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Ops for building neural network layers, regularizers, summaries, etc.
Modules
feature_column module: This API defines FeatureColumn abstraction.
summaries module: Utility functions for summary creation.
Classes
class GDN: Generalized divisive normalization layer.
class 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.
conv1d(...): Adds an N-D convolution followed by an optional batch_norm layer.
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.
convolution(...): Adds an N-D convolution followed by an optional batch_norm layer.
convolution1d(...): Adds an N-D convolution followed by an optional batch_norm 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.
elu(...): partial(func, *args, **keywords) - new function with partial application
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.
group_norm(...): Functional interface for the group normalization layer.
images_to_sequence(...): Convert a batch of images into a batch of sequences.
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.
legacy_linear(...): partial(func, *args, **keywords) - new function with partial application
legacy_relu(...): partial(func, *args, **keywords) - new function with partial application
linear(...): partial(func, *args, **keywords) - new function with partial application
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)
relu(...): partial(func, *args, **keywords) - new function with partial application
relu6(...): partial(func, *args, **keywords) - new function with partial application
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.
scale_gradient(...): _OverloadedFunction encapsulates an overloaded function.
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)
sequence_to_images(...): Convert a batch of sequences into a batch of images.
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.