tf.contrib.layers

 

upper level

 

Modules

 

feature_column:

This API defines FeatureColumn abstraction.

summaries:

Utility functions for summary creation.

 

Classes

 

GDN:

Generalized divisive normalization layer.

RevBlock:

Block of reversible layers. See rev_block.

 

Functions

 

apply_regularization(…):

Returns the summed penalty by applying regularizer to the weights_list.

avg_pool2d(…):

Adds a 2D average pooling op.

avg_pool3d(…):

Adds a 3D average pooling op.

batch_norm(…):

Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167.

bias_add(…):

Adds a bias to the inputs.

bow_encoder(…):

Maps a sequence of symbols to a vector per example by averaging embeddings.

bucketized_column(…):

Creates a _BucketizedColumn for discretizing dense input.

check_feature_columns(…):

Checks the validity of the set of FeatureColumns.

conv2d(…):

Adds an N-D convolution followed by an optional batch_norm layer.

conv2d_in_plane(…):

Performs the same in-plane convolution to each channel independently.

conv2d_transpose(…):

Adds a convolution2d_transpose with an optional batch normalization layer.

conv3d(…):

Adds an N-D convolution followed by an optional batch_norm layer.

conv3d_transpose(…):

Adds a convolution3d_transpose with an optional batch normalization layer.

convolution2d(…):

Adds an N-D convolution followed by an optional batch_norm layer.

convolution2d_in_plane(…):

Performs the same in-plane convolution to each channel independently.

convolution2d_transpose(…):

Adds a convolution2d_transpose with an optional batch normalization layer.

convolution3d(…):

Adds an N-D convolution followed by an optional batch_norm layer.

convolution3d_transpose(…):

Adds a convolution3d_transpose with an optional batch normalization layer.

create_feature_spec_for_parsing(…):

Helper that prepares features config from input feature_columns.

crossed_column(…):

Creates a _CrossedColumn for performing feature crosses.

dense_to_sparse(…):

Converts a dense tensor into a sparse tensor.

dropout(…):

Returns a dropout op applied to the input.

embed_sequence(…):

Maps a sequence of symbols to a sequence of embeddings.

embedding_column(…):

Creates an _EmbeddingColumn for feeding sparse data into a DNN.

embedding_lookup_unique(…):

Version of embedding_lookup that avoids duplicate lookups.

flatten(…):

Flattens the input while maintaining the batch_size.

fully_connected(…):

Adds a fully connected layer.

gdn(…):

Functional interface for GDN layer.

infer_real_valued_columns(…):

input_from_feature_columns(…):

A tf.contrib.layers style input layer builder based on FeatureColumns.

instance_norm(…):

Functional interface for the instance normalization layer.

joint_weighted_sum_from_feature_columns(…):

A restricted linear prediction builder based on FeatureColumns.

l1_l2_regularizer(…):

Returns a function that can be used to apply L1 L2 regularizations.

l1_regularizer(…):

Returns a function that can be used to apply L1 regularization to weights.

l2_regularizer(…):

Returns a function that can be used to apply L2 regularization to weights.

layer_norm(…):

Adds a Layer Normalization layer.

legacy_fully_connected(…):

Adds the parameters for a fully connected layer and returns the output.

make_place_holder_tensors_for_base_features(…):

Returns placeholder tensors for inference.

max_pool2d(…):

Adds a 2D Max Pooling op.

max_pool3d(…):

Adds a 3D Max Pooling op.

maxout(…):

Adds a maxout op from https://arxiv.org/abs/1302.4389

multi_class_target(…):

Creates a _TargetColumn for multi class single label classification. (deprecated)

one_hot_column(…):

Creates an _OneHotColumn for a one-hot or multi-hot repr in a DNN.

one_hot_encoding(…):

Transform numeric labels into onehot_labels using tf.one_hot.

optimize_loss(…):

Given loss and parameters for optimizer, returns a training op.

parse_feature_columns_from_examples(…):

Parses tf.Examples to extract tensors for given feature_columns.

parse_feature_columns_from_sequence_examples(…):

Parses tf.SequenceExamples to extract tensors for given FeatureColumns.

real_valued_column(…):

Creates a _RealValuedColumn for dense numeric data.

recompute_grad(…):

Decorator that recomputes the function on the backwards pass.

regression_target(…):

Creates a _TargetColumn for linear regression. (deprecated)

repeat(…):

Applies the same layer with the same arguments repeatedly.

rev_block(…):

A block of reversible residual layers.

safe_embedding_lookup_sparse(…):

Lookup embedding results, accounting for invalid IDs and empty features.

scattered_embedding_column(…):

Creates an embedding column of a sparse feature using parameter hashing.

separable_conv2d(…):

Adds a depth-separable 2D convolution with optional batch_norm layer.

separable_convolution2d(…):

Adds a depth-separable 2D convolution with optional batch_norm layer.

sequence_input_from_feature_columns(…):

Builds inputs for sequence models from FeatureColumns. (experimental)

shared_embedding_columns(…):

Creates a list of _EmbeddingColumn sharing the same embedding.

softmax(…):

Performs softmax on Nth dimension of N-dimensional logit tensor.

sparse_column_with_hash_bucket(…):

Creates a _SparseColumn with hashed bucket configuration.

sparse_column_with_integerized_feature(…):

Creates an integerized _SparseColumn.

sparse_column_with_keys(…):

Creates a _SparseColumn with keys.

sparse_column_with_vocabulary_file(…):

Creates a _SparseColumn with vocabulary file configuration.

spatial_softmax(…):

Computes the spatial softmax of a convolutional feature map.

stack(…):

Builds a stack of layers by applying layer repeatedly using stack_args.

sum_regularizer(…):

Returns a function that applies the sum of multiple regularizers.

summarize_activation(…):

Summarize an activation.

summarize_activations(…):

Summarize activations, using summarize_activation to summarize.

summarize_collection(…):

Summarize a graph collection of tensors, possibly filtered by name.

summarize_tensor(…):

Summarize a tensor using a suitable summary type.

summarize_tensors(…):

Summarize a set of tensors.

transform_features(…):

Returns transformed features based on features columns passed in.

unit_norm(…):

Normalizes the given input across the specified dimension to unit length.

variance_scaling_initializer(…):

Returns an initializer that generates tensors without scaling variance.

weighted_sparse_column(…):

Creates a _SparseColumn by combining sparse_id_column with a weight column.

weighted_sum_from_feature_columns(…):

A tf.contrib.layers style linear prediction builder based on FeatureColumn.

xavier_initializer(…):

Returns an initializer performing “Xavier” initialization for weights.

xavier_initializer_conv2d(…):

Returns an initializer performing “Xavier” initialization for weights.