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Diffstat (limited to 'tensorflow/docs_src/api_guides/python/contrib.layers.md')
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diff --git a/tensorflow/docs_src/api_guides/python/contrib.layers.md b/tensorflow/docs_src/api_guides/python/contrib.layers.md deleted file mode 100644 index 4c176a129c..0000000000 --- a/tensorflow/docs_src/api_guides/python/contrib.layers.md +++ /dev/null @@ -1,109 +0,0 @@ -# Layers (contrib) -[TOC] - -Ops for building neural network layers, regularizers, summaries, etc. - -## Higher level ops for building neural network layers - -This package provides several ops that take care of creating variables that are -used internally in a consistent way and provide the building blocks for many -common machine learning algorithms. - -* `tf.contrib.layers.avg_pool2d` -* `tf.contrib.layers.batch_norm` -* `tf.contrib.layers.convolution2d` -* `tf.contrib.layers.conv2d_in_plane` -* `tf.contrib.layers.convolution2d_in_plane` -* `tf.nn.conv2d_transpose` -* `tf.contrib.layers.convolution2d_transpose` -* `tf.nn.dropout` -* `tf.contrib.layers.flatten` -* `tf.contrib.layers.fully_connected` -* `tf.contrib.layers.layer_norm` -* `tf.contrib.layers.max_pool2d` -* `tf.contrib.layers.one_hot_encoding` -* `tf.nn.relu` -* `tf.nn.relu6` -* `tf.contrib.layers.repeat` -* `tf.contrib.layers.safe_embedding_lookup_sparse` -* `tf.nn.separable_conv2d` -* `tf.contrib.layers.separable_convolution2d` -* `tf.nn.softmax` -* `tf.stack` -* `tf.contrib.layers.unit_norm` -* `tf.contrib.layers.embed_sequence` - -Aliases for fully_connected which set a default activation function are -available: `relu`, `relu6` and `linear`. - -`stack` operation is also available. It builds a stack of layers by applying -a layer repeatedly. - -## Regularizers - -Regularization can help prevent overfitting. These have the signature -`fn(weights)`. The loss is typically added to -`tf.GraphKeys.REGULARIZATION_LOSSES`. - -* `tf.contrib.layers.apply_regularization` -* `tf.contrib.layers.l1_regularizer` -* `tf.contrib.layers.l2_regularizer` -* `tf.contrib.layers.sum_regularizer` - -## Initializers - -Initializers are used to initialize variables with sensible values given their -size, data type, and purpose. - -* `tf.contrib.layers.xavier_initializer` -* `tf.contrib.layers.xavier_initializer_conv2d` -* `tf.contrib.layers.variance_scaling_initializer` - -## Optimization - -Optimize weights given a loss. - -* `tf.contrib.layers.optimize_loss` - -## Summaries - -Helper functions to summarize specific variables or ops. - -* `tf.contrib.layers.summarize_activation` -* `tf.contrib.layers.summarize_tensor` -* `tf.contrib.layers.summarize_tensors` -* `tf.contrib.layers.summarize_collection` - -The layers module defines convenience functions `summarize_variables`, -`summarize_weights` and `summarize_biases`, which set the `collection` argument -of `summarize_collection` to `VARIABLES`, `WEIGHTS` and `BIASES`, respectively. - -* `tf.contrib.layers.summarize_activations` - -## Feature columns - -Feature columns provide a mechanism to map data to a model. - -* `tf.contrib.layers.bucketized_column` -* `tf.contrib.layers.check_feature_columns` -* `tf.contrib.layers.create_feature_spec_for_parsing` -* `tf.contrib.layers.crossed_column` -* `tf.contrib.layers.embedding_column` -* `tf.contrib.layers.scattered_embedding_column` -* `tf.contrib.layers.input_from_feature_columns` -* `tf.contrib.layers.joint_weighted_sum_from_feature_columns` -* `tf.contrib.layers.make_place_holder_tensors_for_base_features` -* `tf.contrib.layers.multi_class_target` -* `tf.contrib.layers.one_hot_column` -* `tf.contrib.layers.parse_feature_columns_from_examples` -* `tf.contrib.layers.parse_feature_columns_from_sequence_examples` -* `tf.contrib.layers.real_valued_column` -* `tf.contrib.layers.shared_embedding_columns` -* `tf.contrib.layers.sparse_column_with_hash_bucket` -* `tf.contrib.layers.sparse_column_with_integerized_feature` -* `tf.contrib.layers.sparse_column_with_keys` -* `tf.contrib.layers.sparse_column_with_vocabulary_file` -* `tf.contrib.layers.weighted_sparse_column` -* `tf.contrib.layers.weighted_sum_from_feature_columns` -* `tf.contrib.layers.infer_real_valued_columns` -* `tf.contrib.layers.sequence_input_from_feature_columns` |