Ops for building neural network layers, regularizers, summaries, etc.
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.
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 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
Optimize weights given a loss.
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.
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
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_guides/python/contrib.layers