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# 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`
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