <b>`device_index`</b>: int.  Optional device index.  If left
+    unspecified, device represents 'any' device_index.
+
+
+- - -
+
+#### `tf.DeviceSpec.from_string(spec)` {#DeviceSpec.from_string}
+
+Construct a `DeviceSpec` from a string.
+
+##### Args:
+
+
+*  <b>`spec`</b>: a string of the form
+   /job:<name>/replica:<id>/task:<id>/device:CPU:<id>
+  or
+   /job:<name>/replica:<id>/task:<id>/device:GPU:<id>
+  as cpu and gpu are mutually exclusive.
+  All entries are optional.
+
+##### Returns:
+
+  A DeviceSpec.
+
+
+- - -
+
+#### `tf.DeviceSpec.job` {#DeviceSpec.job}
+
+
+
+
+- - -
+
+#### `tf.DeviceSpec.merge_from(dev)` {#DeviceSpec.merge_from}
+
+Merge the properties of "dev" into this `DeviceSpec`.
+
+##### Args:
+
+
+*  <b>`dev`</b>: a `DeviceSpec`.
+
+
+- - -
+
+#### `tf.DeviceSpec.parse_from_string(spec)` {#DeviceSpec.parse_from_string}
+
+Parse a `DeviceSpec` name into its components.
+
+##### Args:
+
+
+*  <b>`spec`</b>: a string of the form
+   /job:<name>/replica:<id>/task:<id>/device:CPU:<id>
+  or
+   /job:<name>/replica:<id>/task:<id>/device:GPU:<id>
+  as cpu and gpu are mutually exclusive.
+  All entries are optional.
+
+##### Returns:
+
+  The `DeviceSpec`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the spec was not valid.
+
+
+- - -
+
+#### `tf.DeviceSpec.replica` {#DeviceSpec.replica}
+
+
+
+
+- - -
+
+#### `tf.DeviceSpec.task` {#DeviceSpec.task}
+
+
+
+
+- - -
+
+#### `tf.DeviceSpec.to_string()` {#DeviceSpec.to_string}
+
+Return a string representation of this `DeviceSpec`.
+
+##### Returns:
+
+  a string of the form
+  /job:<name>/replica:<id>/task:<id>/device:<device_type>:<id>.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.FixedLenFeature.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.FixedLenFeature.md
deleted file mode 100644
index 0ae54940a8..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.FixedLenFeature.md
+++ /dev/null
@@ -1,31 +0,0 @@
-Configuration for parsing a fixed-length input feature.
-
-To treat sparse input as dense, provide a `default_value`; otherwise,
-the parse functions will fail on any examples missing this feature.
-
-Fields:
-  shape: Shape of input data.
-  dtype: Data type of input.
-  default_value: Value to be used if an example is missing this feature. It
-      must be compatible with `dtype`.
-- - -
-
-#### `tf.FixedLenFeature.default_value` {#FixedLenFeature.default_value}
-
-Alias for field number 2
-
-
-- - -
-
-#### `tf.FixedLenFeature.dtype` {#FixedLenFeature.dtype}
-
-Alias for field number 1
-
-
-- - -
-
-#### `tf.FixedLenFeature.shape` {#FixedLenFeature.shape}
-
-Alias for field number 0
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.InteractiveSession.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.InteractiveSession.md
deleted file mode 100644
index cdb5101815..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.InteractiveSession.md
+++ /dev/null
@@ -1,68 +0,0 @@
-A TensorFlow `Session` for use in interactive contexts, such as a shell.
-
-The only difference with a regular `Session` is that an `InteractiveSession`
-installs itself as the default session on construction.
-The methods [`Tensor.eval()`](../../api_docs/python/framework.md#Tensor.eval)
-and [`Operation.run()`](../../api_docs/python/framework.md#Operation.run)
-will use that session to run ops.
-
-This is convenient in interactive shells and [IPython
-notebooks](http://ipython.org), as it avoids having to pass an explicit
-`Session` object to run ops.
-
-For example:
-
-```python
-sess = tf.InteractiveSession()
-a = tf.constant(5.0)
-b = tf.constant(6.0)
-c = a * b
-# We can just use 'c.eval()' without passing 'sess'
-print(c.eval())
-sess.close()
-```
-
-Note that a regular session installs itself as the default session when it
-is created in a `with` statement.  The common usage in non-interactive
-programs is to follow that pattern:
-
-```python
-a = tf.constant(5.0)
-b = tf.constant(6.0)
-c = a * b
-with tf.Session():
-  # We can also use 'c.eval()' here.
-  print(c.eval())
-```
-
-- - -
-
-#### `tf.InteractiveSession.__init__(target='', graph=None, config=None)` {#InteractiveSession.__init__}
-
-Creates a new interactive TensorFlow session.
-
-If no `graph` argument is specified when constructing the session,
-the default graph will be launched in the session. If you are
-using more than one graph (created with `tf.Graph()` in the same
-process, you will have to use different sessions for each graph,
-but each graph can be used in multiple sessions. In this case, it
-is often clearer to pass the graph to be launched explicitly to
-the session constructor.
-
-##### Args:
-
-
-*  <b>`target`</b>: (Optional.) The execution engine to connect to.
-    Defaults to using an in-process engine. At present, no value
-    other than the empty string is supported.
-*  <b>`graph`</b>: (Optional.) The `Graph` to be launched (described above).
-*  <b>`config`</b>: (Optional) `ConfigProto` proto used to configure the session.
-
-
-- - -
-
-#### `tf.InteractiveSession.close()` {#InteractiveSession.close}
-
-Closes an `InteractiveSession`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.OpError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.OpError.md
deleted file mode 100644
index c23014ad17..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.OpError.md
+++ /dev/null
@@ -1,62 +0,0 @@
-A generic error that is raised when TensorFlow execution fails.
-
-Whenever possible, the session will raise a more specific subclass
-of `OpError` from the `tf.errors` module.
-
-- - -
-
-#### `tf.OpError.op` {#OpError.op}
-
-The operation that failed, if known.
-
-*N.B.* If the failed op was synthesized at runtime, e.g. a `Send`
-or `Recv` op, there will be no corresponding
-[`Operation`](../../api_docs/python/framework.md#Operation)
-object.  In that case, this will return `None`, and you should
-instead use the [`OpError.node_def`](#OpError.node_def) to
-discover information about the op.
-
-##### Returns:
-
-  The `Operation` that failed, or None.
-
-
-- - -
-
-#### `tf.OpError.node_def` {#OpError.node_def}
-
-The `NodeDef` proto representing the op that failed.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.OpError.__init__(node_def, op, message, error_code)` {#OpError.__init__}
-
-Creates a new `OpError` indicating that a particular op failed.
-
-##### Args:
-
-
-*  <b>`node_def`</b>: The `graph_pb2.NodeDef` proto representing the op that failed,
-    if known; otherwise None.
-*  <b>`op`</b>: The `ops.Operation` that failed, if known; otherwise None.
-*  <b>`message`</b>: The message string describing the failure.
-*  <b>`error_code`</b>: The `error_codes_pb2.Code` describing the error.
-
-
-- - -
-
-#### `tf.OpError.error_code` {#OpError.error_code}
-
-The integer error code that describes the error.
-
-
-- - -
-
-#### `tf.OpError.message` {#OpError.message}
-
-The error message that describes the error.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.Operation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.Operation.md
deleted file mode 100644
index a9e21fb29e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.Operation.md
+++ /dev/null
@@ -1,225 +0,0 @@
-Represents a graph node that performs computation on tensors.
-
-An `Operation` is a node in a TensorFlow `Graph` that takes zero or
-more `Tensor` objects as input, and produces zero or more `Tensor`
-objects as output. Objects of type `Operation` are created by
-calling a Python op constructor (such as
-[`tf.matmul()`](../../api_docs/python/math_ops.md#matmul))
-or [`Graph.create_op()`](../../api_docs/python/framework.md#Graph.create_op).
-
-For example `c = tf.matmul(a, b)` creates an `Operation` of type
-"MatMul" that takes tensors `a` and `b` as input, and produces `c`
-as output.
-
-After the graph has been launched in a session, an `Operation` can
-be executed by passing it to
-[`Session.run()`](../../api_docs/python/client.md#Session.run).
-`op.run()` is a shortcut for calling `tf.get_default_session().run(op)`.
-
-- - -
-
-#### `tf.Operation.name` {#Operation.name}
-
-The full name of this operation.
-
-
-- - -
-
-#### `tf.Operation.type` {#Operation.type}
-
-The type of the op (e.g. `"MatMul"`).
-
-
-- - -
-
-#### `tf.Operation.inputs` {#Operation.inputs}
-
-The list of `Tensor` objects representing the data inputs of this op.
-
-
-- - -
-
-#### `tf.Operation.control_inputs` {#Operation.control_inputs}
-
-The `Operation` objects on which this op has a control dependency.
-
-Before this op is executed, TensorFlow will ensure that the
-operations in `self.control_inputs` have finished executing. This
-mechanism can be used to run ops sequentially for performance
-reasons, or to ensure that the side effects of an op are observed
-in the correct order.
-
-##### Returns:
-
-  A list of `Operation` objects.
-
-
-- - -
-
-#### `tf.Operation.outputs` {#Operation.outputs}
-
-The list of `Tensor` objects representing the outputs of this op.
-
-
-- - -
-
-#### `tf.Operation.device` {#Operation.device}
-
-The name of the device to which this op has been assigned, if any.
-
-##### Returns:
-
-  The string name of the device to which this op has been
-  assigned, or an empty string if it has not been assigned to a
-  device.
-
-
-- - -
-
-#### `tf.Operation.graph` {#Operation.graph}
-
-The `Graph` that contains this operation.
-
-
-
-- - -
-
-#### `tf.Operation.run(feed_dict=None, session=None)` {#Operation.run}
-
-Runs this operation in a `Session`.
-
-Calling this method will execute all preceding operations that
-produce the inputs needed for this operation.
-
-*N.B.* Before invoking `Operation.run()`, its graph must have been
-launched in a session, and either a default session must be
-available, or `session` must be specified explicitly.
-
-##### Args:
-
-
-*  <b>`feed_dict`</b>: A dictionary that maps `Tensor` objects to feed values.
-    See [`Session.run()`](../../api_docs/python/client.md#Session.run)
-    for a description of the valid feed values.
-*  <b>`session`</b>: (Optional.) The `Session` to be used to run to this operation. If
-    none, the default session will be used.
-
-
-
-- - -
-
-#### `tf.Operation.get_attr(name)` {#Operation.get_attr}
-
-Returns the value of the attr of this op with the given `name`.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name of the attr to fetch.
-
-##### Returns:
-
-  The value of the attr, as a Python object.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If this op does not have an attr with the given `name`.
-
-
-- - -
-
-#### `tf.Operation.traceback` {#Operation.traceback}
-
-Returns the call stack from when this operation was constructed.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.Operation.__init__(node_def, g, inputs=None, output_types=None, control_inputs=None, input_types=None, original_op=None, op_def=None)` {#Operation.__init__}
-
-Creates an `Operation`.
-
-NOTE: This constructor validates the name of the `Operation` (passed
-as `node_def.name`). Valid `Operation` names match the following
-regular expression:
-
-    [A-Za-z0-9.][A-Za-z0-9_.\-/]*
-
-##### Args:
-
-
-*  <b>`node_def`</b>: `graph_pb2.NodeDef`.  `NodeDef` for the `Operation`.
-    Used for attributes of `graph_pb2.NodeDef`, typically `name`,
-    `op`, and `device`.  The `input` attribute is irrelevant here
-    as it will be computed when generating the model.
-*  <b>`g`</b>: `Graph`. The parent graph.
-*  <b>`inputs`</b>: list of `Tensor` objects. The inputs to this `Operation`.
-*  <b>`output_types`</b>: list of `DType` objects.  List of the types of the
-    `Tensors` computed by this operation.  The length of this list indicates
-    the number of output endpoints of the `Operation`.
-*  <b>`control_inputs`</b>: list of operations or tensors from which to have a
-    control dependency.
-*  <b>`input_types`</b>: List of `DType` objects representing the
-    types of the tensors accepted by the `Operation`.  By default
-    uses `[x.dtype.base_dtype for x in inputs]`.  Operations that expect
-    reference-typed inputs must specify these explicitly.
-*  <b>`original_op`</b>: Optional. Used to associate the new `Operation` with an
-    existing `Operation` (for example, a replica with the op that was
-    replicated).
-*  <b>`op_def`</b>: Optional. The `op_def_pb2.OpDef` proto that describes the
-    op type that this `Operation` represents.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if control inputs are not Operations or Tensors,
-    or if `node_def` is not a `NodeDef`,
-    or if `g` is not a `Graph`,
-    or if `inputs` are not tensors,
-    or if `inputs` and `input_types` are incompatible.
-*  <b>`ValueError`</b>: if the `node_def` name is not valid.
-
-
-- - -
-
-#### `tf.Operation.colocation_groups()` {#Operation.colocation_groups}
-
-Returns the list of colocation groups of the op.
-
-
-- - -
-
-#### `tf.Operation.node_def` {#Operation.node_def}
-
-Returns a serialized `NodeDef` representation of this operation.
-
-##### Returns:
-
-  A
-  [`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
-  protocol buffer.
-
-
-- - -
-
-#### `tf.Operation.op_def` {#Operation.op_def}
-
-Returns the `OpDef` proto that represents the type of this op.
-
-##### Returns:
-
-  An
-  [`OpDef`](https://www.tensorflow.org/code/tensorflow/core/framework/op_def.proto)
-  protocol buffer.
-
-
-- - -
-
-#### `tf.Operation.values()` {#Operation.values}
-
-DEPRECATED: Use outputs.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.Print.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.Print.md
deleted file mode 100644
index b1ec7c1af0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.Print.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.Print(input_, data, message=None, first_n=None, summarize=None, name=None)` {#Print}
-
-Prints a list of tensors.
-
-This is an identity op with the side effect of printing `data` when
-evaluating.
-
-##### Args:
-
-
-*  <b>`input_`</b>: A tensor passed through this op.
-*  <b>`data`</b>: A list of tensors to print out when op is evaluated.
-*  <b>`message`</b>: A string, prefix of the error message.
-*  <b>`first_n`</b>: Only log `first_n` number of times. Negative numbers log always;
-           this is the default.
-*  <b>`summarize`</b>: Only print this many entries of each tensor. If None, then a
-             maximum of 3 elements are printed per input tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  Same tensor as `input_`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.ReaderBase.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.ReaderBase.md
deleted file mode 100644
index bc9f62de5a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.ReaderBase.md
+++ /dev/null
@@ -1,156 +0,0 @@
-Base class for different Reader types, that produce a record every step.
-
-Conceptually, Readers convert string 'work units' into records (key,
-value pairs).  Typically the 'work units' are filenames and the
-records are extracted from the contents of those files.  We want a
-single record produced per step, but a work unit can correspond to
-many records.
-
-Therefore we introduce some decoupling using a queue.  The queue
-contains the work units and the Reader dequeues from the queue when
-it is asked to produce a record (via Read()) but it has finished the
-last work unit.
-- - -
-
-#### `tf.ReaderBase.__init__(reader_ref, supports_serialize=False)` {#ReaderBase.__init__}
-
-Creates a new ReaderBase.
-
-##### Args:
-
-
-*  <b>`reader_ref`</b>: The operation that implements the reader.
-*  <b>`supports_serialize`</b>: True if the reader implementation can
-    serialize its state.
-
-
-- - -
-
-#### `tf.ReaderBase.num_records_produced(name=None)` {#ReaderBase.num_records_produced}
-
-Returns the number of records this reader has produced.
-
-This is the same as the number of Read executions that have
-succeeded.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.ReaderBase.num_work_units_completed(name=None)` {#ReaderBase.num_work_units_completed}
-
-Returns the number of work units this reader has finished processing.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  An int64 Tensor.
-
-
-- - -
-
-#### `tf.ReaderBase.read(queue, name=None)` {#ReaderBase.read}
-
-Returns the next record (key, value pair) produced by a reader.
-
-Will dequeue a work unit from queue if necessary (e.g. when the
-Reader needs to start reading from a new file since it has
-finished with the previous file).
-
-##### Args:
-
-
-*  <b>`queue`</b>: A Queue or a mutable string Tensor representing a handle
-    to a Queue, with string work items.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of Tensors (key, value).
-
-*  <b>`key`</b>: A string scalar Tensor.
-*  <b>`value`</b>: A string scalar Tensor.
-
-
-- - -
-
-#### `tf.ReaderBase.reader_ref` {#ReaderBase.reader_ref}
-
-Op that implements the reader.
-
-
-- - -
-
-#### `tf.ReaderBase.reset(name=None)` {#ReaderBase.reset}
-
-Restore a reader to its initial clean state.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.ReaderBase.restore_state(state, name=None)` {#ReaderBase.restore_state}
-
-Restore a reader to a previously saved state.
-
-Not all Readers support being restored, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`state`</b>: A string Tensor.
-    Result of a SerializeState of a Reader with matching type.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
-
-- - -
-
-#### `tf.ReaderBase.serialize_state(name=None)` {#ReaderBase.serialize_state}
-
-Produce a string tensor that encodes the state of a reader.
-
-Not all Readers support being serialized, so this can produce an
-Unimplemented error.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A string Tensor.
-
-
-- - -
-
-#### `tf.ReaderBase.supports_serialize` {#ReaderBase.supports_serialize}
-
-Whether the Reader implementation can serialize its state.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.RegisterShape.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.RegisterShape.md
new file mode 100644
index 0000000000..e3bb956f88
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.RegisterShape.md
@@ -0,0 +1,27 @@
+A decorator for registering the shape function for an op type.
+
+This decorator is only used when defining a new op type. A shape
+function is a function from an `Operation` object to a list of
+`TensorShape` objects, with one `TensorShape` for each output of the
+operation.
+
+For example, assuming that operations of type `"Sub"` take two
+inputs `x` and `y`, and return a single output `x - y`, all with the
+same shape, the following shape function would be registered:
+
+```python
+@tf.RegisterShape("Sub")
+def _sub_shape(op):
+  return [op.inputs[0].get_shape().merge_with(op.inputs[1].get_shape())]
+```
+
+The decorator argument `op_type` is the string type of an
+operation. This corresponds to the `OpDef.name` field for the proto
+that defines the operation.
+- - -
+
+#### `tf.RegisterShape.__init__(op_type)` {#RegisterShape.__init__}
+
+Saves the `op_type` as the `Operation` type.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.TensorShape.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.TensorShape.md
new file mode 100644
index 0000000000..506f44d838
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.TensorShape.md
@@ -0,0 +1,316 @@
+Represents the shape of a `Tensor`.
+
+A `TensorShape` represents a possibly-partial shape specification for a
+`Tensor`. It may be one of the following:
+
+* *Fully-known shape:* has a known number of dimensions and a known size
+  for each dimension.
+* *Partially-known shape:* has a known number of dimensions, and an unknown
+  size for one or more dimension.
+* *Unknown shape:* has an unknown number of dimensions, and an unknown
+  size in all dimensions.
+
+If a tensor is produced by an operation of type `"Foo"`, its shape
+may be inferred if there is a registered shape function for
+`"Foo"`. See [`tf.RegisterShape()`](../../api_docs/python/framework.md#RegisterShape)
+for details of shape
+functions and how to register them. Alternatively, the shape may be set
+explicitly using [`Tensor.set_shape()`](../../api_docs/python/framework.md#Tensor.set_shape).
+
+- - -
+
+#### `tf.TensorShape.merge_with(other)` {#TensorShape.merge_with}
+
+Returns a `TensorShape` combining the information in `self` and `other`.
+
+The dimensions in `self` and `other` are merged elementwise,
+according to the rules defined for `Dimension.merge_with()`.
+
+##### Args:
+
+
+*  <b>`other`</b>: Another `TensorShape`.
+
+##### Returns:
+
+  A `TensorShape` containing the combined information of `self` and
+  `other`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` and `other` are not compatible.
+
+
+- - -
+
+#### `tf.TensorShape.concatenate(other)` {#TensorShape.concatenate}
+
+Returns the concatenation of the dimension in `self` and `other`.
+
+*N.B.* If either `self` or `other` is completely unknown,
+concatenation will discard information about the other shape. In
+future, we might support concatenation that preserves this
+information for use with slicing.
+
+##### Args:
+
+
+*  <b>`other`</b>: Another `TensorShape`.
+
+##### Returns:
+
+  A `TensorShape` whose dimensions are the concatenation of the
+  dimensions in `self` and `other`.
+
+
+
+- - -
+
+#### `tf.TensorShape.ndims` {#TensorShape.ndims}
+
+Returns the rank of this shape, or None if it is unspecified.
+
+
+- - -
+
+#### `tf.TensorShape.dims` {#TensorShape.dims}
+
+Returns a list of Dimensions, or None if the shape is unspecified.
+
+
+- - -
+
+#### `tf.TensorShape.as_list()` {#TensorShape.as_list}
+
+Returns a list of integers or None for each dimension.
+
+##### Returns:
+
+  A list of integers or None for each dimension.
+
+
+- - -
+
+#### `tf.TensorShape.as_proto()` {#TensorShape.as_proto}
+
+Returns this shape as a `TensorShapeProto`.
+
+
+- - -
+
+#### `tf.TensorShape.is_compatible_with(other)` {#TensorShape.is_compatible_with}
+
+Returns True iff `self` is compatible with `other`.
+
+Two possibly-partially-defined shapes are compatible if there
+exists a fully-defined shape that both shapes can represent. Thus,
+compatibility allows the shape inference code to reason about
+partially-defined shapes. For example:
+
+* TensorShape(None) is compatible with all shapes.
+
+* TensorShape([None, None]) is compatible with all two-dimensional
+  shapes, such as TensorShape([32, 784]), and also TensorShape(None). It is
+  not compatible with, for example, TensorShape([None]) or
+  TensorShape([None, None, None]).
+
+* TensorShape([32, None]) is compatible with all two-dimensional shapes
+  with size 32 in the 0th dimension, and also TensorShape([None, None])
+  and TensorShape(None). It is not compatible with, for example,
+  TensorShape([32]), TensorShape([32, None, 1]) or TensorShape([64, None]).
+
+* TensorShape([32, 784]) is compatible with itself, and also
+  TensorShape([32, None]), TensorShape([None, 784]), TensorShape([None,
+  None]) and TensorShape(None). It is not compatible with, for example,
+  TensorShape([32, 1, 784]) or TensorShape([None]).
+
+The compatibility relation is reflexive and symmetric, but not
+transitive. For example, TensorShape([32, 784]) is compatible with
+TensorShape(None), and TensorShape(None) is compatible with
+TensorShape([4, 4]), but TensorShape([32, 784]) is not compatible with
+TensorShape([4, 4]).
+
+##### Args:
+
+
+*  <b>`other`</b>: Another TensorShape.
+
+##### Returns:
+
+  True iff `self` is compatible with `other`.
+
+
+- - -
+
+#### `tf.TensorShape.is_fully_defined()` {#TensorShape.is_fully_defined}
+
+Returns True iff `self` is fully defined in every dimension.
+
+
+
+- - -
+
+#### `tf.TensorShape.with_rank(rank)` {#TensorShape.with_rank}
+
+Returns a shape based on `self` with the given rank.
+
+This method promotes a completely unknown shape to one with a
+known rank.
+
+##### Args:
+
+
+*  <b>`rank`</b>: An integer.
+
+##### Returns:
+
+  A shape that is at least as specific as `self` with the given rank.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` does not represent a shape with the given `rank`.
+
+
+- - -
+
+#### `tf.TensorShape.with_rank_at_least(rank)` {#TensorShape.with_rank_at_least}
+
+Returns a shape based on `self` with at least the given rank.
+
+##### Args:
+
+
+*  <b>`rank`</b>: An integer.
+
+##### Returns:
+
+  A shape that is at least as specific as `self` with at least the given
+  rank.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` does not represent a shape with at least the given
+    `rank`.
+
+
+- - -
+
+#### `tf.TensorShape.with_rank_at_most(rank)` {#TensorShape.with_rank_at_most}
+
+Returns a shape based on `self` with at most the given rank.
+
+##### Args:
+
+
+*  <b>`rank`</b>: An integer.
+
+##### Returns:
+
+  A shape that is at least as specific as `self` with at most the given
+  rank.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` does not represent a shape with at most the given
+    `rank`.
+
+
+
+- - -
+
+#### `tf.TensorShape.assert_has_rank(rank)` {#TensorShape.assert_has_rank}
+
+Raises an exception if `self` is not compatible with the given `rank`.
+
+##### Args:
+
+
+*  <b>`rank`</b>: An integer.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` does not represent a shape with the given `rank`.
+
+
+- - -
+
+#### `tf.TensorShape.assert_same_rank(other)` {#TensorShape.assert_same_rank}
+
+Raises an exception if `self` and `other` do not have compatible ranks.
+
+##### Args:
+
+
+*  <b>`other`</b>: Another `TensorShape`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` and `other` do not represent shapes with the
+    same rank.
+
+
+- - -
+
+#### `tf.TensorShape.assert_is_compatible_with(other)` {#TensorShape.assert_is_compatible_with}
+
+Raises exception if `self` and `other` do not represent the same shape.
+
+This method can be used to assert that there exists a shape that both
+`self` and `other` represent.
+
+##### Args:
+
+
+*  <b>`other`</b>: Another TensorShape.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` and `other` do not represent the same shape.
+
+
+- - -
+
+#### `tf.TensorShape.assert_is_fully_defined()` {#TensorShape.assert_is_fully_defined}
+
+Raises an exception if `self` is not fully defined in every dimension.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `self` does not have a known value for every dimension.
+
+
+
+#### Other Methods
+- - -
+
+#### `tf.TensorShape.__init__(dims)` {#TensorShape.__init__}
+
+Creates a new TensorShape with the given dimensions.
+
+##### Args:
+
+
+*  <b>`dims`</b>: A list of Dimensions, or None if the shape is unspecified.
+*  <b>`DEPRECATED`</b>: A single integer is treated as a singleton list.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If dims cannot be converted to a list of dimensions.
+
+
+- - -
+
+#### `tf.TensorShape.num_elements()` {#TensorShape.num_elements}
+
+Returns the total number of elements, or none for incomplete shapes.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.VarLenFeature.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.VarLenFeature.md
new file mode 100644
index 0000000000..a7b49bfcd6
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.VarLenFeature.md
@@ -0,0 +1,11 @@
+Configuration for parsing a variable-length input feature.
+
+Fields:
+  dtype: Data type of input.
+- - -
+
+#### `tf.VarLenFeature.dtype` {#VarLenFeature.dtype}
+
+Alias for field number 0
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.abs.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.abs.md
new file mode 100644
index 0000000000..63a0b4c954
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.abs.md
@@ -0,0 +1,22 @@
+### `tf.abs(x, name=None)` {#abs}
+
+Computes the absolute value of a tensor.
+
+Given a tensor of real numbers `x`, this operation returns a tensor
+containing the absolute value of each element in `x`. For example, if x is
+an input element and y is an output element, this operation computes
+\\(y = |x|\\).
+
+See [`tf.complex_abs()`](#tf_complex_abs) to compute the absolute value of a complex
+number.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor` of type `float`, `double`, `int32`, or `int64`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+   A `Tensor` the same size and type as `x` with absolute values.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.add.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.add.md
deleted file mode 100644
index 738f0337d3..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.add.md
+++ /dev/null
@@ -1,17 +0,0 @@
-### `tf.add(x, y, name=None)` {#add}
-
-Returns x + y element-wise.
-
-*NOTE*: Add supports broadcasting. AddN does not.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`, `string`.
-*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.all_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.all_variables.md
deleted file mode 100644
index 904b99f321..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.all_variables.md
+++ /dev/null
@@ -1,12 +0,0 @@
-### `tf.all_variables()` {#all_variables}
-
-Returns all variables that must be saved/restored.
-
-The `Variable()` constructor automatically adds new variables to the graph
-collection `GraphKeys.VARIABLES`. This convenience function returns the
-contents of that collection.
-
-##### Returns:
-
-  A list of `Variable` objects.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.as_dtype.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.as_dtype.md
new file mode 100644
index 0000000000..50a048aacb
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.as_dtype.md
@@ -0,0 +1,21 @@
+### `tf.as_dtype(type_value)` {#as_dtype}
+
+Converts the given `type_value` to a `DType`.
+
+##### Args:
+
+
+*  <b>`type_value`</b>: A value that can be converted to a `tf.DType`
+    object. This may currently be a `tf.DType` object, a
+    [`DataType` enum](https://www.tensorflow.org/code/tensorflow/core/framework/types.proto),
+    a string type name, or a `numpy.dtype`.
+
+##### Returns:
+
+  A `DType` corresponding to `type_value`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `type_value` cannot be converted to a `DType`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.assert_less.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.assert_less.md
new file mode 100644
index 0000000000..eb43a62444
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.assert_less.md
@@ -0,0 +1,35 @@
+### `tf.assert_less(x, y, data=None, summarize=None, name=None)` {#assert_less}
+
+Assert the condition `x < y` holds element-wise.
+
+Example of adding a dependency to an operation:
+
+```python
+with tf.control_dependencies([tf.assert_less(x, y)]):
+  output = tf.reduce_sum(x)
+```
+
+Example of adding dependency to the tensor being checked:
+
+```python
+x = tf.with_dependencies([tf.assert_less(x, y)], x)
+```
+
+This condition holds if for every pair of (possibly broadcast) elements
+`x[i]`, `y[i]`, we have `x[i] < y[i]`.
+If both `x` and `y` are empty, this is trivially satisfied.
+
+##### Args:
+
+
+*  <b>`x`</b>: Numeric `Tensor`.
+*  <b>`y`</b>: Numeric `Tensor`, same dtype as and broadcastable to `x`.
+*  <b>`data`</b>: The tensors to print out if the condition is False.  Defaults to
+    error message and first few entries of `x`, `y`.
+*  <b>`summarize`</b>: Print this many entries of each tensor.
+*  <b>`name`</b>: A name for this operation (optional).  Defaults to "assert_less".
+
+##### Returns:
+
+  Op that raises `InvalidArgumentError` if `x < y` is False.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_cholesky_solve.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_cholesky_solve.md
new file mode 100644
index 0000000000..25fcc5c908
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_cholesky_solve.md
@@ -0,0 +1,35 @@
+### `tf.batch_cholesky_solve(chol, rhs, name=None)` {#batch_cholesky_solve}
+
+Solve batches of linear eqns `A X = RHS`, given Cholesky factorizations.
+
+```python
+# Solve one linear system (K = 1) for every member of the length 10 batch.
+A = ... # shape 10 x 2 x 2
+RHS = ... # shape 10 x 2 x 1
+chol = tf.batch_cholesky(A)  # shape 10 x 2 x 2
+X = tf.batch_cholesky_solve(chol, RHS)  # shape 10 x 2 x 1
+# tf.matmul(A, X) ~ RHS
+X[3, :, 0]  # Solution to the linear system A[3, :, :] x = RHS[3, :, 0]
+
+# Solve five linear systems (K = 5) for every member of the length 10 batch.
+A = ... # shape 10 x 2 x 2
+RHS = ... # shape 10 x 2 x 5
+...
+X[3, :, 2]  # Solution to the linear system A[3, :, :] x = RHS[3, :, 2]
+```
+
+##### Args:
+
+
+*  <b>`chol`</b>: A `Tensor`.  Must be `float32` or `float64`, shape is `[..., M, M]`.
+    Cholesky factorization of `A`, e.g. `chol = tf.batch_cholesky(A)`.
+    For that reason, only the lower triangular parts (including the diagonal)
+    of the last two dimensions of `chol` are used.  The strictly upper part is
+    assumed to be zero and not accessed.
+*  <b>`rhs`</b>: A `Tensor`, same type as `chol`, shape is `[..., M, K]`.
+*  <b>`name`</b>: A name to give this `Op`.  Defaults to `batch_cholesky_solve`.
+
+##### Returns:
+
+  Solution to `A x = rhs`, shape `[..., M, K]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_fft.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_fft.md
deleted file mode 100644
index c2ea3aa9c1..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_fft.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.batch_fft(input, name=None)` {#batch_fft}
-
-Compute the 1-dimensional discrete Fourier Transform over the inner-most
-
-dimension of `input`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `complex64`.
-  A complex64 tensor of the same shape as `input`. The inner-most
-  dimension of `input` is replaced with its 1D Fourier Transform.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_ifft3d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_ifft3d.md
new file mode 100644
index 0000000000..1173a17d6d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_ifft3d.md
@@ -0,0 +1,18 @@
+### `tf.batch_ifft3d(input, name=None)` {#batch_ifft3d}
+
+Compute the inverse 3-dimensional discrete Fourier Transform over the inner-most
+
+3 dimensions of `input`.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 tensor.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`.
+  A complex64 tensor of the same shape as `input`. The inner-most 3
+  dimensions of `input` are replaced with their inverse 3D Fourier Transform.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_matrix_diag_part.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_matrix_diag_part.md
deleted file mode 100644
index 0eb431d7a9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.batch_matrix_diag_part.md
+++ /dev/null
@@ -1,46 +0,0 @@
-### `tf.batch_matrix_diag_part(input, name=None)` {#batch_matrix_diag_part}
-
-Returns the batched diagonal part of a batched tensor.
-
-This operation returns a tensor with the `diagonal` part
-of the batched `input`. The `diagonal` part is computed as follows:
-
-Assume `input` has `k` dimensions `[I, J, K, ..., N, N]`, then the output is a
-tensor of rank `k - 1` with dimensions `[I, J, K, ..., N]` where:
-
-`diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`.
-
-The input must be at least a matrix.
-
-For example:
-
-```prettyprint
-# 'input' is [[[1, 0, 0, 0]
-               [0, 2, 0, 0]
-               [0, 0, 3, 0]
-               [0, 0, 0, 4]],
-              [[5, 0, 0, 0]
-               [0, 6, 0, 0]
-               [0, 0, 7, 0]
-               [0, 0, 0, 8]]]
-
-and input.shape = (2, 4, 4)
-
-tf.batch_matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]]
-
-which has shape (2, 4)
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`.
-    Rank `k` tensor where `k >= 2` and the last two dimensions are equal.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-  The extracted diagonal(s) having shape
-  `diagonal.shape = input.shape[:-1]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.bitcast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.bitcast.md
new file mode 100644
index 0000000000..4ded707a89
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.bitcast.md
@@ -0,0 +1,25 @@
+### `tf.bitcast(input, type, name=None)` {#bitcast}
+
+Bitcasts a tensor from one type to another without copying data.
+
+Given a tensor `input`, this operation returns a tensor that has the same buffer
+data as `input` with datatype `type`.
+
+If the input datatype `T` is larger than the output datatype `type` then the
+shape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)].
+
+If `T` is smaller than `type`, the operator requires that the rightmost
+dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from
+[..., sizeof(`type`)/sizeof(`T`)] to [...].
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
+*  <b>`type`</b>: A `tf.DType` from: `tf.float32, tf.float64, tf.int64, tf.int32, tf.uint8, tf.uint16, tf.int16, tf.int8, tf.complex64, tf.complex128, tf.qint8, tf.quint8, tf.qint32, tf.half`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `type`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.boolean_mask.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.boolean_mask.md
new file mode 100644
index 0000000000..e893b8ee63
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.boolean_mask.md
@@ -0,0 +1,43 @@
+### `tf.boolean_mask(tensor, mask, name='boolean_mask')` {#boolean_mask}
+
+Apply boolean mask to tensor.  Numpy equivalent is `tensor[mask]`.
+
+```python
+# 1-D example
+tensor = [0, 1, 2, 3]
+mask = [True, False, True, False]
+boolean_mask(tensor, mask) ==> [0, 2]
+```
+
+In general, `0 < dim(mask) = K <= dim(tensor)`, and `mask`'s shape must match
+the first K dimensions of `tensor`'s shape.  We then have:
+  `boolean_mask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iK,j1,...,jd]`
+where `(i1,...,iK)` is the ith `True` entry of `mask` (row-major order).
+
+##### Args:
+
+
+*  <b>`tensor`</b>: N-D tensor.
+*  <b>`mask`</b>: K-D boolean tensor, K <= N and K must be known statically.
+*  <b>`name`</b>: A name for this operation (optional).
+
+##### Returns:
+
+  Tensor populated by entries in `tensor` corresponding to `True` values in
+    `mask`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If shapes do not conform.
+
+
+*  <b>`Examples`</b>: 
+
+```python
+# 2-D example
+tensor = [[1, 2], [3, 4], [5, 6]]
+mask = [True, False, True]
+boolean_mask(tensor, mask) ==> [[1, 2], [5, 6]]
+```
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.case.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.case.md
deleted file mode 100644
index 9314837b8e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.case.md
+++ /dev/null
@@ -1,75 +0,0 @@
-### `tf.case(pred_fn_pairs, default, exclusive=False, name='case')` {#case}
-
-Create a case operation.
-
-The `pred_fn_pairs` parameter is a dict or list of pairs of size N.
-Each pair contains a boolean scalar tensor and a python callable that
-creates the tensors to be returned if the boolean evaluates to True.
-`default` is a callable generating a list of tensors. All the callables
-in `pred_fn_pairs` as well as `default` should return the same number
-and types of tensors.
-
-If `exclusive==True`, all predicates are evaluated, and a logging operation
-with an error is returned if more than one of the predicates evaluates to
-True. If `exclusive==False`, execution stops are the first predicate which
-evaluates to True, and the tensors generated by the corresponding function
-are returned immediately. If none of the predicates evaluate to True, this
-operation returns the tensors generated by `default`.
-
-Example 1:
-  Pseudocode:
-  ```
-    if (x < y) return 17;
-    else return 23;
-  ```
-
-  Expressions:
-  ```
-    f1 = lambda: tf.constant(17)
-    f2 = lambda: tf.constant(23)
-    r = case([(tf.less(x, y), f1)], default=f2)
-  ```
-
-Example 2:
-  Pseudocode:
-  ```
-    if (x < y && x > z) raise OpError("Only one predicate may evaluate true");
-    if (x < y) return 17;
-    else if (x > z) return 23;
-    else return -1;
-  ```
-
-  Expressions:
-  ```
-    x = tf.constant(0)
-    y = tf.constant(1)
-    z = tf.constant(2)
-    def f1(): return tf.constant(17)
-    def f2(): return tf.constant(23)
-    def f3(): return tf.constant(-1)
-    r = case({tf.less(x, y): f1, tf.greater(x, z): f2},
-             default=f3, exclusive=True)
-  ```
-
-##### Args:
-
-
-*  <b>`pred_fn_pairs`</b>: Dict or list of pairs of a boolean scalar tensor and a
-                 callable which returns a list of tensors.
-*  <b>`default`</b>: A callable that returns a list of tensors.
-*  <b>`exclusive`</b>: True iff more than one predicate is allowed to evaluate to True.
-*  <b>`name`</b>: A name for this operation (optional).
-
-##### Returns:
-
-  The tensors returned by the first pair whose predicate evaluated to True, or
-  those returned by `default` if none does.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `pred_fn_pairs` is not a list/dictionary.
-*  <b>`TypeError`</b>: If `pred_fn_pairs` is a list but does not contain 2-tuples.
-*  <b>`TypeError`</b>: If `fns[i]` is not callable for any i, or `default` is not
-             callable.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.cholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.cholesky.md
deleted file mode 100644
index 4032b80d8e..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.cholesky.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.cholesky(input, name=None)` {#cholesky}
-
-Calculates the Cholesky decomposition of a square matrix.
-
-The input has to be symmetric and positive definite. Only the lower-triangular
-part of the input will be used for this operation. The upper-triangular part
-will not be read.
-
-The result is the lower-triangular matrix of the Cholesky decomposition of the
-input, `L`, so that `input = L L^*`.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`. Must be one of the following types: `float64`, `float32`.
-    Shape is `[M, M]`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`. Shape is `[M, M]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.copy_graph.get_copied_op.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.copy_graph.get_copied_op.md
deleted file mode 100644
index 9e5a2118fd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.copy_graph.get_copied_op.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.contrib.copy_graph.get_copied_op(org_instance, graph, scope='')` {#get_copied_op}
-
-Given an `Operation` instance from some `Graph`, returns
-its namesake from `graph`, under the specified scope
-(default `""`).
-
-If a copy of `org_instance` is present in `graph` under the given
-`scope`, it will be returned.
-
-Args:
-org_instance: An `Operation` from some `Graph`.
-graph: The `Graph` to be searched for a copr of `org_instance`.
-scope: The scope `org_instance` is present in.
-
-##### Returns:
-
-    The `Operation` copy from `graph`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.DirichletMultinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.DirichletMultinomial.md
new file mode 100644
index 0000000000..1d8cb6a6dd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.DirichletMultinomial.md
@@ -0,0 +1,185 @@
+DirichletMultinomial mixture distribution.
+
+This distribution is parameterized by a vector `alpha` of concentration
+parameters for `k` classes.
+
+#### Mathematical details
+
+The Dirichlet Multinomial is a distribution over k-class count data, meaning
+for each k-tuple of non-negative integer `counts = [c_1,...,c_k]`, we have a
+probability of these draws being made from the distribution.  The distribution
+has hyperparameters `alpha = (alpha_1,...,alpha_k)`, and probability mass
+function (pmf):
+
+```pmf(counts) = C! / (c_1!...c_k!) * Beta(alpha + c) / Beta(alpha)```
+
+where above `C = sum_j c_j`, `N!` is `N` factorial, and
+`Beta(x) = prod_j Gamma(x_j) / Gamma(sum_j x_j)` is the multivariate beta
+function.
+
+This is a mixture distribution in that `N` samples can be produced by:
+  1. Choose class probabilities `p = (p_1,...,p_k) ~ Dir(alpha)`
+  2. Draw integers `m = (m_1,...,m_k) ~ Multinomial(p, N)`
+
+This class provides methods to create indexed batches of Dirichlet
+Multinomial distributions.  If the provided `alpha` is rank 2 or higher, for
+every fixed set of leading dimensions, the last dimension represents one
+single Dirichlet Multinomial distribution.  When calling distribution
+functions (e.g. `dist.pdf(counts)`), `alpha` and `counts` are broadcast to the
+same shape (if possible).  In all cases, the last dimension of alpha/counts
+represents single Dirichlet Multinomial distributions.
+
+#### Examples
+
+```python
+alpha = [1, 2, 3]
+dist = DirichletMultinomial(alpha)
+```
+
+Creates a 3-class distribution, with the 3rd class is most likely to be drawn.
+The distribution functions can be evaluated on counts.
+
+```python
+# counts same shape as alpha.
+counts = [0, 2, 0]
+dist.pdf(counts)  # Shape []
+
+# alpha will be broadcast to [[1, 2, 3], [1, 2, 3]] to match counts.
+counts = [[11, 22, 33], [44, 55, 66]]
+dist.pdf(counts)  # Shape [2]
+
+# alpha will be broadcast to shape [5, 7, 3] to match counts.
+counts = [[...]]  # Shape [5, 7, 3]
+dist.pdf(counts)  # Shape [5, 7]
+```
+
+Creates a 2-batch of 3-class distributions.
+
+```python
+alpha = [[1, 2, 3], [4, 5, 6]]  # Shape [2, 3]
+dist = DirichletMultinomial(alpha)
+
+# counts will be broadcast to [[11, 22, 33], [11, 22, 33]] to match alpha.
+counts = [11, 22, 33]
+dist.pdf(counts)  # Shape [2]
+```
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.__init__(alpha)` {#DirichletMultinomial.__init__}
+
+Initialize a batch of DirichletMultinomial distributions.
+
+##### Args:
+
+
+*  <b>`alpha`</b>: Shape `[N1,..., Nn, k]` positive `float` or `double` tensor with
+    `n >= 0`.  Defines this as a batch of `N1 x ... x Nn` different `k`
+    class Dirichlet multinomial distributions.
+
+
+*  <b>`Examples`</b>: 
+
+```python
+# Define 1-batch of 2-class Dirichlet multinomial distribution,
+# also known as a beta-binomial.
+dist = DirichletMultinomial([1.1, 2.0])
+
+# Define a 2-batch of 3-class distributions.
+dist = DirichletMultinomial([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+```
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.alpha` {#DirichletMultinomial.alpha}
+
+Parameters defining this distribution.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.cdf(x)` {#DirichletMultinomial.cdf}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.dtype` {#DirichletMultinomial.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(x)` {#DirichletMultinomial.log_cdf}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.log_pmf(counts, name=None)` {#DirichletMultinomial.log_pmf}
+
+`Log(P[counts])`, computed for every batch member.
+
+For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
+that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
+distribution, the number of draws falling in class `j` is `c_j`.  Note that
+different sequences of draws can result in the same counts, thus the
+probability includes a combinatorial coefficient.
+
+##### Args:
+
+
+*  <b>`counts`</b>: Non-negative `float`, `double`, or `int` tensor whose shape can
+    be broadcast with `self.alpha`.  For fixed leading dimensions, the last
+    dimension represents counts for the corresponding Dirichlet Multinomial
+    distribution in `self.alpha`.
+*  <b>`name`</b>: Name to give this Op, defaults to "log_pmf".
+
+##### Returns:
+
+  Log probabilities for each record, shape `[N1,...,Nn]`.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.mean` {#DirichletMultinomial.mean}
+
+Class means for every batch member.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.num_classes` {#DirichletMultinomial.num_classes}
+
+Tensor providing number of classes in each batch member.
+
+
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.pmf(counts, name=None)` {#DirichletMultinomial.pmf}
+
+`P[counts]`, computed for every batch member.
+
+For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
+that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
+distribution, the number of draws falling in class `j` is `c_j`.  Note that
+different sequences of draws can result in the same counts, thus the
+probability includes a combinatorial coefficient.
+
+##### Args:
+
+
+*  <b>`counts`</b>: Non-negative `float`, `double`, or `int` tensor whose shape can
+    be broadcast with `self.alpha`.  For fixed leading dimensions, the last
+    dimension represents counts for the corresponding Dirichlet Multinomial
+    distribution in `self.alpha`.
+*  <b>`name`</b>: Name to give this Op, defaults to "pmf".
+
+##### Returns:
+
+  Probabilities for each record, shape `[N1,...,Nn]`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Normal.md
new file mode 100644
index 0000000000..d15dd93a65
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Normal.md
@@ -0,0 +1,209 @@
+The scalar Normal distribution with mean and stddev parameters mu, sigma.
+
+#### Mathematical details
+
+The PDF of this distribution is:
+
+```f(x) = sqrt(1/(2*pi*sigma^2)) exp(-(x-mu)^2/(2*sigma^2))```
+
+#### Examples
+
+Examples of initialization of one or a batch of distributions.
+
+```python
+# Define a single scalar Normal distribution.
+dist = tf.contrib.distributions.Normal(mu=0, sigma=3)
+
+# Evaluate the cdf at 1, returning a scalar.
+dist.cdf(1)
+
+# Define a batch of two scalar valued Normals.
+# The first has mean 1 and standard deviation 11, the second 2 and 22.
+dist = tf.contrib.distributions.Normal(mu=[1, 2.], sigma=[11, 22.])
+
+# Evaluate the pdf of the first distribution on 0, and the second on 1.5,
+# returning a length two tensor.
+dist.pdf([0, 1.5])
+
+# Get 3 samples, returning a 3 x 2 tensor.
+dist.sample(3)
+```
+
+Arguments are broadcast when possible.
+
+```python
+# Define a batch of two scalar valued Normals.
+# Both have mean 1, but different standard deviations.
+dist = tf.contrib.distributions.Normal(mu=1, sigma=[11, 22.])
+
+# Evaluate the pdf of both distributions on the same point, 3.0,
+# returning a length 2 tensor.
+dist.pdf(3.0)
+```
+- - -
+
+#### `tf.contrib.distributions.Normal.__init__(mu, sigma, name=None)` {#Normal.__init__}
+
+Construct Normal distributions with mean and stddev `mu` and `sigma`.
+
+The parameters `mu` and `sigma` must be shaped in a way that supports
+broadcasting (e.g. `mu + sigma` is a valid operation).
+
+##### Args:
+
+
+*  <b>`mu`</b>: `float` or `double` tensor, the means of the distribution(s).
+*  <b>`sigma`</b>: `float` or `double` tensor, the stddevs of the distribution(s).
+    sigma must contain only positive values.
+*  <b>`name`</b>: The name to give Ops created by the initializer.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if mu and sigma are different dtypes.
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.cdf(x, name=None)` {#Normal.cdf}
+
+CDF of observations in `x` under these Normal distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.dtype` {#Normal.dtype}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.entropy(name=None)` {#Normal.entropy}
+
+The entropy of Normal distribution(s).
+
+##### Args:
+
+
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.is_reparameterized` {#Normal.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.log_cdf(x, name=None)` {#Normal.log_cdf}
+
+Log CDF of observations `x` under these Normal distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.log_pdf(x, name=None)` {#Normal.log_pdf}
+
+Log pdf of observations in `x` under these Normal distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.mean` {#Normal.mean}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.mu` {#Normal.mu}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.pdf(x, name=None)` {#Normal.pdf}
+
+The PDF of observations in `x` under these Normal distribution(s).
+
+##### Args:
+
+
+*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.sample(n, seed=None, name=None)` {#Normal.sample}
+
+Sample `n` observations from the Normal Distributions.
+
+##### Args:
+
+
+*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`seed`</b>: Python integer, the random seed.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
+    of the distributions determined by broadcasting the hyperparameters.
+
+
+- - -
+
+#### `tf.contrib.distributions.Normal.sigma` {#Normal.sigma}
+
+
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.ffmpeg.encode_audio.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.ffmpeg.encode_audio.md
deleted file mode 100644
index fb9d958f26..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.ffmpeg.encode_audio.md
+++ /dev/null
@@ -1,19 +0,0 @@
-### `tf.contrib.ffmpeg.encode_audio(audio, file_format=None, samples_per_second=None)` {#encode_audio}
-
-Creates an op that encodes an audio file using sampled audio from a tensor.
-
-##### Args:
-
-
-*  <b>`audio`</b>: A rank 2 tensor that has time along dimension 0 and channels along
-      dimension 1. Dimension 0 is `samples_per_second * length` long in
-      seconds.
-*  <b>`file_format`</b>: The type of file to encode. "wav" is the only supported format.
-*  <b>`samples_per_second`</b>: The number of samples in the audio tensor per second of
-      audio.
-
-##### Returns:
-
-  A scalar tensor that contains the encoded audio in the specified file
-  format.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.layers.optimize_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.layers.optimize_loss.md
deleted file mode 100644
index db0b01186a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.layers.optimize_loss.md
+++ /dev/null
@@ -1,43 +0,0 @@
-### `tf.contrib.layers.optimize_loss(loss, global_step, learning_rate, optimizer, gradient_noise_scale=None, gradient_multipliers=None, clip_gradients=None, moving_average_decay=0.9, learning_rate_decay_fn=None, variables=None, name=None)` {#optimize_loss}
-
-Given loss and parameters for optimizer, returns a training op.
-
-##### Args:
-
-
-*  <b>`loss`</b>: Tensor, 0 dimensional.
-*  <b>`global_step`</b>: Tensor, step counter for each update.
-*  <b>`learning_rate`</b>: float or Tensor, magnitude of update per each training step.
-*  <b>`optimizer`</b>: string, class or optimizer instance, used as trainer.
-             string should be name of optimizer, like 'SGD',
-               'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant.
-             class should be sub-class of tf.Optimizer that implements
-               `compute_gradients` and `apply_gradients` functions.
-             optimizer instance should be instantion of tf.Optimizer sub-class
-               and have `compute_gradients` and `apply_gradients` functions.
-*  <b>`gradient_noise_scale`</b>: float or None, adds 0-mean normal noise scaled by this
-                        value.
-*  <b>`gradient_multipliers`</b>: dict of variables or variable names to floats.
-                        If present, gradients for specified
-                        variables will be multiplied by given constant.
-*  <b>`clip_gradients`</b>: float or `None`, clips gradients by this value.
-*  <b>`moving_average_decay`</b>: float or None, takes into account previous loss
-                        to make learning smoother due to outliers.
-*  <b>`learning_rate_decay_fn`</b>: function, takes `learning_rate` and `global_step`
-                          `Tensor`s, returns `Tensor`.
-                          Can be used to implement any learning rate decay
-                          functions.
-                          For example: tf.train.exponential_decay.
-*  <b>`variables`</b>: list of variables to optimize or
-             `None` to use all trainable variables.
-*  <b>`name`</b>: The name for this operation is used to scope operations and summaries.
-
-##### Returns:
-
-  Training op.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if optimizer is wrong type.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.BaseEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.BaseEstimator.md
new file mode 100644
index 0000000000..034af231a1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.BaseEstimator.md
@@ -0,0 +1,189 @@
+Abstract BaseEstimator class to train and evaluate TensorFlow models.
+
+Concrete implementation of this class should provide following functions:
+  * _get_train_ops
+  * _get_eval_ops
+  * _get_predict_ops
+It may override _get_default_metric_functions.
+
+`Estimator` implemented below is a good example of how to use this class.
+
+Parameters:
+  model_dir: Directory to save model parameters, graph and etc.
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.__init__(model_dir=None, config=None)` {#BaseEstimator.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=32, steps=None, metrics=None, name=None)` {#BaseEstimator.evaluate}
+
+Evaluates given model with provided evaluation data.
+
+##### Args:
+
+
+*  <b>`x`</b>: features.
+*  <b>`y`</b>: targets.
+*  <b>`input_fn`</b>: Input function. If set, x and y must be None.
+*  <b>`feed_fn`</b>: Function creating a feed dict every time it is called. Called
+    once per iteration.
+*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32. Ignored
+    if input_fn is set.
+*  <b>`steps`</b>: Number of steps to evalute for.
+*  <b>`metrics`</b>: Dict of metric ops to run. If None, the default metric functions
+    are used; if {}, no metrics are used.
+*  <b>`name`</b>: Name of the evaluation if user needs to run multiple evaluation on
+    different data sets, such as evaluate on training data vs test data.
+
+##### Returns:
+
+  Returns self.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If x or y are not None while input_fn or feed_fn is not None.
+
+
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.fit(x, y, steps, batch_size=32, monitors=None)` {#BaseEstimator.fit}
+
+Trains a model given training data X and y.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+     iterator that returns arrays of features. The training input
+     samples for fitting the model.
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+     iterator that returns array of targets. The training target values
+     (class labels in classification, real numbers in regression).
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.get_params(deep=True)` {#BaseEstimator.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.model_dir` {#BaseEstimator.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.partial_fit(x, y, steps=1, batch_size=32, monitors=None)` {#BaseEstimator.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+    iterator that returns arrays of features. The training input
+    samples for fitting the model.
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+    iterator that returns array of targets. The training target values
+    (class label in classification, real numbers in regression).
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to 32.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.predict(x=None, input_fn=None, batch_size=None)` {#BaseEstimator.predict}
+
+Returns predictions for given features.
+
+##### Args:
+
+
+*  <b>`x`</b>: features.
+*  <b>`input_fn`</b>: Input function. If set, x must be None.
+*  <b>`batch_size`</b>: Override default batch size.
+
+##### Returns:
+
+  Numpy array of predicted classes or regression values.
+
+
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.set_params(**params)` {#BaseEstimator.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.train(input_fn, steps, monitors=None)` {#BaseEstimator.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md
deleted file mode 100644
index 130b2706de..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md
+++ /dev/null
@@ -1,312 +0,0 @@
-TensorFlow RNN Classifier model.
-
-Parameters:
-  rnn_size: The size for rnn cell, e.g. size of your word embeddings.
-  cell_type: The type of rnn cell, including rnn, gru, and lstm.
-  num_layers: The number of layers of the rnn model.
-  input_op_fn: Function that will transform the input tensor, such as
-    creating word embeddings, byte list, etc. This takes
-    an argument X for input and returns transformed X.
-  bidirectional: boolean, Whether this is a bidirectional rnn.
-  sequence_length: If sequence_length is provided, dynamic calculation is
-    performed. This saves computational time when unrolling past max sequence
-    length.
-  initial_state: An initial state for the RNN. This must be a tensor of
-    appropriate type and shape [batch_size x cell.state_size].
-  n_classes: Number of classes in the target.
-  batch_size: Mini batch size.
-  steps: Number of steps to run over data.
-  optimizer: Optimizer name (or class), for example "SGD", "Adam", "Adagrad".
-  learning_rate: If this is constant float value, no decay function is
-    used. Instead, a customized decay function can be passed that accepts
-    global_step as parameter and returns a Tensor.
-    e.g. exponential decay function:
-    def exp_decay(global_step):
-        return tf.train.exponential_decay(
-            learning_rate=0.1, global_step,
-            decay_steps=2, decay_rate=0.001)
-  class_weight: None or list of n_classes floats. Weight associated with
-    classes for loss computation. If not given, all classes are
-    supposed to have weight one.
-  continue_training: when continue_training is True, once initialized
-    model will be continuely trained on every call of fit.
-  config: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc.
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.__init__(rnn_size, n_classes, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None, batch_size=32, steps=50, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowRNNClassifier.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.bias_` {#TensorFlowRNNClassifier.bias_}
-
-Returns bias of the rnn layer.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowRNNClassifier.evaluate}
-
-See base class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowRNNClassifier.fit}
-
-Builds a neural network model given provided `model_fn` and training
-data X and y.
-
-Note: called first time constructs the graph and initializers
-variables. Consecutives times it will continue training the same model.
-This logic follows partial_fit() interface in scikit-learn.
-
-To restart learning, create new estimator.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class labels in classification, real numbers in regression).
-
-*  <b>`steps`</b>: int, number of steps to train.
-         If None or 0, train for `self.steps`.
-*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
-    invoke early stopping.
-*  <b>`logdir`</b>: the directory to save the log file that can be used for
-  optional visualization.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.get_params(deep=True)` {#TensorFlowRNNClassifier.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-    If True, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.get_tensor(name)` {#TensorFlowRNNClassifier.get_tensor}
-
-Returns tensor by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.get_tensor_value(name)` {#TensorFlowRNNClassifier.get_tensor_value}
-
-Returns value of the tensor give by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.get_variable_names()` {#TensorFlowRNNClassifier.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.model_dir` {#TensorFlowRNNClassifier.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.partial_fit(x, y)` {#TensorFlowRNNClassifier.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
-  iterator that returns arrays of features. The training input
-  samples for fitting the model.
-
-*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-  iterator that returns array of targets. The training target values
-  (class label in classification, real numbers in regression).
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.predict(x, axis=1, batch_size=None)` {#TensorFlowRNNClassifier.predict}
-
-Predict class or regression for X.
-
-For a classification model, the predicted class for each sample in X is
-returned. For a regression model, the predicted value based on X is
-returned.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`axis`</b>: Which axis to argmax for classification.
-    By default axis 1 (next after batch) is used.
-    Use 2 for sequence predictions.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member
-    variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
-  value.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.predict_proba(x, batch_size=None)` {#TensorFlowRNNClassifier.predict_proba}
-
-Predict class probability of the input samples X.
-
-##### Args:
-
-
-*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
-*  <b>`batch_size`</b>: If test set is too big, use batch size to split
-    it into mini batches. By default the batch_size member variable is used.
-
-##### Returns:
-
-
-*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
-  probabilities for each class.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.restore(cls, path, config=None)` {#TensorFlowRNNClassifier.restore}
-
-Restores model from give path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Path to the checkpoints and other model information.
-*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
-    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
-      reconfigured.
-
-##### Returns:
-
-  Estimator, object of the subclass of TensorFlowEstimator.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.save(path)` {#TensorFlowRNNClassifier.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.set_params(**params)` {#TensorFlowRNNClassifier.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Returns:
-
-  self
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.train(input_fn, steps, monitors=None)` {#TensorFlowRNNClassifier.train}
-
-Trains a model given input builder function.
-
-##### Args:
-
-
-*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
-            dict and Tensor.
-*  <b>`steps`</b>: number of steps to train model for.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-            inside the training loop.
-
-##### Returns:
-
-  Returns self.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.weights_` {#TensorFlowRNNClassifier.weights_}
-
-Returns weights of the rnn layer.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRegressor.md
new file mode 100644
index 0000000000..169509f72f
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRegressor.md
@@ -0,0 +1,279 @@
+TensorFlow Linear Regression model.
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.__init__(n_classes=0, batch_size=32, steps=200, optimizer='Adagrad', learning_rate=0.1, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowRegressor.__init__}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.bias_` {#TensorFlowRegressor.bias_}
+
+Returns bias of the linear regression.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.evaluate(x=None, y=None, input_fn=None, steps=None)` {#TensorFlowRegressor.evaluate}
+
+See base class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.fit(x, y, steps=None, monitors=None, logdir=None)` {#TensorFlowRegressor.fit}
+
+Builds a neural network model given provided `model_fn` and training
+data X and y.
+
+Note: called first time constructs the graph and initializers
+variables. Consecutives times it will continue training the same model.
+This logic follows partial_fit() interface in scikit-learn.
+
+To restart learning, create new estimator.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class labels in classification, real numbers in regression).
+
+*  <b>`steps`</b>: int, number of steps to train.
+         If None or 0, train for `self.steps`.
+*  <b>`monitors`</b>: List of `BaseMonitor` objects to print training progress and
+    invoke early stopping.
+*  <b>`logdir`</b>: the directory to save the log file that can be used for
+  optional visualization.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.get_params(deep=True)` {#TensorFlowRegressor.get_params}
+
+Get parameters for this estimator.
+
+##### Args:
+
+
+*  <b>`deep`</b>: boolean, optional
+    If True, will return the parameters for this estimator and
+    contained subobjects that are estimators.
+
+##### Returns:
+
+  params : mapping of string to any
+  Parameter names mapped to their values.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.get_tensor(name)` {#TensorFlowRegressor.get_tensor}
+
+Returns tensor by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.get_tensor_value(name)` {#TensorFlowRegressor.get_tensor_value}
+
+Returns value of the tensor give by name.
+
+##### Args:
+
+
+*  <b>`name`</b>: string, name of the tensor.
+
+##### Returns:
+
+  Numpy array - value of the tensor.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.get_variable_names()` {#TensorFlowRegressor.get_variable_names}
+
+Returns list of all variable names in this model.
+
+##### Returns:
+
+  List of names.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.model_dir` {#TensorFlowRegressor.model_dir}
+
+
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.partial_fit(x, y)` {#TensorFlowRegressor.partial_fit}
+
+Incremental fit on a batch of samples.
+
+This method is expected to be called several times consecutively
+on different or the same chunks of the dataset. This either can
+implement iterative training or out-of-core/online training.
+
+This is especially useful when the whole dataset is too big to
+fit in memory at the same time. Or when model is taking long time
+to converge, and you want to split up training into subparts.
+
+##### Args:
+
+
+*  <b>`x`</b>: matrix or tensor of shape [n_samples, n_features...]. Can be
+  iterator that returns arrays of features. The training input
+  samples for fitting the model.
+
+*  <b>`y`</b>: vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
+  iterator that returns array of targets. The training target values
+  (class label in classification, real numbers in regression).
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.predict(x, axis=1, batch_size=None)` {#TensorFlowRegressor.predict}
+
+Predict class or regression for X.
+
+For a classification model, the predicted class for each sample in X is
+returned. For a regression model, the predicted value based on X is
+returned.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`axis`</b>: Which axis to argmax for classification.
+    By default axis 1 (next after batch) is used.
+    Use 2 for sequence predictions.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member
+    variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples]. The predicted classes or predicted
+  value.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.predict_proba(x, batch_size=None)` {#TensorFlowRegressor.predict_proba}
+
+Predict class probability of the input samples X.
+
+##### Args:
+
+
+*  <b>`x`</b>: array-like matrix, [n_samples, n_features...] or iterator.
+*  <b>`batch_size`</b>: If test set is too big, use batch size to split
+    it into mini batches. By default the batch_size member variable is used.
+
+##### Returns:
+
+
+*  <b>`y`</b>: array of shape [n_samples, n_classes]. The predicted
+  probabilities for each class.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.restore(cls, path, config=None)` {#TensorFlowRegressor.restore}
+
+Restores model from give path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Path to the checkpoints and other model information.
+*  <b>`config`</b>: RunConfig object that controls the configurations of the session,
+    e.g. num_cores, gpu_memory_fraction, etc. This is allowed to be
+      reconfigured.
+
+##### Returns:
+
+  Estimator, object of the subclass of TensorFlowEstimator.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.save(path)` {#TensorFlowRegressor.save}
+
+Saves checkpoints and graph to given path.
+
+##### Args:
+
+
+*  <b>`path`</b>: Folder to save model to.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.set_params(**params)` {#TensorFlowRegressor.set_params}
+
+Set the parameters of this estimator.
+
+The method works on simple estimators as well as on nested objects
+(such as pipelines). The former have parameters of the form
+``<component>__<parameter>`` so that it's possible to update each
+component of a nested object.
+
+##### Returns:
+
+  self
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.train(input_fn, steps, monitors=None)` {#TensorFlowRegressor.train}
+
+Trains a model given input builder function.
+
+##### Args:
+
+
+*  <b>`input_fn`</b>: Input builder function, returns tuple of dicts or
+            dict and Tensor.
+*  <b>`steps`</b>: number of steps to train model for.
+*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
+            inside the training loop.
+
+##### Returns:
+
+  Returns self.
+
+
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.weights_` {#TensorFlowRegressor.weights_}
+
+Returns weights of the linear regression.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.extract_dask_data.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.extract_dask_data.md
deleted file mode 100644
index a14a51ff56..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.extract_dask_data.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.contrib.learn.extract_dask_data(data)` {#extract_dask_data}
-
-Extract data from dask.Series or dask.DataFrame for predictors
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.extract_pandas_labels.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.extract_pandas_labels.md
new file mode 100644
index 0000000000..521a8560e5
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.extract_pandas_labels.md
@@ -0,0 +1,4 @@
+### `tf.contrib.learn.extract_pandas_labels(labels)` {#extract_pandas_labels}
+
+Extract data from pandas.DataFrame for labels
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.set_difference.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.set_difference.md
deleted file mode 100644
index 452115a428..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.set_difference.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.contrib.metrics.set_difference(a, b, aminusb=True, validate_indices=True)` {#set_difference}
-
-Compute set difference of elements in last dimension of `a` and `b`.
-
-All but the last dimension of `a` and `b` must match.
-
-##### Args:
-
-
-*  <b>`a`</b>: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
-      must be sorted in row-major order.
-*  <b>`b`</b>: `Tensor` or `SparseTensor` of the same type as `a`. Must be
-      `SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
-      sorted in row-major order.
-*  <b>`aminusb`</b>: Whether to subtract `b` from `a`, vs vice versa.
-*  <b>`validate_indices`</b>: Whether to validate the order and range of sparse indices
-     in `a` and `b`.
-
-##### Returns:
-
-  A `SparseTensor` with the same rank as `a` and `b`, and all but the last
-  dimension the same. Elements along the last dimension contain the
-  differences.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.set_size.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.set_size.md
deleted file mode 100644
index 8f58261e7d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.set_size.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.contrib.metrics.set_size(a, validate_indices=True)` {#set_size}
-
-Compute number of unique elements along last dimension of `a`.
-
-##### Args:
-
-
-*  <b>`a`</b>: `SparseTensor`, with indices sorted in row-major order.
-*  <b>`validate_indices`</b>: Whether to validate the order and range of sparse indices
-     in `a`.
-
-##### Returns:
-
-  For `a` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st
-  `n-1` dimensions as `a`. Each value is the number of unique elements in
-  the corresponding `[0...n-1]` dimension of `a`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `a` is an invalid types.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.streaming_mean_absolute_error.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.streaming_mean_absolute_error.md
deleted file mode 100644
index b4ecd6e916..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.streaming_mean_absolute_error.md
+++ /dev/null
@@ -1,48 +0,0 @@
-### `tf.contrib.metrics.streaming_mean_absolute_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_mean_absolute_error}
-
-Computes the mean absolute error between the labels and predictions.
-
-The `streaming_mean_absolute_error` function creates two local variables,
-`total` and `count` that are used to compute the mean absolute error. This
-average is ultimately returned as `mean_absolute_error`: an idempotent
-operation that simply divides `total` by `count`. To facilitate the estimation
-of the mean absolute error over a stream of data, the function utilizes two
-operations. First, an `absolute_errors` operation computes the absolute value
-of the differences between `predictions` and `labels`. Second, an `update_op`
-operation whose behavior is dependent on the value of `weights`. If `weights`
-is None, then `update_op` increments `total` with the reduced sum of
-`absolute_errors` and increments `count` with the number of elements in
-`absolute_errors`. If `weights` is not `None`, then `update_op` increments
-`total` with the reduced sum of the product of `weights` and `absolute_errors`
-and increments `count` with the reduced sum of `weights`. In addition to
-performing the updates, `update_op` also returns the `mean_absolute_error`
-value.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: A `Tensor` of arbitrary shape.
-*  <b>`labels`</b>: A `Tensor` of the same shape as `predictions`.
-*  <b>`weights`</b>: An optional set of weights of the same shape as `predictions`. If
-    `weights` is not None, the function computes a weighted mean.
-*  <b>`metrics_collections`</b>: An optional list of collections that
-    `mean_absolute_error` should be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
-    be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`mean_absolute_error`</b>: A tensor representing the current mean, the value of
-    `total` divided by `count`.
-*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
-    appropriately and whose value matches `mean_absolute_error`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `weights` is not `None` and its shape doesn't match
-    `predictions` or if either `metrics_collections` or `updates_collections`
-    are not a list or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.streaming_recall.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.streaming_recall.md
deleted file mode 100644
index 26308e2f5f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.streaming_recall.md
+++ /dev/null
@@ -1,50 +0,0 @@
-### `tf.contrib.metrics.streaming_recall(predictions, labels, ignore_mask=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_recall}
-
-Computes the recall of the predictions with respect to the labels.
-
-The `streaming_recall` function creates two local variables,
-`true_positives` and `false_negatives`, that are used to compute the
-recall. This value is ultimately returned as `recall`, an idempotent
-operation that simply divides `true_positives` by the sum of `true_positives`
-and `false_negatives`. To facilitate the calculation of the recall over a
-stream of data, the function creates an `update_op` operation whose behavior
-is dependent on the value of `ignore_mask`. If `ignore_mask` is None, then
-`update_op` increments `true_positives` with the number of elements of
-`predictions` and `labels` that are both `True` and increments
-`false_negatives` with the number of elements of `predictions` that are
-`False` whose corresponding `labels` element is `False`. If `ignore_mask` is
-not `None`, then the increments for `true_positives` and `false_negatives` are
-only computed using elements of `predictions` and `labels` whose corresponding
-values in `ignore_mask` are `False`. In addition to performing the updates,
-`update_op` also returns the value of `recall`.
-
-##### Args:
-
-
-*  <b>`predictions`</b>: The predicted values, a binary `Tensor` of arbitrary shape.
-*  <b>`labels`</b>: The ground truth values, a binary `Tensor` whose dimensions must
-    match `predictions`.
-*  <b>`ignore_mask`</b>: An optional, binary tensor whose size matches `predictions`.
-*  <b>`metrics_collections`</b>: An optional list of collections that `precision` should
-    be added to.
-*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
-    be added to.
-*  <b>`name`</b>: An optional variable_op_scope name.
-
-##### Returns:
-
-
-*  <b>`recall`</b>: Scalar float `Tensor` with the value of `true_positives` divided
-    by the sum of `true_positives` and `false_negatives`.
-*  <b>`update_op`</b>: `Operation` that increments `true_positives` and
-    `false_negatives` variables appropriately and whose value matches
-    `recall`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If the dimensions of `predictions` and `labels` don't match or
-    if `ignore_mask` is not `None` and its shape doesn't match `predictions`
-    or if either `metrics_collections` or `updates_collections` are not a list
-    or tuple.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.streaming_root_mean_squared_error.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.streaming_root_mean_squared_error.md
new file mode 100644
index 0000000000..85319f44dd
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.metrics.streaming_root_mean_squared_error.md
@@ -0,0 +1,48 @@
+### `tf.contrib.metrics.streaming_root_mean_squared_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_root_mean_squared_error}
+
+Computes the root mean squared error between the labels and predictions.
+
+The `streaming_root_mean_squared_error` function creates two local variables,
+`total` and `count` that are used to compute the root mean squared error.
+This average is ultimately returned as `root_mean_squared_error`: an
+idempotent operation that takes the square root of the division of `total`
+by `count`. To facilitate the estimation of the root mean squared error over a
+stream of data, the function utilizes two operations. First, a `squared_error`
+operation computes the element-wise square of the difference between
+`predictions` and `labels`. Second, an `update_op` operation whose behavior is
+dependent on the value of `weights`. If `weights` is None, then `update_op`
+increments `total` with the reduced sum of `squared_error` and increments
+`count` with the number of elements in `squared_error`. If `weights` is not
+`None`, then `update_op` increments `total` with the reduced sum of the
+product of `weights` and `squared_error` and increments `count` with the
+reduced sum of `weights`. In addition to performing the updates, `update_op`
+also returns the `root_mean_squared_error` value.
+
+##### Args:
+
+
+*  <b>`predictions`</b>: A `Tensor` of arbitrary shape.
+*  <b>`labels`</b>: A `Tensor` of the same shape as `predictions`.
+*  <b>`weights`</b>: An optional set of weights of the same shape as `predictions`. If
+    `weights` is not None, the function computes a weighted mean.
+*  <b>`metrics_collections`</b>: An optional list of collections that
+    `root_mean_squared_error` should be added to.
+*  <b>`updates_collections`</b>: An optional list of collections that `update_op` should
+    be added to.
+*  <b>`name`</b>: An optional variable_op_scope name.
+
+##### Returns:
+
+
+*  <b>`root_mean_squared_error`</b>: A tensor representing the current mean, the value
+    of `total` divided by `count`.
+*  <b>`update_op`</b>: An operation that increments the `total` and `count` variables
+    appropriately and whose value matches `root_mean_squared_error`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `weights` is not `None` and its shape doesn't match
+    `predictions` or if either `metrics_collections` or `updates_collections`
+    are not a list or tuple.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.cos.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.cos.md
deleted file mode 100644
index b4f6f89933..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.cos.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.cos(x, name=None)` {#cos}
-
-Computes cos of x element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.decode_raw.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.decode_raw.md
new file mode 100644
index 0000000000..125c15d9a8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.decode_raw.md
@@ -0,0 +1,23 @@
+### `tf.decode_raw(bytes, out_type, little_endian=None, name=None)` {#decode_raw}
+
+Reinterpret the bytes of a string as a vector of numbers.
+
+##### Args:
+
+
+*  <b>`bytes`</b>: A `Tensor` of type `string`.
+    All the elements must have the same length.
+*  <b>`out_type`</b>: A `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.int64`.
+*  <b>`little_endian`</b>: An optional `bool`. Defaults to `True`.
+    Whether the input `bytes` are in little-endian order.
+    Ignored for `out_type` values that are stored in a single byte like
+    `uint8`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `out_type`.
+  A Tensor with one more dimension than the input `bytes`.  The
+  added dimension will have size equal to the length of the elements
+  of `bytes` divided by the number of bytes to represent `out_type`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.dynamic_stitch.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.dynamic_stitch.md
new file mode 100644
index 0000000000..6bb1f8dd10
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.dynamic_stitch.md
@@ -0,0 +1,53 @@
+### `tf.dynamic_stitch(indices, data, name=None)` {#dynamic_stitch}
+
+Interleave the values from the `data` tensors into a single tensor.
+
+Builds a merged tensor such that
+
+    merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
+
+For example, if each `indices[m]` is scalar or vector, we have
+
+    # Scalar indices
+    merged[indices[m], ...] = data[m][...]
+
+    # Vector indices
+    merged[indices[m][i], ...] = data[m][i, ...]
+
+Each `data[i].shape` must start with the corresponding `indices[i].shape`,
+and the rest of `data[i].shape` must be constant w.r.t. `i`.  That is, we
+must have `data[i].shape = indices[i].shape + constant`.  In terms of this
+`constant`, the output shape is
+
+    merged.shape = [max(indices)] + constant
+
+Values are merged in order, so if an index appears in both `indices[m][i]` and
+`indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the
+merged result.
+
+For example:
+
+    indices[0] = 6
+    indices[1] = [4, 1]
+    indices[2] = [[5, 2], [0, 3]]
+    data[0] = [61, 62]
+    data[1] = [[41, 42], [11, 12]]
+    data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
+    merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
+              [51, 52], [61, 62]]
+
+<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+<img style="width:100%" src="../../images/DynamicStitch.png" alt>
+</div>
+
+##### Args:
+
+
+*  <b>`indices`</b>: A list of at least 2 `Tensor` objects of type `int32`.
+*  <b>`data`</b>: A list with the same number of `Tensor` objects as `indices` of `Tensor` objects of the same type.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `data`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.edit_distance.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.edit_distance.md
new file mode 100644
index 0000000000..e5f6471817
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.edit_distance.md
@@ -0,0 +1,65 @@
+### `tf.edit_distance(hypothesis, truth, normalize=True, name='edit_distance')` {#edit_distance}
+
+Computes the Levenshtein distance between sequences.
+
+This operation takes variable-length sequences (`hypothesis` and `truth`),
+each provided as a `SparseTensor`, and computes the Levenshtein distance.
+You can normalize the edit distance by length of `truth` by setting
+`normalize` to true.
+
+For example, given the following input:
+
+```python
+# 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values:
+#   (0,0) = ["a"]
+#   (1,0) = ["b"]
+hypothesis = tf.SparseTensor(
+    [[0, 0, 0],
+     [1, 0, 0]],
+    ["a", "b"]
+    (2, 1, 1))
+
+# 'truth' is a tensor of shape `[2, 2]` with variable-length values:
+#   (0,0) = []
+#   (0,1) = ["a"]
+#   (1,0) = ["b", "c"]
+#   (1,1) = ["a"]
+truth = tf.SparseTensor(
+    [[0, 1, 0],
+     [1, 0, 0],
+     [1, 0, 1],
+     [1, 1, 0]]
+    ["a", "b", "c", "a"],
+    (2, 2, 2))
+
+normalize = True
+```
+
+This operation would return the following:
+
+```python
+# 'output' is a tensor of shape `[2, 2]` with edit distances normalized
+# by 'truth' lengths.
+output ==> [[inf, 1.0],  # (0,0): no truth, (0,1): no hypothesis
+           [0.5, 1.0]]  # (1,0): addition, (1,1): no hypothesis
+```
+
+##### Args:
+
+
+*  <b>`hypothesis`</b>: A `SparseTensor` containing hypothesis sequences.
+*  <b>`truth`</b>: A `SparseTensor` containing truth sequences.
+*  <b>`normalize`</b>: A `bool`. If `True`, normalizes the Levenshtein distance by
+    length of `truth.`
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A dense `Tensor` with rank `R - 1`, where R is the rank of the
+  `SparseTensor` inputs `hypothesis` and `truth`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If either `hypothesis` or `truth` are not a `SparseTensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.erfc.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.erfc.md
deleted file mode 100644
index d2ac7952e0..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.erfc.md
+++ /dev/null
@@ -1,14 +0,0 @@
-### `tf.erfc(x, name=None)` {#erfc}
-
-Computes the complementary error function of `x` element-wise.
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.errors.InvalidArgumentError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.errors.InvalidArgumentError.md
deleted file mode 100644
index 877325fe0b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.errors.InvalidArgumentError.md
+++ /dev/null
@@ -1,17 +0,0 @@
-Raised when an operation receives an invalid argument.
-
-This may occur, for example, if an operation is receives an input
-tensor that has an invalid value or shape. For example, the
-[`tf.matmul()`](../../api_docs/python/math_ops.md#matmul) op will raise this
-error if it receives an input that is not a matrix, and the
-[`tf.reshape()`](../../api_docs/python/array_ops.md#reshape) op will raise
-this error if the new shape does not match the number of elements in the input
-tensor.
-
-- - -
-
-#### `tf.errors.InvalidArgumentError.__init__(node_def, op, message)` {#InvalidArgumentError.__init__}
-
-Creates an `InvalidArgumentError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.errors.UnimplementedError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.errors.UnimplementedError.md
new file mode 100644
index 0000000000..945daa1a22
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.errors.UnimplementedError.md
@@ -0,0 +1,15 @@
+Raised when an operation has not been implemented.
+
+Some operations may raise this error when passed otherwise-valid
+arguments that it does not currently support. For example, running
+the [`tf.nn.max_pool()`](../../api_docs/python/nn.md#max_pool) operation
+would raise this error if pooling was requested on the batch dimension,
+because this is not yet supported.
+
+- - -
+
+#### `tf.errors.UnimplementedError.__init__(node_def, op, message)` {#UnimplementedError.__init__}
+
+Creates an `UnimplementedError`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.errors.UnknownError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.errors.UnknownError.md
deleted file mode 100644
index 3e18ec866b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.errors.UnknownError.md
+++ /dev/null
@@ -1,15 +0,0 @@
-Unknown error.
-
-An example of where this error may be returned is if a Status value
-received from another address space belongs to an error-space that
-is not known to this address space. Also errors raised by APIs that
-do not return enough error information may be converted to this
-error.
-
-- - -
-
-#### `tf.errors.UnknownError.__init__(node_def, op, message, error_code=2)` {#UnknownError.__init__}
-
-Creates an `UnknownError`.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.fft2d.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.fft2d.md
new file mode 100644
index 0000000000..e480dcb27e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.fft2d.md
@@ -0,0 +1,14 @@
+### `tf.fft2d(input, name=None)` {#fft2d}
+
+Compute the 2-dimensional discrete Fourier Transform.
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor` of type `complex64`. A complex64 matrix.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `complex64`. The 2D Fourier Transform of `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.foldr.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.foldr.md
deleted file mode 100644
index 0a75190c04..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.foldr.md
+++ /dev/null
@@ -1,44 +0,0 @@
-### `tf.foldr(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#foldr}
-
-foldr on the list of tensors unpacked from `elems` on dimension 0.
-
-This foldr operator repeatedly applies the callable `fn` to a sequence
-of elements from last to first. The elements are made of the tensors
-unpacked from `elems`. The callable fn takes two tensors as arguments.
-The first argument is the accumulated value computed from the preceding
-invocation of fn. If `initializer` is None, `elems` must contain at least
-one element, and its first element is used as the initializer.
-
-Suppose that `elems` is unpacked into `values`, a list of tensors. The shape
-of the result tensor is `fn(initializer, values[0]).shape`.
-
-##### Args:
-
-
-*  <b>`fn`</b>: The callable to be performed.
-*  <b>`elems`</b>: A tensor that is unpacked into a sequence of tensors to apply `fn`.
-*  <b>`initializer`</b>: (optional) The initial value for the accumulator.
-*  <b>`parallel_iterations`</b>: (optional) The number of iterations allowed to run
-                       in parallel.
-*  <b>`back_prop`</b>: (optional) True enables back propagation.
-*  <b>`swap_memory`</b>: (optional) True enables GPU-CPU memory swapping.
-*  <b>`name`</b>: (optional) Name prefix for the returned tensors.
-
-##### Returns:
-
-  A tensor resulting from applying `fn` consecutively to the list of tensors
-  unpacked from `elems`, from last to first.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if `fn` is not callable.
-
-##### Example:
-
-  ```python
-  elems = [1, 2, 3, 4, 5, 6]
-  sum = foldr(lambda a, x: a + x, elems)
-  # sum == 21
-  ```
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.get_variable.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.get_variable.md
new file mode 100644
index 0000000000..59e1a1797a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.get_variable.md
@@ -0,0 +1,72 @@
+### `tf.get_variable(name, shape=None, dtype=tf.float32, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True)` {#get_variable}
+
+Gets an existing variable with these parameters or create a new one.
+
+This function prefixes the name with the current variable scope
+and performs reuse checks. See the
+[Variable Scope How To](../../how_tos/variable_scope/index.md)
+for an extensive description of how reusing works. Here is a basic example:
+
+```python
+with tf.variable_scope("foo"):
+    v = tf.get_variable("v", [1])  # v.name == "foo/v:0"
+    w = tf.get_variable("w", [1])  # w.name == "foo/w:0"
+with tf.variable_scope("foo", reuse=True)
+    v1 = tf.get_variable("v")  # The same as v above.
+```
+
+If initializer is `None` (the default), the default initializer passed in
+the variable scope will be used. If that one is `None` too, a
+`UniformUnitScalingInitializer` will be used. The initializer can also be
+a Tensor, in which case the variable is initialized to this value and shape.
+
+Similarly, if the regularizer is `None` (the default), the default regularizer
+passed in the variable scope will be used (if that is `None` too,
+then by default no regularization is performed).
+
+If a partitioner is provided, first a sharded `Variable` is created
+via `_get_partitioned_variable_list`, and the return value is a
+`Tensor` composed of the shards concatenated along the partition axis.
+
+Some useful partitioners are available.  See, e.g.,
+`variable_axis_size_partitioner`.
+
+##### Args:
+
+
+*  <b>`name`</b>: The name of the new or existing variable.
+*  <b>`shape`</b>: Shape of the new or existing variable.
+*  <b>`dtype`</b>: Type of the new or existing variable (defaults to `DT_FLOAT`).
+*  <b>`initializer`</b>: Initializer for the variable if one is created.
+*  <b>`regularizer`</b>: A (Tensor -> Tensor or None) function; the result of
+    applying it on a newly created variable will be added to the collection
+    GraphKeys.REGULARIZATION_LOSSES and can be used for regularization.
+*  <b>`trainable`</b>: If `True` also add the variable to the graph collection
+    `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
+*  <b>`collections`</b>: List of graph collections keys to add the Variable to.
+    Defaults to `[GraphKeys.VARIABLES]` (see tf.Variable).
+    If partitioning is enabled and used, the concatenated return value
+    is also added to collection `GraphKeys.CONCATENATED_VARIABLES`.
+*  <b>`caching_device`</b>: Optional device string or function describing where the
+    Variable should be cached for reading.  Defaults to the Variable's
+    device.  If not `None`, caches on another device.  Typical use is to
+    cache on the device where the Ops using the Variable reside, to
+    deduplicate copying through `Switch` and other conditional statements.
+*  <b>`partitioner`</b>: Optional callable that accepts a fully defined `TensorShape`
+    and `dtype` of the Variable to be created, and returns a list of
+    partitions for each axis (currently only one axis can be partitioned).
+*  <b>`validate_shape`</b>: If False, allows the variable to be initialized with a
+      value of unknown shape. If True, the default, the shape of initial_value
+      must be known.
+
+##### Returns:
+
+  The created or existing variable.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: when creating a new variable and shape is not declared,
+    or when violating reuse during variable creation. Reuse is set inside
+    `variable_scope`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.igammac.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.igammac.md
deleted file mode 100644
index 1b739bcfca..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.igammac.md
+++ /dev/null
@@ -1,29 +0,0 @@
-### `tf.igammac(a, x, name=None)` {#igammac}
-
-Compute the upper regularized incomplete Gamma function `Q(a, x)`.
-
-The upper regularized incomplete Gamma function is defined as:
-
-```
-Q(a, x) = Gamma(a, x) / Gamma(x) = 1 - P(a, x)
-```
-where
-```
-Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt
-```
-is the upper incomplete Gama function.
-
-Note, above `P(a, x)` (`Igamma`) is the lower regularized complete
-Gamma function.
-
-##### Args:
-
-
-*  <b>`a`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-*  <b>`x`</b>: A `Tensor`. Must have the same type as `a`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `a`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.random_contrast.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.random_contrast.md
deleted file mode 100644
index 76cd2292cf..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.random_contrast.md
+++ /dev/null
@@ -1,26 +0,0 @@
-### `tf.image.random_contrast(image, lower, upper, seed=None)` {#random_contrast}
-
-Adjust the contrast of an image by a random factor.
-
-Equivalent to `adjust_contrast()` but uses a `contrast_factor` randomly
-picked in the interval `[lower, upper]`.
-
-##### Args:
-
-
-*  <b>`image`</b>: An image tensor with 3 or more dimensions.
-*  <b>`lower`</b>: float.  Lower bound for the random contrast factor.
-*  <b>`upper`</b>: float.  Upper bound for the random contrast factor.
-*  <b>`seed`</b>: A Python integer. Used to create a random seed. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-
-##### Returns:
-
-  The contrast-adjusted tensor.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `upper <= lower` or if `lower < 0`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.resize_images.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.resize_images.md
new file mode 100644
index 0000000000..d010cac831
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.resize_images.md
@@ -0,0 +1,43 @@
+### `tf.image.resize_images(images, new_height, new_width, method=0, align_corners=False)` {#resize_images}
+
+Resize `images` to `new_width`, `new_height` using the specified `method`.
+
+Resized images will be distorted if their original aspect ratio is not
+the same as `new_width`, `new_height`.  To avoid distortions see
+[`resize_image_with_crop_or_pad`](#resize_image_with_crop_or_pad).
+
+`method` can be one of:
+
+*   <b>`ResizeMethod.BILINEAR`</b>: [Bilinear interpolation.]
+    (https://en.wikipedia.org/wiki/Bilinear_interpolation)
+*   <b>`ResizeMethod.NEAREST_NEIGHBOR`</b>: [Nearest neighbor interpolation.]
+    (https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
+*   <b>`ResizeMethod.BICUBIC`</b>: [Bicubic interpolation.]
+    (https://en.wikipedia.org/wiki/Bicubic_interpolation)
+*   <b>`ResizeMethod.AREA`</b>: Area interpolation.
+
+##### Args:
+
+
+*  <b>`images`</b>: 4-D Tensor of shape `[batch, height, width, channels]` or
+          3-D Tensor of shape `[height, width, channels]`.
+*  <b>`new_height`</b>: integer.
+*  <b>`new_width`</b>: integer.
+*  <b>`method`</b>: ResizeMethod.  Defaults to `ResizeMethod.BILINEAR`.
+*  <b>`align_corners`</b>: bool. If true, exactly align all 4 corners of the input and
+                 output. Defaults to `false`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the shape of `images` is incompatible with the
+    shape arguments to this function
+*  <b>`ValueError`</b>: if an unsupported resize method is specified.
+
+##### Returns:
+
+  If `images` was 4-D, a 4-D float Tensor of shape
+  `[batch, new_height, new_width, channels]`.
+  If `images` was 3-D, a 3-D float Tensor of shape
+  `[new_height, new_width, channels]`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.resize_nearest_neighbor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.resize_nearest_neighbor.md
deleted file mode 100644
index ba72e73ebd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.resize_nearest_neighbor.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.image.resize_nearest_neighbor(images, size, align_corners=None, name=None)` {#resize_nearest_neighbor}
-
-Resize `images` to `size` using nearest neighbor interpolation.
-
-##### Args:
-
-
-*  <b>`images`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
-    4-D with shape `[batch, height, width, channels]`.
-*  <b>`size`</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
-    new size for the images.
-*  <b>`align_corners`</b>: An optional `bool`. Defaults to `False`.
-    If true, rescale input by (new_height - 1) / (height - 1), which
-    exactly aligns the 4 corners of images and resized images. If false, rescale
-    by new_height / height. Treat similarly the width dimension.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `images`. 4-D with shape
-  `[batch, new_height, new_width, channels]`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.rgb_to_hsv.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.rgb_to_hsv.md
deleted file mode 100644
index 7c5d05f515..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.rgb_to_hsv.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.image.rgb_to_hsv(images, name=None)` {#rgb_to_hsv}
-
-Converts one or more images from RGB to HSV.
-
-Outputs a tensor of the same shape as the `images` tensor, containing the HSV
-value of the pixels. The output is only well defined if the value in `images`
-are in `[0,1]`.
-
-`output[..., 0]` contains hue, `output[..., 1]` contains saturation, and
-`output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0
-corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue.
-
-##### Args:
-
-
-*  <b>`images`</b>: A `Tensor` of type `float32`.
-    1-D or higher rank. RGB data to convert. Last dimension must be size 3.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `float32`. `images` converted to HSV.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.sample_distorted_bounding_box.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.sample_distorted_bounding_box.md
deleted file mode 100644
index 2831492f54..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.image.sample_distorted_bounding_box.md
+++ /dev/null
@@ -1,85 +0,0 @@
-### `tf.image.sample_distorted_bounding_box(image_size, bounding_boxes, seed=None, seed2=None, min_object_covered=None, aspect_ratio_range=None, area_range=None, max_attempts=None, use_image_if_no_bounding_boxes=None, name=None)` {#sample_distorted_bounding_box}
-
-Generate a single randomly distorted bounding box for an image.
-
-Bounding box annotations are often supplied in addition to ground-truth labels
-in image recognition or object localization tasks. A common technique for
-training such a system is to randomly distort an image while preserving
-its content, i.e. *data augmentation*. This Op outputs a randomly distorted
-localization of an object, i.e. bounding box, given an `image_size`,
-`bounding_boxes` and a series of constraints.
-
-The output of this Op is a single bounding box that may be used to crop the
-original image. The output is returned as 3 tensors: `begin`, `size` and
-`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
-image. The latter may be supplied to `tf.image.draw_bounding_box` to visualize
-what the bounding box looks like.
-
-Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
-bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
-height of the underlying image.
-
-For example,
-
-    # Generate a single distorted bounding box.
-    begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
-        tf.shape(image),
-        bounding_boxes=bounding_boxes)
-
-    # Draw the bounding box in an image summary.
-    image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
-                                                  bbox_for_draw)
-    tf.image_summary('images_with_box', image_with_box)
-
-    # Employ the bounding box to distort the image.
-    distorted_image = tf.slice(image, begin, size)
-
-Note that if no bounding box information is available, setting
-`use_image_if_no_bounding_boxes = true` will assume there is a single implicit
-bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
-false and no bounding boxes are supplied, an error is raised.
-
-##### Args:
-
-
-*  <b>`image_size`</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`.
-    1-D, containing `[height, width, channels]`.
-*  <b>`bounding_boxes`</b>: A `Tensor` of type `float32`.
-    3-D with shape `[batch, N, 4]` describing the N bounding boxes
-    associated with the image.
-*  <b>`seed`</b>: An optional `int`. Defaults to `0`.
-    If either `seed` or `seed2` are set to non-zero, the random number
-    generator is seeded by the given `seed`.  Otherwise, it is seeded by a random
-    seed.
-*  <b>`seed2`</b>: An optional `int`. Defaults to `0`.
-    A second seed to avoid seed collision.
-*  <b>`min_object_covered`</b>: An optional `float`. Defaults to `0.1`.
-    The cropped area of the image must contain at least this
-    fraction of any bounding box supplied.
-*  <b>`aspect_ratio_range`</b>: An optional list of `floats`. Defaults to `[0.75, 1.33]`.
-    The cropped area of the image must have an aspect ratio =
-    width / height within this range.
-*  <b>`area_range`</b>: An optional list of `floats`. Defaults to `[0.05, 1]`.
-    The cropped area of the image must contain a fraction of the
-    supplied image within in this range.
-*  <b>`max_attempts`</b>: An optional `int`. Defaults to `100`.
-    Number of attempts at generating a cropped region of the image
-    of the specified constraints. After `max_attempts` failures, return the entire
-    image.
-*  <b>`use_image_if_no_bounding_boxes`</b>: An optional `bool`. Defaults to `False`.
-    Controls behavior if no bounding boxes supplied.
-    If true, assume an implicit bounding box covering the whole input. If false,
-    raise an error.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tuple of `Tensor` objects (begin, size, bboxes).
-
-*  <b>`begin`</b>: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
-    `tf.slice`.
-*  <b>`size`</b>: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[target_height, target_width, -1]`. Provide as input to
-    `tf.slice`.
-*  <b>`bboxes`</b>: A `Tensor` of type `float32`. 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
-    Provide as input to `tf.image.draw_bounding_boxes`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.lgamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.lgamma.md
new file mode 100644
index 0000000000..2b8fda7dee
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.lgamma.md
@@ -0,0 +1,14 @@
+### `tf.lgamma(x, name=None)` {#lgamma}
+
+Computes the log of the absolute value of `Gamma(x)` element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.matching_files.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.matching_files.md
new file mode 100644
index 0000000000..297462d580
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.matching_files.md
@@ -0,0 +1,17 @@
+### `tf.matching_files(pattern, name=None)` {#matching_files}
+
+Returns the set of files matching a pattern.
+
+Note that this routine only supports wildcard characters in the
+basename portion of the pattern, not in the directory portion.
+
+##### Args:
+
+
+*  <b>`pattern`</b>: A `Tensor` of type `string`. A (scalar) shell wildcard pattern.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `string`. A vector of matching filenames.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.merge_all_summaries.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.merge_all_summaries.md
deleted file mode 100644
index 40143de15d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.merge_all_summaries.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.merge_all_summaries(key='summaries')` {#merge_all_summaries}
-
-Merges all summaries collected in the default graph.
-
-##### Args:
-
-
-*  <b>`key`</b>: `GraphKey` used to collect the summaries.  Defaults to
-    `GraphKeys.SUMMARIES`.
-
-##### Returns:
-
-  If no summaries were collected, returns None.  Otherwise returns a scalar
-  `Tensor` of type `string` containing the serialized `Summary` protocol
-  buffer resulting from the merging.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.moving_average_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.moving_average_variables.md
deleted file mode 100644
index 467a666e2c..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.moving_average_variables.md
+++ /dev/null
@@ -1,13 +0,0 @@
-### `tf.moving_average_variables()` {#moving_average_variables}
-
-Returns all variables that maintain their moving averages.
-
-If an `ExponentialMovingAverage` object is created and the `apply()`
-method is called on a list of variables, these variables will
-be added to the `GraphKeys.MOVING_AVERAGE_VARIABLES` collection.
-This convenience function returns the contents of that collection.
-
-##### Returns:
-
-  A list of Variable objects.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.name_scope.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.name_scope.md
deleted file mode 100644
index a003f2327f..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.name_scope.md
+++ /dev/null
@@ -1,18 +0,0 @@
-### `tf.name_scope(name)` {#name_scope}
-
-Wrapper for `Graph.name_scope()` using the default graph.
-
-See
-[`Graph.name_scope()`](../../api_docs/python/framework.md#Graph.name_scope)
-for more details.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the scope.
-
-##### Returns:
-
-  A context manager that installs `name` as a new name scope in the
-  default graph.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.max_pool.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.max_pool.md
deleted file mode 100644
index f17efa01de..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.max_pool.md
+++ /dev/null
@@ -1,21 +0,0 @@
-### `tf.nn.max_pool(value, ksize, strides, padding, data_format='NHWC', name=None)` {#max_pool}
-
-Performs the max pooling on the input.
-
-##### Args:
-
-
-*  <b>`value`</b>: A 4-D `Tensor` with shape `[batch, height, width, channels]` and
-    type `tf.float32`.
-*  <b>`ksize`</b>: A list of ints that has length >= 4.  The size of the window for
-    each dimension of the input tensor.
-*  <b>`strides`</b>: A list of ints that has length >= 4.  The stride of the sliding
-    window for each dimension of the input tensor.
-*  <b>`padding`</b>: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
-*  <b>`data_format`</b>: A string. 'NHWC' and 'NCHW' are supported.
-*  <b>`name`</b>: Optional name for the operation.
-
-##### Returns:
-
-  A `Tensor` with type `tf.float32`.  The max pooled output tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.nce_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.nce_loss.md
new file mode 100644
index 0000000000..2fc7ab6b65
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.nce_loss.md
@@ -0,0 +1,53 @@
+### `tf.nn.nce_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=False, partition_strategy='mod', name='nce_loss')` {#nce_loss}
+
+Computes and returns the noise-contrastive estimation training loss.
+
+See [Noise-contrastive estimation: A new estimation principle for
+unnormalized statistical models]
+(http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf).
+Also see our [Candidate Sampling Algorithms Reference]
+(../../extras/candidate_sampling.pdf)
+
+Note: In the case where `num_true` > 1, we assign to each target class
+the target probability 1 / `num_true` so that the target probabilities
+sum to 1 per-example.
+
+Note: It would be useful to allow a variable number of target classes per
+example.  We hope to provide this functionality in a future release.
+For now, if you have a variable number of target classes, you can pad them
+out to a constant number by either repeating them or by padding
+with an otherwise unused class.
+
+##### Args:
+
+
+*  <b>`weights`</b>: A `Tensor` of shape `[num_classes, dim]`, or a list of `Tensor`
+      objects whose concatenation along dimension 0 has shape
+      [num_classes, dim].  The (possibly-partitioned) class embeddings.
+*  <b>`biases`</b>: A `Tensor` of shape `[num_classes]`.  The class biases.
+*  <b>`inputs`</b>: A `Tensor` of shape `[batch_size, dim]`.  The forward
+      activations of the input network.
+*  <b>`labels`</b>: A `Tensor` of type `int64` and shape `[batch_size,
+      num_true]`. The target classes.
+*  <b>`num_sampled`</b>: An `int`.  The number of classes to randomly sample per batch.
+*  <b>`num_classes`</b>: An `int`. The number of possible classes.
+*  <b>`num_true`</b>: An `int`.  The number of target classes per training example.
+*  <b>`sampled_values`</b>: a tuple of (`sampled_candidates`, `true_expected_count`,
+      `sampled_expected_count`) returned by a `*_candidate_sampler` function.
+      (if None, we default to `log_uniform_candidate_sampler`)
+*  <b>`remove_accidental_hits`</b>: A `bool`.  Whether to remove "accidental hits"
+      where a sampled class equals one of the target classes.  If set to
+      `True`, this is a "Sampled Logistic" loss instead of NCE, and we are
+      learning to generate log-odds instead of log probabilities.  See
+      our [Candidate Sampling Algorithms Reference]
+      (../../extras/candidate_sampling.pdf).
+      Default is False.
+*  <b>`partition_strategy`</b>: A string specifying the partitioning strategy, relevant
+      if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported.
+      Default is `"mod"`. See `tf.nn.embedding_lookup` for more details.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `batch_size` 1-D tensor of per-example NCE losses.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.relu6.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.relu6.md
deleted file mode 100644
index 9695e557eb..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.relu6.md
+++ /dev/null
@@ -1,15 +0,0 @@
-### `tf.nn.relu6(features, name=None)` {#relu6}
-
-Computes Rectified Linear 6: `min(max(features, 0), 6)`.
-
-##### Args:
-
-
-*  <b>`features`</b>: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
-    `int16`, or `int8`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` with the same type as `features`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.softplus.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.softplus.md
new file mode 100644
index 0000000000..c0faef9687
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.softplus.md
@@ -0,0 +1,14 @@
+### `tf.nn.softplus(features, name=None)` {#softplus}
+
+Computes softplus: `log(exp(features) + 1)`.
+
+##### Args:
+
+
+*  <b>`features`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `features`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.no_op.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.no_op.md
deleted file mode 100644
index c1b5c0824b..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.no_op.md
+++ /dev/null
@@ -1,13 +0,0 @@
-### `tf.no_op(name=None)` {#no_op}
-
-Does nothing. Only useful as a placeholder for control edges.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The created Operation.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.not_equal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.not_equal.md
new file mode 100644
index 0000000000..9c18792223
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.not_equal.md
@@ -0,0 +1,15 @@
+### `tf.not_equal(x, y, name=None)` {#not_equal}
+
+Returns the truth value of (x != y) element-wise.
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `quint8`, `qint8`, `qint32`, `string`, `bool`, `complex128`.
+*  <b>`y`</b>: A `Tensor`. Must have the same type as `x`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `bool`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.ones_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.ones_initializer.md
deleted file mode 100644
index 0ddbc8b801..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.ones_initializer.md
+++ /dev/null
@@ -1,4 +0,0 @@
-### `tf.ones_initializer(shape, dtype=tf.float32)` {#ones_initializer}
-
-An adaptor for ones() to match the Initializer spec.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.polygamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.polygamma.md
deleted file mode 100644
index c8b5b2578a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.polygamma.md
+++ /dev/null
@@ -1,22 +0,0 @@
-### `tf.polygamma(a, x, name=None)` {#polygamma}
-
-Compute the polygamma function \\(\psi^{(n)}(x)\\).
-
-The polygamma function is defined as:
-
-```
-\psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x)
-```
-where \\(\psi(x)\\) is the digamma function.
-
-##### Args:
-
-
-*  <b>`a`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
-*  <b>`x`</b>: A `Tensor`. Must have the same type as `a`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `a`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.python_io.TFRecordWriter.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.python_io.TFRecordWriter.md
new file mode 100644
index 0000000000..4a67724209
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.python_io.TFRecordWriter.md
@@ -0,0 +1,41 @@
+A class to write records to a TFRecords file.
+
+This class implements `__enter__` and `__exit__`, and can be used
+in `with` blocks like a normal file.
+
+- - -
+
+#### `tf.python_io.TFRecordWriter.__init__(path)` {#TFRecordWriter.__init__}
+
+Opens file `path` and creates a `TFRecordWriter` writing to it.
+
+##### Args:
+
+
+*  <b>`path`</b>: The path to the TFRecords file.
+
+##### Raises:
+
+
+*  <b>`IOError`</b>: If `path` cannot be opened for writing.
+
+
+- - -
+
+#### `tf.python_io.TFRecordWriter.write(record)` {#TFRecordWriter.write}
+
+Write a string record to the file.
+
+##### Args:
+
+
+*  <b>`record`</b>: str
+
+
+- - -
+
+#### `tf.python_io.TFRecordWriter.close()` {#TFRecordWriter.close}
+
+Close the file.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_normal.md
deleted file mode 100644
index 1344423202..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_normal.md
+++ /dev/null
@@ -1,23 +0,0 @@
-### `tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)` {#random_normal}
-
-Outputs random values from a normal distribution.
-
-##### Args:
-
-
-*  <b>`shape`</b>: A 1-D integer Tensor or Python array. The shape of the output tensor.
-*  <b>`mean`</b>: A 0-D Tensor or Python value of type `dtype`. The mean of the normal
-    distribution.
-*  <b>`stddev`</b>: A 0-D Tensor or Python value of type `dtype`. The standard deviation
-    of the normal distribution.
-*  <b>`dtype`</b>: The type of the output.
-*  <b>`seed`</b>: A Python integer. Used to create a random seed for the distribution.
-    See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A tensor of the specified shape filled with random normal values.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_normal_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_normal_initializer.md
new file mode 100644
index 0000000000..9f229e3b1c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.random_normal_initializer.md
@@ -0,0 +1,25 @@
+### `tf.random_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)` {#random_normal_initializer}
+
+Returns an initializer that generates tensors with a normal distribution.
+
+##### Args:
+
+
+*  <b>`mean`</b>: a python scalar or a scalar tensor. Mean of the random values
+    to generate.
+*  <b>`stddev`</b>: a python scalar or a scalar tensor. Standard deviation of the
+    random values to generate.
+*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
+    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
+    for behavior.
+*  <b>`dtype`</b>: The data type. Only floating point types are supported.
+
+##### Returns:
+
+  An initializer that generates tensors with a normal distribution.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.reduce_any.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.reduce_any.md
new file mode 100644
index 0000000000..58a911a8cf
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.reduce_any.md
@@ -0,0 +1,35 @@
+### `tf.reduce_any(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_any}
+
+Computes the "logical or" of elements across dimensions of a tensor.
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+For example:
+
+```python
+# 'x' is [[True,  True]
+#         [False, False]]
+tf.reduce_any(x) ==> True
+tf.reduce_any(x, 0) ==> [True, True]
+tf.reduce_any(x, 1) ==> [True, False]
+```
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The boolean tensor to reduce.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.reduce_sum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.reduce_sum.md
deleted file mode 100644
index edbb1ab055..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.reduce_sum.md
+++ /dev/null
@@ -1,37 +0,0 @@
-### `tf.reduce_sum(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_sum}
-
-Computes the sum of elements across dimensions of a tensor.
-
-Reduces `input_tensor` along the dimensions given in `reduction_indices`.
-Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
-entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
-are retained with length 1.
-
-If `reduction_indices` has no entries, all dimensions are reduced, and a
-tensor with a single element is returned.
-
-For example:
-
-```python
-# 'x' is [[1, 1, 1]
-#         [1, 1, 1]]
-tf.reduce_sum(x) ==> 6
-tf.reduce_sum(x, 0) ==> [2, 2, 2]
-tf.reduce_sum(x, 1) ==> [3, 3]
-tf.reduce_sum(x, 1, keep_dims=True) ==> [[3], [3]]
-tf.reduce_sum(x, [0, 1]) ==> 6
-```
-
-##### Args:
-
-
-*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
-*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the default),
-    reduces all dimensions.
-*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  The reduced tensor.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.reset_default_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.reset_default_graph.md
new file mode 100644
index 0000000000..ae5a906a0d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.reset_default_graph.md
@@ -0,0 +1,10 @@
+### `tf.reset_default_graph()` {#reset_default_graph}
+
+Clears the default graph stack and resets the global default graph.
+
+NOTE: The default graph is a property of the current thread. This
+function applies only to the current thread.  Calling this function while
+a `tf.Session` or `tf.InteractiveSession` is active will result in undefined
+behavior. Using any previously created `tf.Operation` or `tf.Tensor` objects
+after calling this function will result in undefined behavior.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.rsqrt.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.rsqrt.md
new file mode 100644
index 0000000000..5e8b1bc917
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.rsqrt.md
@@ -0,0 +1,16 @@
+### `tf.rsqrt(x, name=None)` {#rsqrt}
+
+Computes reciprocal of square root of x element-wise.
+
+I.e., \\(y = 1 / \sqrt{x}\\).
+
+##### Args:
+
+
+*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `x`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.scalar_mul.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.scalar_mul.md
new file mode 100644
index 0000000000..5af291597d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.scalar_mul.md
@@ -0,0 +1,23 @@
+### `tf.scalar_mul(scalar, x)` {#scalar_mul}
+
+Multiplies a scalar times a `Tensor` or `IndexedSlices` object.
+
+Intended for use in gradient code which might deal with `IndexedSlices`
+objects, which are easy to multiply by a scalar but more expensive to
+multiply with arbitrary tensors.
+
+##### Args:
+
+
+*  <b>`scalar`</b>: A 0-D scalar `Tensor`. Must have known shape.
+*  <b>`x`</b>: A `Tensor` or `IndexedSlices` to be scaled.
+
+##### Returns:
+
+  `scalar * x` of the same type (`Tensor` or `IndexedSlices`) as `x`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if scalar is not a 0-D `scalar`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.set_random_seed.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.set_random_seed.md
new file mode 100644
index 0000000000..af817dbafa
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.set_random_seed.md
@@ -0,0 +1,98 @@
+### `tf.set_random_seed(seed)` {#set_random_seed}
+
+Sets the graph-level random seed.
+
+Operations that rely on a random seed actually derive it from two seeds:
+the graph-level and operation-level seeds. This sets the graph-level seed.
+
+Its interactions with operation-level seeds is as follows:
+
+  1. If neither the graph-level nor the operation seed is set:
+    A random seed is used for this op.
+  2. If the graph-level seed is set, but the operation seed is not:
+    The system deterministically picks an operation seed in conjunction
+    with the graph-level seed so that it gets a unique random sequence.
+  3. If the graph-level seed is not set, but the operation seed is set:
+    A default graph-level seed and the specified operation seed are used to
+    determine the random sequence.
+  4. If both the graph-level and the operation seed are set:
+    Both seeds are used in conjunction to determine the random sequence.
+
+To illustrate the user-visible effects, consider these examples:
+
+To generate different sequences across sessions, set neither
+graph-level nor op-level seeds:
+
+```python
+a = tf.random_uniform([1])
+b = tf.random_normal([1])
+
+print("Session 1")
+with tf.Session() as sess1:
+  print(sess1.run(a))  # generates 'A1'
+  print(sess1.run(a))  # generates 'A2'
+  print(sess1.run(b))  # generates 'B1'
+  print(sess1.run(b))  # generates 'B2'
+
+print("Session 2")
+with tf.Session() as sess2:
+  print(sess2.run(a))  # generates 'A3'
+  print(sess2.run(a))  # generates 'A4'
+  print(sess2.run(b))  # generates 'B3'
+  print(sess2.run(b))  # generates 'B4'
+```
+
+To generate the same repeatable sequence for an op across sessions, set the
+seed for the op:
+
+```python
+a = tf.random_uniform([1], seed=1)
+b = tf.random_normal([1])
+
+# Repeatedly running this block with the same graph will generate the same
+# sequence of values for 'a', but different sequences of values for 'b'.
+print("Session 1")
+with tf.Session() as sess1:
+  print(sess1.run(a))  # generates 'A1'
+  print(sess1.run(a))  # generates 'A2'
+  print(sess1.run(b))  # generates 'B1'
+  print(sess1.run(b))  # generates 'B2'
+
+print("Session 2")
+with tf.Session() as sess2:
+  print(sess2.run(a))  # generates 'A1'
+  print(sess2.run(a))  # generates 'A2'
+  print(sess2.run(b))  # generates 'B3'
+  print(sess2.run(b))  # generates 'B4'
+```
+
+To make the random sequences generated by all ops be repeatable across
+sessions, set a graph-level seed:
+
+```python
+tf.set_random_seed(1234)
+a = tf.random_uniform([1])
+b = tf.random_normal([1])
+
+# Repeatedly running this block with the same graph will generate different
+# sequences of 'a' and 'b'.
+print("Session 1")
+with tf.Session() as sess1:
+  print(sess1.run(a))  # generates 'A1'
+  print(sess1.run(a))  # generates 'A2'
+  print(sess1.run(b))  # generates 'B1'
+  print(sess1.run(b))  # generates 'B2'
+
+print("Session 2")
+with tf.Session() as sess2:
+  print(sess2.run(a))  # generates 'A1'
+  print(sess2.run(a))  # generates 'A2'
+  print(sess2.run(b))  # generates 'B1'
+  print(sess2.run(b))  # generates 'B2'
+```
+
+##### Args:
+
+
+*  <b>`seed`</b>: integer.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.size.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.size.md
new file mode 100644
index 0000000000..67f1bc4885
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.size.md
@@ -0,0 +1,24 @@
+### `tf.size(input, name=None)` {#size}
+
+Returns the size of a tensor.
+
+This operation returns an integer representing the number of elements in
+`input`.
+
+For example:
+
+```prettyprint
+# 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
+size(t) ==> 12
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` of type `int32`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.slice.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.slice.md
new file mode 100644
index 0000000000..6da47df0b0
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.slice.md
@@ -0,0 +1,47 @@
+### `tf.slice(input_, begin, size, name=None)` {#slice}
+
+Extracts a slice from a tensor.
+
+This operation extracts a slice of size `size` from a tensor `input` starting
+at the location specified by `begin`. The slice `size` is represented as a
+tensor shape, where `size[i]` is the number of elements of the 'i'th dimension
+of `input` that you want to slice. The starting location (`begin`) for the
+slice is represented as an offset in each dimension of `input`. In other
+words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
+want to slice from.
+
+`begin` is zero-based; `size` is one-based. If `size[i]` is -1,
+all remaining elements in dimension i are included in the
+slice. In other words, this is equivalent to setting:
+
+`size[i] = input.dim_size(i) - begin[i]`
+
+This operation requires that:
+
+`0 <= begin[i] <= begin[i] + size[i] <= Di  for i in [0, n]`
+
+For example:
+
+```
+# 'input' is [[[1, 1, 1], [2, 2, 2]],
+#             [[3, 3, 3], [4, 4, 4]],
+#             [[5, 5, 5], [6, 6, 6]]]
+tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]]
+tf.slice(input, [1, 0, 0], [1, 2, 3]) ==> [[[3, 3, 3],
+                                            [4, 4, 4]]]
+tf.slice(input, [1, 0, 0], [2, 1, 3]) ==> [[[3, 3, 3]],
+                                           [[5, 5, 5]]]
+```
+
+##### Args:
+
+
+*  <b>`input_`</b>: A `Tensor`.
+*  <b>`begin`</b>: An `int32` or `int64` `Tensor`.
+*  <b>`size`</b>: An `int32` or `int64` `Tensor`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` the same type as `input`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_reorder.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_reorder.md
deleted file mode 100644
index 1e7b8fd857..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_reorder.md
+++ /dev/null
@@ -1,41 +0,0 @@
-### `tf.sparse_reorder(sp_input, name=None)` {#sparse_reorder}
-
-Reorders a `SparseTensor` into the canonical, row-major ordering.
-
-Note that by convention, all sparse ops preserve the canonical ordering
-along increasing dimension number. The only time ordering can be violated
-is during manual manipulation of the indices and values to add entries.
-
-Reordering does not affect the shape of the `SparseTensor`.
-
-For example, if `sp_input` has shape `[4, 5]` and `indices` / `values`:
-
-    [0, 3]: b
-    [0, 1]: a
-    [3, 1]: d
-    [2, 0]: c
-
-then the output will be a `SparseTensor` of shape `[4, 5]` and
-`indices` / `values`:
-
-    [0, 1]: a
-    [0, 3]: b
-    [2, 0]: c
-    [3, 1]: d
-
-##### Args:
-
-
-*  <b>`sp_input`</b>: The input `SparseTensor`.
-*  <b>`name`</b>: A name prefix for the returned tensors (optional)
-
-##### Returns:
-
-  A `SparseTensor` with the same shape and non-empty values, but in
-  canonical ordering.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sqrt_n.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sqrt_n.md
new file mode 100644
index 0000000000..bc665a42a8
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sqrt_n.md
@@ -0,0 +1,26 @@
+### `tf.sparse_segment_sqrt_n(data, indices, segment_ids, name=None)` {#sparse_segment_sqrt_n}
+
+Computes the sum along sparse segments of a tensor divided by the sqrt of N.
+
+N is the size of the segment being reduced.
+
+Read [the section on
+Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
+of segments.
+
+##### Args:
+
+
+*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`indices`</b>: A `Tensor` of type `int32`.
+    A 1-D tensor. Has same rank as `segment_ids`.
+*  <b>`segment_ids`</b>: A `Tensor` of type `int32`.
+    A 1-D tensor. Values should be sorted and can be repeated.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `data`.
+  Has same shape as data, except for dimension 0 which
+  has size `k`, the number of segments.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sqrt_n_grad.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sqrt_n_grad.md
new file mode 100644
index 0000000000..2a2e0c9e33
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sqrt_n_grad.md
@@ -0,0 +1,24 @@
+### `tf.sparse_segment_sqrt_n_grad(grad, indices, segment_ids, output_dim0, name=None)` {#sparse_segment_sqrt_n_grad}
+
+Computes gradients for SparseSegmentSqrtN.
+
+Returns tensor "output" with same shape as grad, except for dimension 0 whose
+value is output_dim0.
+
+##### Args:
+
+
+*  <b>`grad`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+    gradient propagated to the SparseSegmentSqrtN op.
+*  <b>`indices`</b>: A `Tensor` of type `int32`.
+    indices passed to the corresponding SparseSegmentSqrtN op.
+*  <b>`segment_ids`</b>: A `Tensor` of type `int32`.
+    segment_ids passed to the corresponding SparseSegmentSqrtN op.
+*  <b>`output_dim0`</b>: A `Tensor` of type `int32`.
+    dimension 0 of "data" passed to SparseSegmentSqrtN op.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `grad`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sum.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sum.md
deleted file mode 100644
index 6691a6b7bc..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_segment_sum.md
+++ /dev/null
@@ -1,50 +0,0 @@
-### `tf.sparse_segment_sum(data, indices, segment_ids, name=None)` {#sparse_segment_sum}
-
-Computes the sum along sparse segments of a tensor.
-
-Read [the section on
-Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
-of segments.
-
-Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first
-dimension, selecting a subset of dimension 0, specified by `indices`.
-
-For example:
-
-```prettyprint
-c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
-
-# Select two rows, one segment.
-tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
-  ==> [[0 0 0 0]]
-
-# Select two rows, two segment.
-tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
-  ==> [[ 1  2  3  4]
-       [-1 -2 -3 -4]]
-
-# Select all rows, two segments.
-tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
-  ==> [[0 0 0 0]
-       [5 6 7 8]]
-
-# Which is equivalent to:
-tf.segment_sum(c, tf.constant([0, 0, 1]))
-```
-
-##### Args:
-
-
-*  <b>`data`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `half`.
-*  <b>`indices`</b>: A `Tensor` of type `int32`.
-    A 1-D tensor. Has same rank as `segment_ids`.
-*  <b>`segment_ids`</b>: A `Tensor` of type `int32`.
-    A 1-D tensor. Values should be sorted and can be repeated.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `data`.
-  Has same shape as data, except for dimension 0 which
-  has size `k`, the number of segments.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_split.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_split.md
new file mode 100644
index 0000000000..e3e608a9e2
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_split.md
@@ -0,0 +1,40 @@
+### `tf.sparse_split(split_dim, num_split, sp_input, name=None)` {#sparse_split}
+
+Split a `SparseTensor` into `num_split` tensors along `split_dim`.
+
+If the `sp_input.shape[split_dim]` is not an integer multiple of `num_split`
+each slice starting from 0:`shape[split_dim] % num_split` gets extra one
+dimension. For example, if `split_dim = 1` and `num_split = 2` and the
+input is:
+
+    input_tensor = shape = [2, 7]
+    [    a   d e  ]
+    [b c          ]
+
+Graphically the output tensors are:
+
+    output_tensor[0] =
+    [    a ]
+    [b c   ]
+
+    output_tensor[1] =
+    [ d e  ]
+    [      ]
+
+##### Args:
+
+
+*  <b>`split_dim`</b>: A 0-D `int32` `Tensor`. The dimension along which to split.
+*  <b>`num_split`</b>: A Python integer. The number of ways to split.
+*  <b>`sp_input`</b>: The `SparseTensor` to split.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  `num_split` `SparseTensor` objects resulting from splitting `value`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_to_indicator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_to_indicator.md
new file mode 100644
index 0000000000..8ee455be32
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sparse_to_indicator.md
@@ -0,0 +1,52 @@
+### `tf.sparse_to_indicator(sp_input, vocab_size, name=None)` {#sparse_to_indicator}
+
+Converts a `SparseTensor` of ids into a dense bool indicator tensor.
+
+The last dimension of `sp_input.indices` is discarded and replaced with
+the values of `sp_input`.  If `sp_input.shape = [D0, D1, ..., Dn, K]`, then
+`output.shape = [D0, D1, ..., Dn, vocab_size]`, where
+
+    output[d_0, d_1, ..., d_n, sp_input[d_0, d_1, ..., d_n, k]] = True
+
+and False elsewhere in `output`.
+
+For example, if `sp_input.shape = [2, 3, 4]` with non-empty values:
+
+    [0, 0, 0]: 0
+    [0, 1, 0]: 10
+    [1, 0, 3]: 103
+    [1, 1, 2]: 150
+    [1, 1, 3]: 149
+    [1, 1, 4]: 150
+    [1, 2, 1]: 121
+
+and `vocab_size = 200`, then the output will be a `[2, 3, 200]` dense bool
+tensor with False everywhere except at positions
+
+    (0, 0, 0), (0, 1, 10), (1, 0, 103), (1, 1, 149), (1, 1, 150),
+    (1, 2, 121).
+
+Note that repeats are allowed in the input SparseTensor.
+This op is useful for converting `SparseTensor`s into dense formats for
+compatibility with ops that expect dense tensors.
+
+The input `SparseTensor` must be in row-major order.
+
+##### Args:
+
+
+*  <b>`sp_input`</b>: A `SparseTensor` with `values` property of type `int32` or
+    `int64`.
+*  <b>`vocab_size`</b>: A scalar int64 Tensor (or Python int) containing the new size
+    of the last dimension, `all(0 <= sp_input.values < vocab_size)`.
+*  <b>`name`</b>: A name prefix for the returned tensors (optional)
+
+##### Returns:
+
+  A dense bool indicator tensor representing the indices with specified value.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `sp_input` is not a `SparseTensor`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sqrt.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sqrt.md
deleted file mode 100644
index 250817f3bf..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.sqrt.md
+++ /dev/null
@@ -1,16 +0,0 @@
-### `tf.sqrt(x, name=None)` {#sqrt}
-
-Computes square root of x element-wise.
-
-I.e., \\(y = \sqrt{x} = x^{1/2}\\).
-
-##### Args:
-
-
-*  <b>`x`</b>: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `x`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.squeeze.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.squeeze.md
new file mode 100644
index 0000000000..e76c02e115
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.squeeze.md
@@ -0,0 +1,38 @@
+### `tf.squeeze(input, squeeze_dims=None, name=None)` {#squeeze}
+
+Removes dimensions of size 1 from the shape of a tensor.
+
+Given a tensor `input`, this operation returns a tensor of the same type with
+all dimensions of size 1 removed. If you don't want to remove all size 1
+dimensions, you can remove specific size 1 dimensions by specifying
+`squeeze_dims`.
+
+For example:
+
+```prettyprint
+# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
+shape(squeeze(t)) ==> [2, 3]
+```
+
+Or, to remove specific size 1 dimensions:
+
+```prettyprint
+# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
+shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
+```
+
+##### Args:
+
+
+*  <b>`input`</b>: A `Tensor`. The `input` to squeeze.
+*  <b>`squeeze_dims`</b>: An optional list of `ints`. Defaults to `[]`.
+    If specified, only squeezes the dimensions listed. The dimension
+    index starts at 0. It is an error to squeeze a dimension that is not 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `input`.
+  Contains the same data as `input`, but has one or more dimensions of
+  size 1 removed.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.stop_gradient.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.stop_gradient.md
deleted file mode 100644
index 53759f49ff..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.stop_gradient.md
+++ /dev/null
@@ -1,34 +0,0 @@
-### `tf.stop_gradient(input, name=None)` {#stop_gradient}
-
-Stops gradient computation.
-
-When executed in a graph, this op outputs its input tensor as-is.
-
-When building ops to compute gradients, this op prevents the contribution of
-its inputs to be taken into account.  Normally, the gradient generator adds ops
-to a graph to compute the derivatives of a specified 'loss' by recursively
-finding out inputs that contributed to its computation.  If you insert this op
-in the graph it inputs are masked from the gradient generator.  They are not
-taken into account for computing gradients.
-
-This is useful any time you want to compute a value with TensorFlow but need
-to pretend that the value was a constant. Some examples include:
-
-*  The *EM* algorithm where the *M-step* should not involve backpropagation
-   through the output of the *E-step*.
-*  Contrastive divergence training of Boltzmann machines where, when
-   differentiating the energy function, the training must not backpropagate
-   through the graph that generated the samples from the model.
-*  Adversarial training, where no backprop should happen through the adversarial
-   example generation process.
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor`. Has the same type as `input`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.test.compute_gradient.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.test.compute_gradient.md
new file mode 100644
index 0000000000..19b302d466
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.test.compute_gradient.md
@@ -0,0 +1,40 @@
+### `tf.test.compute_gradient(x, x_shape, y, y_shape, x_init_value=None, delta=0.001, init_targets=None)` {#compute_gradient}
+
+Computes and returns the theoretical and numerical Jacobian.
+
+If `x` or `y` is complex, the Jacobian will still be real but the
+corresponding Jacobian dimension(s) will be twice as large.  This is required
+even if both input and output is complex since TensorFlow graphs are not
+necessarily holomorphic, and may have gradients not expressible as complex
+numbers.  For example, if `x` is complex with shape `[m]` and `y` is complex
+with shape `[n]`, each Jacobian `J` will have shape `[m * 2, n * 2]` with
+
+    J[:m, :n] = d(Re y)/d(Re x)
+    J[:m, n:] = d(Im y)/d(Re x)
+    J[m:, :n] = d(Re y)/d(Im x)
+    J[m:, n:] = d(Im y)/d(Im x)
+
+##### Args:
+
+
+*  <b>`x`</b>: a tensor or list of tensors
+*  <b>`x_shape`</b>: the dimensions of x as a tuple or an array of ints. If x is a list,
+  then this is the list of shapes.
+
+*  <b>`y`</b>: a tensor
+*  <b>`y_shape`</b>: the dimensions of y as a tuple or an array of ints.
+*  <b>`x_init_value`</b>: (optional) a numpy array of the same shape as "x"
+    representing the initial value of x. If x is a list, this should be a list
+    of numpy arrays.  If this is none, the function will pick a random tensor
+    as the initial value.
+*  <b>`delta`</b>: (optional) the amount of perturbation.
+*  <b>`init_targets`</b>: list of targets to run to initialize model params.
+    TODO(mrry): remove this argument.
+
+##### Returns:
+
+  Two 2-d numpy arrays representing the theoretical and numerical
+  Jacobian for dy/dx. Each has "x_size" rows and "y_size" columns
+  where "x_size" is the number of elements in x and "y_size" is the
+  number of elements in y. If x is a list, returns a list of two numpy arrays.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.AdamOptimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.AdamOptimizer.md
new file mode 100644
index 0000000000..8667ec8ed3
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.AdamOptimizer.md
@@ -0,0 +1,49 @@
+Optimizer that implements the Adam algorithm.
+
+See [Kingma et. al., 2014](http://arxiv.org/abs/1412.6980)
+([pdf](http://arxiv.org/pdf/1412.6980.pdf)).
+
+- - -
+
+#### `tf.train.AdamOptimizer.__init__(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')` {#AdamOptimizer.__init__}
+
+Construct a new Adam optimizer.
+
+Initialization:
+
+```
+m_0 <- 0 (Initialize initial 1st moment vector)
+v_0 <- 0 (Initialize initial 2nd moment vector)
+t <- 0 (Initialize timestep)
+```
+
+The update rule for `variable` with gradient `g` uses an optimization
+described at the end of section2 of the paper:
+
+```
+t <- t + 1
+lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
+
+m_t <- beta1 * m_{t-1} + (1 - beta1) * g
+v_t <- beta2 * v_{t-1} + (1 - beta2) * g * g
+variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
+```
+
+The default value of 1e-8 for epsilon might not be a good default in
+general. For example, when training an Inception network on ImageNet a
+current good choice is 1.0 or 0.1.
+
+##### Args:
+
+
+*  <b>`learning_rate`</b>: A Tensor or a floating point value.  The learning rate.
+*  <b>`beta1`</b>: A float value or a constant float tensor.
+    The exponential decay rate for the 1st moment estimates.
+*  <b>`beta2`</b>: A float value or a constant float tensor.
+    The exponential decay rate for the 2nd moment estimates.
+*  <b>`epsilon`</b>: A small constant for numerical stability.
+*  <b>`use_locking`</b>: If True use locks for update operations.
+*  <b>`name`</b>: Optional name for the operations created when applying gradients.
+    Defaults to "Adam".
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.ClusterSpec.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.ClusterSpec.md
deleted file mode 100644
index c695781a86..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.ClusterSpec.md
+++ /dev/null
@@ -1,86 +0,0 @@
-Represents a cluster as a set of "tasks", organized into "jobs".
-
-A `tf.train.ClusterSpec` represents the set of processes that
-participate in a distributed TensorFlow computation. Every
-[`tf.train.Server`](#Server) is constructed in a particular cluster.
-
-To create a cluster with two jobs and five tasks, you specify the
-mapping from job names to lists of network addresses (typically
-hostname-port pairs).
-
-```
-cluster = tf.train.ClusterSpec({"worker": ["worker0.example.com:2222",
-                                           "worker1.example.com:2222",
-                                           "worker2.example.com:2222"],
-                                "ps": ["ps0.example.com:2222",
-                                       "ps1.example.com:2222"]})
-```
-
-- - -
-
-#### `tf.train.ClusterSpec.as_cluster_def()` {#ClusterSpec.as_cluster_def}
-
-Returns a `tf.train.ClusterDef` protocol buffer based on this cluster.
-
-
-- - -
-
-#### `tf.train.ClusterSpec.as_dict()` {#ClusterSpec.as_dict}
-
-Returns a dictionary from job names to lists of network addresses.
-
-
-
-#### Other Methods
-- - -
-
-#### `tf.train.ClusterSpec.__init__(cluster)` {#ClusterSpec.__init__}
-
-Creates a `ClusterSpec`.
-
-##### Args:
-
-
-*  <b>`cluster`</b>: A dictionary mapping one or more job names to lists of network
-    addresses, or a `tf.train.ClusterDef` protocol buffer.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `cluster` is not a dictionary mapping strings to lists
-    of strings, and not a `tf.train.ClusterDef` protobuf.
-
-
-- - -
-
-#### `tf.train.ClusterSpec.job_tasks(job_name)` {#ClusterSpec.job_tasks}
-
-Returns a list of tasks in the given job.
-
-##### Args:
-
-
-*  <b>`job_name`</b>: The string name of a job in this cluster.
-
-##### Returns:
-
-  A list of strings, corresponding to the network addresses of tasks in
-  the given job, ordered by task index.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If `job_name` does not name a job in this cluster.
-
-
-- - -
-
-#### `tf.train.ClusterSpec.jobs` {#ClusterSpec.jobs}
-
-Returns a list of job names in this cluster.
-
-##### Returns:
-
-  A list of strings, corresponding to the names of jobs in this cluster.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.ExponentialMovingAverage.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.ExponentialMovingAverage.md
deleted file mode 100644
index ea0aa48161..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.ExponentialMovingAverage.md
+++ /dev/null
@@ -1,229 +0,0 @@
-Maintains moving averages of variables by employing an exponential decay.
-
-When training a model, it is often beneficial to maintain moving averages of
-the trained parameters.  Evaluations that use averaged parameters sometimes
-produce significantly better results than the final trained values.
-
-The `apply()` method adds shadow copies of trained variables and add ops that
-maintain a moving average of the trained variables in their shadow copies.
-It is used when building the training model.  The ops that maintain moving
-averages are typically run after each training step.
-The `average()` and `average_name()` methods give access to the shadow
-variables and their names.  They are useful when building an evaluation
-model, or when restoring a model from a checkpoint file.  They help use the
-moving averages in place of the last trained values for evaluations.
-
-The moving averages are computed using exponential decay.  You specify the
-decay value when creating the `ExponentialMovingAverage` object.  The shadow
-variables are initialized with the same initial values as the trained
-variables.  When you run the ops to maintain the moving averages, each
-shadow variable is updated with the formula:
-
-  `shadow_variable -= (1 - decay) * (shadow_variable - variable)`
-
-This is mathematically equivalent to the classic formula below, but the use
-of an `assign_sub` op (the `"-="` in the formula) allows concurrent lockless
-updates to the variables:
-
-  `shadow_variable = decay * shadow_variable + (1 - decay) * variable`
-
-Reasonable values for `decay` are close to 1.0, typically in the
-multiple-nines range: 0.999, 0.9999, etc.
-
-Example usage when creating a training model:
-
-```python
-# Create variables.
-var0 = tf.Variable(...)
-var1 = tf.Variable(...)
-# ... use the variables to build a training model...
-...
-# Create an op that applies the optimizer.  This is what we usually
-# would use as a training op.
-opt_op = opt.minimize(my_loss, [var0, var1])
-
-# Create an ExponentialMovingAverage object
-ema = tf.train.ExponentialMovingAverage(decay=0.9999)
-
-# Create the shadow variables, and add ops to maintain moving averages
-# of var0 and var1.
-maintain_averages_op = ema.apply([var0, var1])
-
-# Create an op that will update the moving averages after each training
-# step.  This is what we will use in place of the usual training op.
-with tf.control_dependencies([opt_op]):
-    training_op = tf.group(maintain_averages_op)
-
-...train the model by running training_op...
-```
-
-There are two ways to use the moving averages for evaluations:
-
-*  Build a model that uses the shadow variables instead of the variables.
-   For this, use the `average()` method which returns the shadow variable
-   for a given variable.
-*  Build a model normally but load the checkpoint files to evaluate by using
-   the shadow variable names.  For this use the `average_name()` method.  See
-   the [Saver class](../../api_docs/python/train.md#Saver) for more
-   information on restoring saved variables.
-
-Example of restoring the shadow variable values:
-
-```python
-# Create a Saver that loads variables from their saved shadow values.
-shadow_var0_name = ema.average_name(var0)
-shadow_var1_name = ema.average_name(var1)
-saver = tf.train.Saver({shadow_var0_name: var0, shadow_var1_name: var1})
-saver.restore(...checkpoint filename...)
-# var0 and var1 now hold the moving average values
-```
-
-- - -
-
-#### `tf.train.ExponentialMovingAverage.__init__(decay, num_updates=None, name='ExponentialMovingAverage')` {#ExponentialMovingAverage.__init__}
-
-Creates a new ExponentialMovingAverage object.
-
-The `apply()` method has to be called to create shadow variables and add
-ops to maintain moving averages.
-
-The optional `num_updates` parameter allows one to tweak the decay rate
-dynamically. .  It is typical to pass the count of training steps, usually
-kept in a variable that is incremented at each step, in which case the
-decay rate is lower at the start of training.  This makes moving averages
-move faster.  If passed, the actual decay rate used is:
-
-  `min(decay, (1 + num_updates) / (10 + num_updates))`
-
-##### Args:
-
-
-*  <b>`decay`</b>: Float.  The decay to use.
-*  <b>`num_updates`</b>: Optional count of number of updates applied to variables.
-*  <b>`name`</b>: String. Optional prefix name to use for the name of ops added in
-    `apply()`.
-
-
-- - -
-
-#### `tf.train.ExponentialMovingAverage.apply(var_list=None)` {#ExponentialMovingAverage.apply}
-
-Maintains moving averages of variables.
-
-`var_list` must be a list of `Variable` or `Tensor` objects.  This method
-creates shadow variables for all elements of `var_list`.  Shadow variables
-for `Variable` objects are initialized to the variable's initial value.
-They will be added to the `GraphKeys.MOVING_AVERAGE_VARIABLES` collection.
-For `Tensor` objects, the shadow variables are initialized to 0.
-
-shadow variables are created with `trainable=False` and added to the
-`GraphKeys.ALL_VARIABLES` collection.  They will be returned by calls to
-`tf.all_variables()`.
-
-Returns an op that updates all shadow variables as described above.
-
-Note that `apply()` can be called multiple times with different lists of
-variables.
-
-##### Args:
-
-
-*  <b>`var_list`</b>: A list of Variable or Tensor objects. The variables
-    and Tensors must be of types float32 or float64.
-
-##### Returns:
-
-  An Operation that updates the moving averages.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If the arguments are not all float32 or float64.
-*  <b>`ValueError`</b>: If the moving average of one of the variables is already
-    being computed.
-
-
-- - -
-
-#### `tf.train.ExponentialMovingAverage.average_name(var)` {#ExponentialMovingAverage.average_name}
-
-Returns the name of the `Variable` holding the average for `var`.
-
-The typical scenario for `ExponentialMovingAverage` is to compute moving
-averages of variables during training, and restore the variables from the
-computed moving averages during evaluations.
-
-To restore variables, you have to know the name of the shadow variables.
-That name and the original variable can then be passed to a `Saver()` object
-to restore the variable from the moving average value with:
-  `saver = tf.train.Saver({ema.average_name(var): var})`
-
-`average_name()` can be called whether or not `apply()` has been called.
-
-##### Args:
-
-
-*  <b>`var`</b>: A `Variable` object.
-
-##### Returns:
-
-  A string: The name of the variable that will be used or was used
-  by the `ExponentialMovingAverage class` to hold the moving average of
-  `var`.
-
-
-- - -
-
-#### `tf.train.ExponentialMovingAverage.average(var)` {#ExponentialMovingAverage.average}
-
-Returns the `Variable` holding the average of `var`.
-
-##### Args:
-
-
-*  <b>`var`</b>: A `Variable` object.
-
-##### Returns:
-
-  A `Variable` object or `None` if the moving average of `var`
-  is not maintained..
-
-
-- - -
-
-#### `tf.train.ExponentialMovingAverage.variables_to_restore(moving_avg_variables=None)` {#ExponentialMovingAverage.variables_to_restore}
-
-Returns a map of names to `Variables` to restore.
-
-If a variable has a moving average, use the moving average variable name as
-the restore name; otherwise, use the variable name.
-
-For example,
-
-```python
-  variables_to_restore = ema.variables_to_restore()
-  saver = tf.train.Saver(variables_to_restore)
-```
-
-Below is an example of such mapping:
-
-```
-  conv/batchnorm/gamma/ExponentialMovingAverage: conv/batchnorm/gamma,
-  conv_4/conv2d_params/ExponentialMovingAverage: conv_4/conv2d_params,
-  global_step: global_step
-```
-
-##### Args:
-
-
-*  <b>`moving_avg_variables`</b>: a list of variables that require to use of the
-    moving variable name to be restored. If None, it will default to
-    variables.moving_average_variables() + variables.trainable_variables()
-
-##### Returns:
-
-  A map from restore_names to variables. The restore_name can be the
-  moving_average version of the variable name if it exist, or the original
-  variable name.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.Optimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.Optimizer.md
deleted file mode 100644
index d5d8bb13dd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.Optimizer.md
+++ /dev/null
@@ -1,255 +0,0 @@
-Base class for optimizers.
-
-This class defines the API to add Ops to train a model.  You never use this
-class directly, but instead instantiate one of its subclasses such as
-`GradientDescentOptimizer`, `AdagradOptimizer`, or `MomentumOptimizer`.
-
-### Usage
-
-```python
-# Create an optimizer with the desired parameters.
-opt = GradientDescentOptimizer(learning_rate=0.1)
-# Add Ops to the graph to minimize a cost by updating a list of variables.
-# "cost" is a Tensor, and the list of variables contains tf.Variable
-# objects.
-opt_op = opt.minimize(cost, var_list=<list of variables>)
-```
-
-In the training program you will just have to run the returned Op.
-
-```python
-# Execute opt_op to do one step of training:
-opt_op.run()
-```
-
-### Processing gradients before applying them.
-
-Calling `minimize()` takes care of both computing the gradients and
-applying them to the variables.  If you want to process the gradients
-before applying them you can instead use the optimizer in three steps:
-
-1.  Compute the gradients with `compute_gradients()`.
-2.  Process the gradients as you wish.
-3.  Apply the processed gradients with `apply_gradients()`.
-
-Example:
-
-```python
-# Create an optimizer.
-opt = GradientDescentOptimizer(learning_rate=0.1)
-
-# Compute the gradients for a list of variables.
-grads_and_vars = opt.compute_gradients(loss, <list of variables>)
-
-# grads_and_vars is a list of tuples (gradient, variable).  Do whatever you
-# need to the 'gradient' part, for example cap them, etc.
-capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars]
-
-# Ask the optimizer to apply the capped gradients.
-opt.apply_gradients(capped_grads_and_vars)
-```
-
-- - -
-
-#### `tf.train.Optimizer.__init__(use_locking, name)` {#Optimizer.__init__}
-
-Create a new Optimizer.
-
-This must be called by the constructors of subclasses.
-
-##### Args:
-
-
-*  <b>`use_locking`</b>: Bool. If True apply use locks to prevent concurrent updates
-    to variables.
-*  <b>`name`</b>: A non-empty string.  The name to use for accumulators created
-    for the optimizer.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If name is malformed.
-
-
-
-- - -
-
-#### `tf.train.Optimizer.minimize(loss, global_step=None, var_list=None, gate_gradients=1, aggregation_method=None, colocate_gradients_with_ops=False, name=None, grad_loss=None)` {#Optimizer.minimize}
-
-Add operations to minimize `loss` by updating `var_list`.
-
-This method simply combines calls `compute_gradients()` and
-`apply_gradients()`. If you want to process the gradient before applying
-them call `compute_gradients()` and `apply_gradients()` explicitly instead
-of using this function.
-
-##### Args:
-
-
-*  <b>`loss`</b>: A `Tensor` containing the value to minimize.
-*  <b>`global_step`</b>: Optional `Variable` to increment by one after the
-    variables have been updated.
-*  <b>`var_list`</b>: Optional list of `Variable` objects to update to minimize
-    `loss`.  Defaults to the list of variables collected in the graph
-    under the key `GraphKeys.TRAINABLE_VARIABLES`.
-*  <b>`gate_gradients`</b>: How to gate the computation of gradients.  Can be
-    `GATE_NONE`, `GATE_OP`, or  `GATE_GRAPH`.
-*  <b>`aggregation_method`</b>: Specifies the method used to combine gradient terms.
-    Valid values are defined in the class `AggregationMethod`.
-*  <b>`colocate_gradients_with_ops`</b>: If True, try colocating gradients with
-    the corresponding op.
-*  <b>`name`</b>: Optional name for the returned operation.
-*  <b>`grad_loss`</b>: Optional. A `Tensor` holding the gradient computed for `loss`.
-
-##### Returns:
-
-  An Operation that updates the variables in `var_list`.  If `global_step`
-  was not `None`, that operation also increments `global_step`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If some of the variables are not `Variable` objects.
-
-
-- - -
-
-#### `tf.train.Optimizer.compute_gradients(loss, var_list=None, gate_gradients=1, aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None)` {#Optimizer.compute_gradients}
-
-Compute gradients of `loss` for the variables in `var_list`.
-
-This is the first part of `minimize()`.  It returns a list
-of (gradient, variable) pairs where "gradient" is the gradient
-for "variable".  Note that "gradient" can be a `Tensor`, an
-`IndexedSlices`, or `None` if there is no gradient for the
-given variable.
-
-##### Args:
-
-
-*  <b>`loss`</b>: A Tensor containing the value to minimize.
-*  <b>`var_list`</b>: Optional list of tf.Variable to update to minimize
-    `loss`.  Defaults to the list of variables collected in the graph
-    under the key `GraphKey.TRAINABLE_VARIABLES`.
-*  <b>`gate_gradients`</b>: How to gate the computation of gradients.  Can be
-    `GATE_NONE`, `GATE_OP`, or `GATE_GRAPH`.
-*  <b>`aggregation_method`</b>: Specifies the method used to combine gradient terms.
-    Valid values are defined in the class `AggregationMethod`.
-*  <b>`colocate_gradients_with_ops`</b>: If True, try colocating gradients with
-    the corresponding op.
-*  <b>`grad_loss`</b>: Optional. A `Tensor` holding the gradient computed for `loss`.
-
-##### Returns:
-
-  A list of (gradient, variable) pairs.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `var_list` contains anything else than `Variable` objects.
-*  <b>`ValueError`</b>: If some arguments are invalid.
-
-
-- - -
-
-#### `tf.train.Optimizer.apply_gradients(grads_and_vars, global_step=None, name=None)` {#Optimizer.apply_gradients}
-
-Apply gradients to variables.
-
-This is the second part of `minimize()`. It returns an `Operation` that
-applies gradients.
-
-##### Args:
-
-
-*  <b>`grads_and_vars`</b>: List of (gradient, variable) pairs as returned by
-    `compute_gradients()`.
-*  <b>`global_step`</b>: Optional `Variable` to increment by one after the
-    variables have been updated.
-*  <b>`name`</b>: Optional name for the returned operation.  Default to the
-    name passed to the `Optimizer` constructor.
-
-##### Returns:
-
-  An `Operation` that applies the specified gradients. If `global_step`
-  was not None, that operation also increments `global_step`.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: If `grads_and_vars` is malformed.
-*  <b>`ValueError`</b>: If none of the variables have gradients.
-
-
-
-### Gating Gradients
-
-Both `minimize()` and `compute_gradients()` accept a `gate_gradient` argument
-that controls the degree of parallelism during the application of the
-gradients.
-
-The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
-
-<b>`GATE_NONE`</b>: Compute and apply gradients in parallel.  This provides
-the maximum parallelism in execution, at the cost of some non-reproducibility
-in the results.  For example the two gradients of `matmul` depend on the input
-values: With `GATE_NONE` one of the gradients could be applied to one of the
-inputs _before_ the other gradient is computed resulting in non-reproducible
-results.
-
-<b>`GATE_OP`</b>: For each Op, make sure all gradients are computed before
-they are used.  This prevents race conditions for Ops that generate gradients
-for multiple inputs where the gradients depend on the inputs.
-
-<b>`GATE_GRAPH`</b>: Make sure all gradients for all variables are computed
-before any one of them is used.  This provides the least parallelism but can
-be useful if you want to process all gradients before applying any of them.
-
-### Slots
-
-Some optimizer subclasses, such as `MomentumOptimizer` and `AdagradOptimizer`
-allocate and manage additional variables associated with the variables to
-train.  These are called <i>Slots</i>.  Slots have names and you can ask the
-optimizer for the names of the slots that it uses.  Once you have a slot name
-you can ask the optimizer for the variable it created to hold the slot value.
-
-This can be useful if you want to log debug a training algorithm, report stats
-about the slots, etc.
-
-- - -
-
-#### `tf.train.Optimizer.get_slot_names()` {#Optimizer.get_slot_names}
-
-Return a list of the names of slots created by the `Optimizer`.
-
-See `get_slot()`.
-
-##### Returns:
-
-  A list of strings.
-
-
-- - -
-
-#### `tf.train.Optimizer.get_slot(var, name)` {#Optimizer.get_slot}
-
-Return a slot named `name` created for `var` by the Optimizer.
-
-Some `Optimizer` subclasses use additional variables.  For example
-`Momentum` and `Adagrad` use variables to accumulate updates.  This method
-gives access to these `Variable` objects if for some reason you need them.
-
-Use `get_slot_names()` to get the list of slot names created by the
-`Optimizer`.
-
-##### Args:
-
-
-*  <b>`var`</b>: A variable passed to `minimize()` or `apply_gradients()`.
-*  <b>`name`</b>: A string.
-
-##### Returns:
-
-  The `Variable` for the slot if it was created, `None` otherwise.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.QueueRunner.from_proto.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.QueueRunner.from_proto.md
deleted file mode 100644
index 2caa8f769a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.QueueRunner.from_proto.md
+++ /dev/null
@@ -1,4 +0,0 @@
-#### `tf.train.QueueRunner.from_proto(queue_runner_def)` {#QueueRunner.from_proto}
-
-Returns a `QueueRunner` object created from `queue_runner_def`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.QueueRunner.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.QueueRunner.md
deleted file mode 100644
index 812dc2b5bd..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.QueueRunner.md
+++ /dev/null
@@ -1,161 +0,0 @@
-Holds a list of enqueue operations for a queue, each to be run in a thread.
-
-Queues are a convenient TensorFlow mechanism to compute tensors
-asynchronously using multiple threads. For example in the canonical 'Input
-Reader' setup one set of threads generates filenames in a queue; a second set
-of threads read records from the files, processes them, and enqueues tensors
-on a second queue; a third set of threads dequeues these input records to
-construct batches and runs them through training operations.
-
-There are several delicate issues when running multiple threads that way:
-closing the queues in sequence as the input is exhausted, correctly catching
-and reporting exceptions, etc.
-
-The `QueueRunner`, combined with the `Coordinator`, helps handle these issues.
-- - -
-
-#### `tf.train.QueueRunner.__init__(queue=None, enqueue_ops=None, close_op=None, cancel_op=None, queue_runner_def=None)` {#QueueRunner.__init__}
-
-Create a QueueRunner.
-
-On construction the `QueueRunner` adds an op to close the queue.  That op
-will be run if the enqueue ops raise exceptions.
-
-When you later call the `create_threads()` method, the `QueueRunner` will
-create one thread for each op in `enqueue_ops`.  Each thread will run its
-enqueue op in parallel with the other threads.  The enqueue ops do not have
-to all be the same op, but it is expected that they all enqueue tensors in
-`queue`.
-
-##### Args:
-
-
-*  <b>`queue`</b>: A `Queue`.
-*  <b>`enqueue_ops`</b>: List of enqueue ops to run in threads later.
-*  <b>`close_op`</b>: Op to close the queue. Pending enqueue ops are preserved.
-*  <b>`cancel_op`</b>: Op to close the queue and cancel pending enqueue ops.
-*  <b>`queue_runner_def`</b>: Optional `QueueRunnerDef` protocol buffer. If specified,
-    recreates the QueueRunner from its contents. `queue_runner_def` and the
-    other arguments are mutually exclusive.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If both `queue_runner_def` and `queue` are both specified.
-*  <b>`ValueError`</b>: If `queue` or `enqueue_ops` are not provided when not
-    restoring from `queue_runner_def`.
-
-
-- - -
-
-#### `tf.train.QueueRunner.cancel_op` {#QueueRunner.cancel_op}
-
-
-
-
-- - -
-
-#### `tf.train.QueueRunner.close_op` {#QueueRunner.close_op}
-
-
-
-
-- - -
-
-#### `tf.train.QueueRunner.create_threads(sess, coord=None, daemon=False, start=False)` {#QueueRunner.create_threads}
-
-Create threads to run the enqueue ops.
-
-This method requires a session in which the graph was launched.  It creates
-a list of threads, optionally starting them.  There is one thread for each
-op passed in `enqueue_ops`.
-
-The `coord` argument is an optional coordinator, that the threads will use
-to terminate together and report exceptions.  If a coordinator is given,
-this method starts an additional thread to close the queue when the
-coordinator requests a stop.
-
-This method may be called again as long as all threads from a previous call
-have stopped.
-
-##### Args:
-
-
-*  <b>`sess`</b>: A `Session`.
-*  <b>`coord`</b>: Optional `Coordinator` object for reporting errors and checking
-    stop conditions.
-*  <b>`daemon`</b>: Boolean.  If `True` make the threads daemon threads.
-*  <b>`start`</b>: Boolean.  If `True` starts the threads.  If `False` the
-    caller must call the `start()` method of the returned threads.
-
-##### Returns:
-
-  A list of threads.
-
-##### Raises:
-
-
-*  <b>`RuntimeError`</b>: If threads from a previous call to `create_threads()` are
-  still running.
-
-
-- - -
-
-#### `tf.train.QueueRunner.enqueue_ops` {#QueueRunner.enqueue_ops}
-
-
-
-
-- - -
-
-#### `tf.train.QueueRunner.exceptions_raised` {#QueueRunner.exceptions_raised}
-
-Exceptions raised but not handled by the `QueueRunner` threads.
-
-Exceptions raised in queue runner threads are handled in one of two ways
-depending on whether or not a `Coordinator` was passed to
-`create_threads()`:
-
-* With a `Coordinator`, exceptions are reported to the coordinator and
-  forgotten by the `QueueRunner`.
-* Without a `Coordinator`, exceptions are captured by the `QueueRunner` and
-  made available in this `exceptions_raised` property.
-
-##### Returns:
-
-  A list of Python `Exception` objects.  The list is empty if no exception
-  was captured.  (No exceptions are captured when using a Coordinator.)
-
-
-- - -
-
-#### `tf.train.QueueRunner.from_proto(queue_runner_def)` {#QueueRunner.from_proto}
-
-Returns a `QueueRunner` object created from `queue_runner_def`.
-
-
-- - -
-
-#### `tf.train.QueueRunner.name` {#QueueRunner.name}
-
-The string name of the underlying Queue.
-
-
-- - -
-
-#### `tf.train.QueueRunner.queue` {#QueueRunner.queue}
-
-
-
-
-- - -
-
-#### `tf.train.QueueRunner.to_proto()` {#QueueRunner.to_proto}
-
-Converts this `QueueRunner` to a `QueueRunnerDef` protocol buffer.
-
-##### Returns:
-
-  A `QueueRunnerDef` protocol buffer.
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.batch.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.batch.md
new file mode 100644
index 0000000000..96142e0719
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.batch.md
@@ -0,0 +1,68 @@
+### `tf.train.batch(tensors, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, shared_name=None, name=None)` {#batch}
+
+Creates batches of tensors in `tensors`.
+
+The argument `tensors` can be a list or a dictionary of tensors.
+The value returned by the function will be of the same type
+as `tensors`.
+
+This function is implemented using a queue. A `QueueRunner` for the
+queue is added to the current `Graph`'s `QUEUE_RUNNER` collection.
+
+If `enqueue_many` is `False`, `tensors` is assumed to represent a single
+example.  An input tensor with shape `[x, y, z]` will be output as a tensor
+with shape `[batch_size, x, y, z]`.
+
+If `enqueue_many` is `True`, `tensors` is assumed to represent a batch of
+examples, where the first dimension is indexed by example, and all members of
+`tensor_list` should have the same size in the first dimension.  If an input
+tensor has shape `[*, x, y, z]`, the output will have shape `[batch_size, x,
+y, z]`.  The `capacity` argument controls the how long the prefetching is
+allowed to grow the queues.
+
+The returned operation is a dequeue operation and will throw
+`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
+operation is feeding another input queue, its queue runner will catch
+this exception, however, if this operation is used in your main thread
+you are responsible for catching this yourself.
+
+*N.B.:* If `dynamic_pad` is `False`, you must ensure that either
+(i) the `shapes` argument is passed, or (ii) all of the tensors in
+`tensors` must have fully-defined shapes. `ValueError` will be
+raised if neither of these conditions holds.
+
+If `dynamic_pad` is `True`, it is sufficient that the *rank* of the
+tensors is known, but individual dimensions may have shape `None`.
+In this case, for each enqueue the dimensions with value `None`
+may have a variable length; upon dequeue, the output tensors will be padded
+on the right to the maximum shape of the tensors in the current minibatch.
+For numbers, this padding takes value 0.  For strings, this padding is
+the empty string.  See `PaddingFIFOQueue` for more info.
+
+##### Args:
+
+
+*  <b>`tensors`</b>: The list or dictionary of tensors to enqueue.
+*  <b>`batch_size`</b>: The new batch size pulled from the queue.
+*  <b>`num_threads`</b>: The number of threads enqueuing `tensor_list`.
+*  <b>`capacity`</b>: An integer. The maximum number of elements in the queue.
+*  <b>`enqueue_many`</b>: Whether each tensor in `tensor_list` is a single example.
+*  <b>`shapes`</b>: (Optional) The shapes for each example.  Defaults to the
+    inferred shapes for `tensor_list`.
+*  <b>`dynamic_pad`</b>: Boolean.  Allow variable dimensions in input shapes.
+    The given dimensions are padded upon dequeue so that tensors within a
+    batch have the same shapes.
+*  <b>`shared_name`</b>: (optional). If set, this queue will be shared under the given
+    name across multiple sessions.
+*  <b>`name`</b>: (Optional) A name for the operations.
+
+##### Returns:
+
+  A list or dictionary of tensors with the same types as `tensors`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be
+    inferred from the elements of `tensors`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.export_meta_graph.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.export_meta_graph.md
deleted file mode 100644
index c09e6783c6..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.export_meta_graph.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.train.export_meta_graph(filename=None, meta_info_def=None, graph_def=None, saver_def=None, collection_list=None, as_text=False)` {#export_meta_graph}
-
-Returns `MetaGraphDef` proto. Optionally writes it to filename.
-
-This function exports the graph, saver, and collection objects into
-`MetaGraphDef` protocol buffer with the intension of it being imported
-at a later time or location to restart training, run inference, or be
-a subgraph.
-
-##### Args:
-
-
-*  <b>`filename`</b>: Optional filename including the path for writing the
-    generated `MetaGraphDef` protocol buffer.
-*  <b>`meta_info_def`</b>: `MetaInfoDef` protocol buffer.
-*  <b>`graph_def`</b>: `GraphDef` protocol buffer.
-*  <b>`saver_def`</b>: `SaverDef` protocol buffer.
-*  <b>`collection_list`</b>: List of string keys to collect.
-*  <b>`as_text`</b>: If `True`, writes the `MetaGraphDef` as an ASCII proto.
-
-##### Returns:
-
-  A `MetaGraphDef` proto.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.latest_checkpoint.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.latest_checkpoint.md
new file mode 100644
index 0000000000..b1fc87cdd7
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.latest_checkpoint.md
@@ -0,0 +1,16 @@
+### `tf.train.latest_checkpoint(checkpoint_dir, latest_filename=None)` {#latest_checkpoint}
+
+Finds the filename of latest saved checkpoint file.
+
+##### Args:
+
+
+*  <b>`checkpoint_dir`</b>: Directory where the variables were saved.
+*  <b>`latest_filename`</b>: Optional name for the protocol buffer file that
+    contains the list of most recent checkpoint filenames.
+    See the corresponding argument to `Saver.save()`.
+
+##### Returns:
+
+  The full path to the latest checkpoint or `None` if no checkpoint was found.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.update_checkpoint_state.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.update_checkpoint_state.md
new file mode 100644
index 0000000000..68747fc0c7
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.train.update_checkpoint_state.md
@@ -0,0 +1,24 @@
+### `tf.train.update_checkpoint_state(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None, latest_filename=None)` {#update_checkpoint_state}
+
+Updates the content of the 'checkpoint' file.
+
+This updates the checkpoint file containing a CheckpointState
+proto.
+
+##### Args:
+
+
+*  <b>`save_dir`</b>: Directory where the model was saved.
+*  <b>`model_checkpoint_path`</b>: The checkpoint file.
+*  <b>`all_model_checkpoint_paths`</b>: List of strings.  Paths to all not-yet-deleted
+    checkpoints, sorted from oldest to newest.  If this is a non-empty list,
+    the last element must be equal to model_checkpoint_path.  These paths
+    are also saved in the CheckpointState proto.
+*  <b>`latest_filename`</b>: Optional name of the checkpoint file.  Default to
+    'checkpoint'.
+
+##### Raises:
+
+
+*  <b>`RuntimeError`</b>: If the save paths conflict.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.uniform_unit_scaling_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.uniform_unit_scaling_initializer.md
deleted file mode 100644
index 6033fbf53a..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.uniform_unit_scaling_initializer.md
+++ /dev/null
@@ -1,47 +0,0 @@
-### `tf.uniform_unit_scaling_initializer(factor=1.0, seed=None, dtype=tf.float32, full_shape=None)` {#uniform_unit_scaling_initializer}
-
-Returns an initializer that generates tensors without scaling variance.
-
-When initializing a deep network, it is in principle advantageous to keep
-the scale of the input variance constant, so it does not explode or diminish
-by reaching the final layer. If the input is `x` and the operation `x * W`,
-and we want to initialize `W` uniformly at random, we need to pick `W` from
-
-    [-sqrt(3) / sqrt(dim), sqrt(3) / sqrt(dim)]
-
-to keep the scale intact, where `dim = W.shape[0]` (the size of the input).
-A similar calculation for convolutional networks gives an analogous result
-with `dim` equal to the product of the first 3 dimensions.  When
-nonlinearities are present, we need to multiply this by a constant `factor`.
-See [Sussillo et al., 2014](https://arxiv.org/abs/1412.6558)
-([pdf](http://arxiv.org/pdf/1412.6558.pdf)) for deeper motivation, experiments
-and the calculation of constants. In section 2.3 there, the constants were
-numerically computed: for a linear layer it's 1.0, relu: ~1.43, tanh: ~1.15.
-
-If the shape tuple `full_shape` is provided, the scale will be calculated from
-this predefined shape.  This is useful when a `Variable` is being partitioned
-across several shards, and each shard has a smaller shape than the whole.
-Since the shards are usually concatenated when used, the scale should be
-based on the shape of the whole.
-
-##### Args:
-
-
-*  <b>`factor`</b>: Float.  A multiplicative factor by which the values will be scaled.
-*  <b>`seed`</b>: A Python integer. Used to create random seeds. See
-    [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
-    for behavior.
-*  <b>`dtype`</b>: The data type. Only floating point types are supported.
-*  <b>`full_shape`</b>: Tuple or list of integers.  The shape used for calculating
-    scale normalization (instead of the shape passed at creation time).
-    Useful when creating sharded variables via partitioning.
-
-##### Returns:
-
-  An initializer that generates tensors with unit variance.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `dtype` is not a floating point type.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.where.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.where.md
deleted file mode 100644
index eae2259721..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.where.md
+++ /dev/null
@@ -1,46 +0,0 @@
-### `tf.where(input, name=None)` {#where}
-
-Returns locations of true values in a boolean tensor.
-
-This operation returns the coordinates of true elements in `input`. The
-coordinates are returned in a 2-D tensor where the first dimension (rows)
-represents the number of true elements, and the second dimension (columns)
-represents the coordinates of the true elements. Keep in mind, the shape of
-the output tensor can vary depending on how many true values there are in
-`input`. Indices are output in row-major order.
-
-For example:
-
-```prettyprint
-# 'input' tensor is [[True, False]
-#                    [True, False]]
-# 'input' has two true values, so output has two coordinates.
-# 'input' has rank of 2, so coordinates have two indices.
-where(input) ==> [[0, 0],
-                  [1, 0]]
-
-# `input` tensor is [[[True, False]
-#                     [True, False]]
-#                    [[False, True]
-#                     [False, True]]
-#                    [[False, False]
-#                     [False, True]]]
-# 'input' has 5 true values, so output has 5 coordinates.
-# 'input' has rank of 3, so coordinates have three indices.
-where(input) ==> [[0, 0, 0],
-                  [0, 1, 0],
-                  [1, 0, 1],
-                  [1, 1, 1],
-                  [2, 1, 1]]
-```
-
-##### Args:
-
-
-*  <b>`input`</b>: A `Tensor` of type `bool`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` of type `int64`.
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.zeros.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.zeros.md
deleted file mode 100644
index 57598a372d..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.zeros.md
+++ /dev/null
@@ -1,24 +0,0 @@
-### `tf.zeros(shape, dtype=tf.float32, name=None)` {#zeros}
-
-Creates a tensor with all elements set to zero.
-
-This operation returns a tensor of type `dtype` with shape `shape` and
-all elements set to zero.
-
-For example:
-
-```python
-tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
-```
-
-##### Args:
-
-
-*  <b>`shape`</b>: Either a list of integers, or a 1-D `Tensor` of type `int32`.
-*  <b>`dtype`</b>: The type of an element in the resulting `Tensor`.
-*  <b>`name`</b>: A name for the operation (optional).
-
-##### Returns:
-
-  A `Tensor` with all elements set to zero.
-
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