# 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.

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.

## Initializers

Initializers are used to initialize variables with sensible values given their size, data type, and purpose.

## Optimization

Optimize weights given a loss.

## Summaries

Helper functions to summarize specific variables or ops.

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

Feature columns provide a mechanism to map data to a model.