Layers

Layers (contrib)

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.linear
• 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.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