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diff --git a/tensorflow/docs_src/api_guides/python/train.md b/tensorflow/docs_src/api_guides/python/train.md deleted file mode 100644 index 4b4c6a4fe3..0000000000 --- a/tensorflow/docs_src/api_guides/python/train.md +++ /dev/null @@ -1,139 +0,0 @@ -# Training -[TOC] - -`tf.train` provides a set of classes and functions that help train models. - -## Optimizers - -The Optimizer base class provides methods to compute gradients for a loss and -apply gradients to variables. A collection of subclasses implement classic -optimization algorithms such as GradientDescent and Adagrad. - -You never instantiate the Optimizer class itself, but instead instantiate one -of the subclasses. - -* `tf.train.Optimizer` -* `tf.train.GradientDescentOptimizer` -* `tf.train.AdadeltaOptimizer` -* `tf.train.AdagradOptimizer` -* `tf.train.AdagradDAOptimizer` -* `tf.train.MomentumOptimizer` -* `tf.train.AdamOptimizer` -* `tf.train.FtrlOptimizer` -* `tf.train.ProximalGradientDescentOptimizer` -* `tf.train.ProximalAdagradOptimizer` -* `tf.train.RMSPropOptimizer` - -See `tf.contrib.opt` for more optimizers. - -## Gradient Computation - -TensorFlow provides functions to compute the derivatives for a given -TensorFlow computation graph, adding operations to the graph. The -optimizer classes automatically compute derivatives on your graph, but -creators of new Optimizers or expert users can call the lower-level -functions below. - -* `tf.gradients` -* `tf.AggregationMethod` -* `tf.stop_gradient` -* `tf.hessians` - - -## Gradient Clipping - -TensorFlow provides several operations that you can use to add clipping -functions to your graph. You can use these functions to perform general data -clipping, but they're particularly useful for handling exploding or vanishing -gradients. - -* `tf.clip_by_value` -* `tf.clip_by_norm` -* `tf.clip_by_average_norm` -* `tf.clip_by_global_norm` -* `tf.global_norm` - -## Decaying the learning rate - -* `tf.train.exponential_decay` -* `tf.train.inverse_time_decay` -* `tf.train.natural_exp_decay` -* `tf.train.piecewise_constant` -* `tf.train.polynomial_decay` -* `tf.train.cosine_decay` -* `tf.train.linear_cosine_decay` -* `tf.train.noisy_linear_cosine_decay` - -## Moving Averages - -Some training algorithms, such as GradientDescent and Momentum often benefit -from maintaining a moving average of variables during optimization. Using the -moving averages for evaluations often improve results significantly. - -* `tf.train.ExponentialMovingAverage` - -## Coordinator and QueueRunner - -See [Threading and Queues](../../api_guides/python/threading_and_queues.md) -for how to use threads and queues. For documentation on the Queue API, -see [Queues](../../api_guides/python/io_ops.md#queues). - - -* `tf.train.Coordinator` -* `tf.train.QueueRunner` -* `tf.train.LooperThread` -* `tf.train.add_queue_runner` -* `tf.train.start_queue_runners` - -## Distributed execution - -See [Distributed TensorFlow](../../deploy/distributed.md) for -more information about how to configure a distributed TensorFlow program. - -* `tf.train.Server` -* `tf.train.Supervisor` -* `tf.train.SessionManager` -* `tf.train.ClusterSpec` -* `tf.train.replica_device_setter` -* `tf.train.MonitoredTrainingSession` -* `tf.train.MonitoredSession` -* `tf.train.SingularMonitoredSession` -* `tf.train.Scaffold` -* `tf.train.SessionCreator` -* `tf.train.ChiefSessionCreator` -* `tf.train.WorkerSessionCreator` - -## Reading Summaries from Event Files - -See [Summaries and TensorBoard](../../guide/summaries_and_tensorboard.md) for an -overview of summaries, event files, and visualization in TensorBoard. - -* `tf.train.summary_iterator` - -## Training Hooks - -Hooks are tools that run in the process of training/evaluation of the model. - -* `tf.train.SessionRunHook` -* `tf.train.SessionRunArgs` -* `tf.train.SessionRunContext` -* `tf.train.SessionRunValues` -* `tf.train.LoggingTensorHook` -* `tf.train.StopAtStepHook` -* `tf.train.CheckpointSaverHook` -* `tf.train.NewCheckpointReader` -* `tf.train.StepCounterHook` -* `tf.train.NanLossDuringTrainingError` -* `tf.train.NanTensorHook` -* `tf.train.SummarySaverHook` -* `tf.train.GlobalStepWaiterHook` -* `tf.train.FinalOpsHook` -* `tf.train.FeedFnHook` - -## Training Utilities - -* `tf.train.global_step` -* `tf.train.basic_train_loop` -* `tf.train.get_global_step` -* `tf.train.assert_global_step` -* `tf.train.write_graph` |