diff options
Diffstat (limited to 'tensorflow/g3doc/api_docs/python/contrib.layers.md')
| -rw-r--r-- | tensorflow/g3doc/api_docs/python/contrib.layers.md | 39 |
1 files changed, 1 insertions, 38 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.layers.md b/tensorflow/g3doc/api_docs/python/contrib.layers.md index 12370048d8..910cab1cc7 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.layers.md +++ b/tensorflow/g3doc/api_docs/python/contrib.layers.md @@ -5,11 +5,7 @@ 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. +See the @{$python/contrib.layers} guide. - - - @@ -1015,18 +1011,6 @@ Typical use case would be reusing embeddings between an encoder and decoder. -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(regularizer, weights_list=None)` {#apply_regularization} @@ -1125,11 +1109,6 @@ Returns a function that applies the sum of multiple regularizers. -## Initializers - -Initializers are used to initialize variables with sensible values given their -size, data type, and purpose. - - - - ### `tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32)` {#xavier_initializer} @@ -1252,10 +1231,6 @@ by reaching the final layer. This initializer use the following formula: -## Optimization - -Optimize weights given a loss. - - - - ### `tf.contrib.layers.optimize_loss(loss, global_step, learning_rate, optimizer, gradient_noise_scale=None, gradient_multipliers=None, clip_gradients=None, learning_rate_decay_fn=None, update_ops=None, variables=None, name=None, summaries=None, colocate_gradients_with_ops=False)` {#optimize_loss} @@ -1343,10 +1318,6 @@ Various ways of passing optimizers, include: -## Summaries - -Helper functions to summarize specific variables or ops. - - - - ### `tf.contrib.layers.summarize_activation(op)` {#summarize_activation} @@ -1402,10 +1373,6 @@ Summarize a graph collection of tensors, possibly filtered by name. -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(name_filter=None, summarizer=summarize_activation)` {#summarize_activations} @@ -1414,10 +1381,6 @@ Summarize activations, using `summarize_activation` to summarize. -## Feature columns - -Feature columns provide a mechanism to map data to a model. - - - - ### `tf.contrib.layers.bucketized_column(source_column, boundaries)` {#bucketized_column} |