# Layers (contrib)

[TOC]

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.convolution2d(*args, **kwargs)

Adds a 2D convolution followed by an optional batch_norm layer.

convolution2d creates a variable called weights, representing the convolutional kernel, that is convolved with the inputs to produce a Tensor of activations. If a normalizer_fn is provided (such as batch_norm), it is then applied. Otherwise, if normalizer_fn is None and a biases_initializer is provided then a biases variable would be created and added the activations. Finally, if activation_fn is not None, it is applied to the activations as well.

##### Args:
• inputs: a 4-D tensor [batch_size, height, width, channels].
• num_outputs: integer, the number of output filters.
• kernel_size: a list of length 2 [kernel_height, kernel_width] of of the filters. Can be an int if both values are the same.
• stride: a list of length 2 [stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value.
• padding: one of VALID or SAME.
• activation_fn: activation function.
• normalizer_fn: normalization function to use instead of biases. If normalize_fn is provided then biases_initializer and biases_regularizer are ignored and biases are not created nor added.
• normalizer_params: normalization function parameters.
• weights_initializer: An initializer for the weights.
• weights_regularizer: Optional regularizer for the weights.
• biases_initializer: An initializer for the biases. If None skip biases.
• biases_regularizer: Optional regularizer for the biases.
• reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
• variables_collections: optional list of collections for all the variables or a dictionay containing a different list of collection per variable.
• outputs_collections: collection to add the outputs.
• trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
• scope: Optional scope for variable_op_scope.
##### Returns:

a tensor representing the output of the operation.

### tf.contrib.layers.fully_connected(*args, **kwargs)

fully_connected creates a variable called weights, representing a fully connected weight matrix, which is multiplied by the inputs to produce a Tensor of hidden units. If a normalizer_fn is provided (such as batch_norm), it is then applied. Otherwise, if normalizer_fn is None and a biases_initializer is provided then a biases variable would be created and added the hidden units. Finally, if activation_fn is not None, it is applied to the hidden units as well.

Note: that if inputs have a rank greater than 2, then inputs is flattened prior to the initial matrix multiply by weights.

##### Args:
• inputs: A tensor of with at least rank 2 and value for the last dimension, i.e. [batch_size, depth], [None, None, None, channels].
• num_outputs: Integer, the number of output units in the layer.
• activation_fn: activation function.
• normalizer_fn: normalization function to use instead of biases. If normalize_fn is provided then biases_initializer and biases_regularizer are ignored and biases are not created nor added.
• normalizer_params: normalization function parameters.
• weights_initializer: An initializer for the weights.
• weights_regularizer: Optional regularizer for the weights.
• biases_initializer: An initializer for the biases. If None skip biases.
• biases_regularizer: Optional regularizer for the biases.
• reuse: whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
• variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collections per variable.
• outputs_collections: collection to add the outputs.
• trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
• scope: Optional scope for variable_op_scope.
##### Returns:

the tensor variable representing the result of the series of operations.

##### Raises:
• ValueError: if x has rank less than 2 or if its last dimension is not set.

Aliases for fully_connected which set a default activation function are available: relu, relu6 and linear.

## Regularizers

Regularization can help prevent overfitting. These have the signature fn(weights). The loss is typically added to tf.GraphKeys.REGULARIZATION_LOSS

### tf.contrib.layers.apply_regularization(regularizer, weights_list=None)

Returns the summed penalty by applying regularizer to the weights_list.

Adding a regularization penalty over the layer weights and embedding weights can help prevent overfitting the training data. Regularization over layer biases is less common/useful, but assuming proper data preprocessing/mean subtraction, it usually shouldn't hurt much either.

##### Args:
• regularizer: A function that takes a single Tensor argument and returns a scalar Tensor output.
• weights_list: List of weights Tensors or Variables to apply regularizer over. Defaults to the GraphKeys.WEIGHTS collection if None.
##### Returns:

A scalar representing the overall regularization penalty.

##### Raises:
• ValueError: If regularizer does not return a scalar output.

### tf.contrib.layers.l1_regularizer(scale)

Returns a function that can be used to apply L1 regularization to weights.

L1 regularization encourages sparsity.

##### Args:
• scale: A scalar multiplier Tensor. 0.0 disables the regularizer.
##### Returns:

A function with signature l1(weights, name=None) that apply L1 regularization.

##### Raises:
• ValueError: If scale is outside of the range [0.0, 1.0] or if scale is not a float.

### tf.contrib.layers.l2_regularizer(scale)

Returns a function that can be used to apply L2 regularization to weights.

Small values of L2 can help prevent overfitting the training data.

##### Args:
• scale: A scalar multiplier Tensor. 0.0 disables the regularizer.
##### Returns:

A function with signature l2(weights, name=None) that applies L2 regularization.

##### Raises:
• ValueError: If scale is outside of the range [0.0, 1.0] or if scale is not a float.

### tf.contrib.layers.sum_regularizer(regularizer_list)

Returns a function that applies the sum of multiple regularizers.

##### Args:
• regularizer_list: A list of regularizers to apply.
##### Returns:

A function with signature sum_reg(weights, name=None) that applies the sum of all the input 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)

Returns an initializer performing "Xavier" initialization for weights.

This function implements the weight initialization from:

Xavier Glorot and Yoshua Bengio (2010): Understanding the difficulty of training deep feedforward neural networks. International conference on artificial intelligence and statistics.

This initializer is designed to keep the scale of the gradients roughly the same in all layers. In uniform distribution this ends up being the range: x = sqrt(6. / (in + out)); [-x, x] and for normal distribution a standard deviation of sqrt(3. / (in + out)) is used.

##### Args:
• uniform: Whether to use uniform or normal distributed random initialization.
• seed: A Python integer. Used to create random seeds. See set_random_seed for behavior.
• dtype: The data type. Only floating point types are supported.
##### Returns:

An initializer for a weight matrix.

### tf.contrib.layers.xavier_initializer_conv2d(uniform=True, seed=None, dtype=tf.float32)

Returns an initializer performing "Xavier" initialization for weights.

This function implements the weight initialization from:

Xavier Glorot and Yoshua Bengio (2010): Understanding the difficulty of training deep feedforward neural networks. International conference on artificial intelligence and statistics.

This initializer is designed to keep the scale of the gradients roughly the same in all layers. In uniform distribution this ends up being the range: x = sqrt(6. / (in + out)); [-x, x] and for normal distribution a standard deviation of sqrt(3. / (in + out)) is used.

##### Args:
• uniform: Whether to use uniform or normal distributed random initialization.
• seed: A Python integer. Used to create random seeds. See set_random_seed for behavior.
• dtype: The data type. Only floating point types are supported.
##### Returns:

An initializer for a weight matrix.

### tf.contrib.layers.variance_scaling_initializer(factor=2.0, mode='FAN_IN', uniform=False, seed=None, dtype=tf.float32)

Returns an initializer that generates tensors without scaling variance.

When initializing a deep network, it is in principle advantageous to keep the scale of the input variance constant, so it does not explode or diminish by reaching the final layer. This initializer use the following formula: if mode='FAN_IN': # Count only number of input connections. n = fan_in elif mode='FAN_OUT': # Count only number of output connections. n = fan_out elif mode='FAN_AVG': # Average number of inputs and output connections. n = (fan_in + fan_out)/2.0

truncated_normal(shape, 0.0, stddev=sqrt(factor / n))


To get http://arxiv.org/pdf/1502.01852v1.pdf use (Default):

##### Args:
• factor: Float. A multiplicative factor.
• mode: String. 'FAN_IN', 'FAN_OUT', 'FAN_AVG'.
• uniform: Whether to use uniform or normal distributed random initialization.
• seed: A Python integer. Used to create random seeds. See set_random_seed for behavior.
• dtype: The data type. Only floating point types are supported.
##### Returns:

An initializer that generates tensors with unit variance.

##### Raises:
• ValueError: if dtype is not a floating point type.
• TypeError: if mode is not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG'].

## 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, moving_average_decay=0.9, learning_rate_decay_fn=None, update_ops=None, variables=None, name=None)

Given loss and parameters for optimizer, returns a training op.

##### Args:
• loss: Tensor, 0 dimensional.
• global_step: Tensor, step counter for each update.
• learning_rate: float or Tensor, magnitude of update per each training step.
• optimizer: string, class or optimizer instance, used as trainer.
      string should be name of optimizer, like 'SGD',
class should be sub-class of tf.Optimizer that implements
compute_gradients and apply_gradients functions.
optimizer instance should be instantion of tf.Optimizer sub-class
and have compute_gradients and apply_gradients functions.

• gradient_noise_scale: float or None, adds 0-mean normal noise scaled by this
                 value.

• gradient_multipliers: dict of variables or variable names to floats.
                 If present, gradients for specified
variables will be multiplied by given constant.

• clip_gradients: float or None, clips gradients by this value.
• moving_average_decay: float or None, takes into account previous loss
                 to make learning smoother due to outliers.

• learning_rate_decay_fn: function, takes learning_rate and global_step
                   Tensors, returns Tensor.
Can be used to implement any learning rate decay
functions.
For example: tf.train.exponential_decay.

• update_ops: list of update Operations to execute at each step. If None,
       uses elements of UPDATE_OPS collection.

• variables: list of variables to optimize or
      None to use all trainable variables.

• name: The name for this operation is used to scope operations and summaries.

Training op.

##### Raises:
• ValueError: if optimizer is wrong type.

## Summaries

Helper functions to summarize specific variables or ops.

### tf.contrib.layers.summarize_activation(op)

Summarize an activation.

This applies the given activation and adds useful summaries specific to the activation.

##### Args:
• op: The tensor to summarize (assumed to be a layer activation).
##### Returns:

The summary op created to summarize op.

### tf.contrib.layers.summarize_tensor(tensor, tag=None)

Summarize a tensor using a suitable summary type.

This function adds a summary op for tensor. The type of summary depends on the shape of tensor. For scalars, a scalar_summary is created, for all other tensors, histogram_summary is used.

##### Args:
• tensor: The tensor to summarize
• tag: The tag to use, if None then use tensor's op's name.
##### Returns:

The summary op created or None for string tensors.

### tf.contrib.layers.summarize_tensors(tensors, summarizer=summarize_tensor)

Summarize a set of tensors.

### tf.contrib.layers.summarize_collection(collection, name_filter=None, summarizer=summarize_tensor)

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, using summarize_activation to summarize.