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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.avg_pool2d(*args, **kwargs) {#avg_pool2d}

Adds a 2D average pooling op.

It is assumed that the pooling is done per image but not in batch or channels.

Args:
  • inputs: A 4-D tensor of shape [batch_size, height, width, channels] if data_format is NHWC, and [batch_size, channels, height, width] if data_format is NCHW.
  • kernel_size: A list of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. 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: The padding method, either 'VALID' or 'SAME'.
  • data_format: A string. NHWC (default) and NCHW are supported.
  • outputs_collections: The collections to which the outputs are added.
  • scope: Optional scope for name_scope.
Returns:

A Tensor representing the results of the pooling operation.

Raises:
  • ValueError: if data_format is neither NHWC nor NCHW.

tf.contrib.layers.batch_norm(*args, **kwargs) {#batch_norm}

Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167.

"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift"

Sergey Ioffe, Christian Szegedy

Can be used as a normalizer function for conv2d and fully_connected.

Note: When is_training is True the moving_mean and moving_variance need to be updated, by default the update_ops are placed in tf.GraphKeys.UPDATE_OPS so they need to be added as a dependency to the train_op, example:

update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) if update_ops: updates = tf.group(*update_ops) total_loss = control_flow_ops.with_dependencies([updates], total_loss)

One can set updates_collections=None to force the updates in place, but that can have speed penalty, specially in distributed settings.

Args:
  • inputs: a tensor with 2 or more dimensions, where the first dimension has batch_size. The normalization is over all but the last dimension if data_format is NHWC and the second dimension if data_format is NCHW.
  • decay: decay for the moving average. Reasonable values for decay are close to 1.0, typically in the multiple-nines range: 0.999, 0.99, 0.9, etc. Lower decay value (recommend trying decay=0.9) if model experiences reasonably good training performance but poor validation and/or test performance. Try zero_debias_moving_mean=True for improved stability.
  • center: If True, add offset of beta to normalized tensor. If False, beta is ignored.
  • scale: If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling can be done by the next layer.
  • epsilon: small float added to variance to avoid dividing by zero.
  • activation_fn: activation function, default set to None to skip it and maintain a linear activation.
  • param_initializers: optional initializers for beta, gamma, moving mean and moving variance.
  • updates_collections: collections to collect the update ops for computation. The updates_ops need to be executed with the train_op. If None, a control dependency would be added to make sure the updates are computed in place.
  • is_training: whether or not the layer is in training mode. In training mode it would accumulate the statistics of the moments into moving_mean and moving_variance using an exponential moving average with the given decay. When it is not in training mode then it would use the values of the moving_mean and the moving_variance.
  • 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 collections for the variables.
  • outputs_collections: collections to add the outputs.
  • trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
  • batch_weights: An optional tensor of shape [batch_size], containing a frequency weight for each batch item. If present, then the batch normalization uses weighted mean and variance. (This can be used to correct for bias in training example selection.)
  • fused: Use nn.fused_batch_norm if True, nn.batch_normalization otherwise.
  • data_format: A string. NHWC (default) and NCHW are supported.
  • zero_debias_moving_mean: Use zero_debias for moving_mean. It creates a new pair of variables 'moving_mean/biased' and 'moving_mean/local_step'.
  • scope: Optional scope for variable_scope.
Returns:

A Tensor representing the output of the operation.

Raises:
  • ValueError: if batch_weights is not None and fused is True.
  • ValueError: if data_format is neither NHWC nor NCHW.
  • ValueError: if the rank of inputs is undefined.
  • ValueError: if rank or channels dimension of inputs is undefined.

tf.contrib.layers.convolution2d(*args, **kwargs) {#convolution2d}

Adds an N-D convolution followed by an optional batch_norm layer.

It is required that 1 <= N <= 3.

convolution creates a variable called weights, representing the convolutional kernel, that is convolved (actually cross-correlated) 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.

Performs a'trous convolution with input stride/dilation rate equal to rate if a value > 1 for any dimension of rate is specified. In this case stride values != 1 are not supported.

Args:
  • inputs: a Tensor of rank N+2 of shape [batch_size] + input_spatial_shape + [in_channels] if data_format does not start with "NC" (default), or [batch_size, in_channels] + input_spatial_shape if data_format starts with "NC".
  • num_outputs: integer, the number of output filters.
  • kernel_size: a sequence of N positive integers specifying the spatial dimensions of of the filters. Can be a single integer to specify the same value for all spatial dimensions.
  • stride: a sequence of N positive integers specifying the stride at which to compute output. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any rate value != 1.
  • padding: one of "VALID" or "SAME".
  • data_format: A string or None. Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"), or the second dimension (if data_format starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, currently the only valid value is "NDHWC".
  • rate: a sequence of N positive integers specifying the dilation rate to use for a'trous convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any rate value != 1 is incompatible with specifying any stride value != 1.
  • activation_fn: activation function, set to None to skip it and maintain a linear activation.
  • normalizer_fn: normalization function to use instead of biases. If normalizer_fn is provided then biases_initializer and biases_regularizer are ignored and biases are not created nor added. default set to None for no normalizer function
  • 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 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_scope.
Returns:

a tensor representing the output of the operation.

Raises:
  • ValueError: if data_format is invalid.
  • ValueError: both 'rate' and stride are not uniformly 1.

tf.contrib.layers.convolution2d_in_plane(*args, **kwargs) {#convolution2d_in_plane}

Performs the same in-plane convolution to each channel independently.

This is useful for performing various simple channel-independent convolution operations such as image gradients:

image = tf.constant(..., shape=(16, 240, 320, 3)) vert_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[2, 1]) horz_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[1, 2])

Args:
  • inputs: a 4-D tensor with dimensions [batch_size, height, width, channels].
  • kernel_size: a list of length 2 holding the [kernel_height, kernel_width] of of the pooling. 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: the padding type to use, either 'SAME' or 'VALID'.
  • activation_fn: activation function, set to None to skip it and maintain a linear activation.
  • normalizer_fn: normalization function to use instead of biases. If normalizer_fn is provided then biases_initializer and biases_regularizer are ignored and biases are not created nor added. default set to None for no normalizer function
  • 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 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_scope.
Returns:

A Tensor representing the output of the operation.

tf.contrib.layers.convolution2d_transpose(*args, **kwargs) {#convolution2d_transpose}

Adds a convolution2d_transpose with an optional batch normalization layer.

The function creates a variable called weights, representing the kernel, that is convolved with the input. If batch_norm_params is None, a second variable called 'biases' is added to the result of the operation.

Args:
  • inputs: A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
  • num_outputs: integer, the number of output filters.
  • kernel_size: a list of length 2 holding the [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'.
  • data_format: A string. NHWC (default) and NCHW are supported.
  • activation_fn: activation function, set to None to skip it and maintain a linear activation.
  • normalizer_fn: normalization function to use instead of biases. If normalizer_fn is provided then biases_initializer and biases_regularizer are ignored and biases are not created nor added. default set to None for no normalizer function
  • 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 collection per variable.
  • outputs_collections: collection to add the outputs.
  • trainable: whether or not the variables should be trainable or not.
  • scope: Optional scope for variable_scope.
Returns:

a tensor representing the output of the operation.

Raises:
  • ValueError: if 'kernel_size' is not a list of length 2.
  • ValueError: if data_format is neither NHWC nor NCHW.
  • ValueError: if C dimension of inputs is None.

tf.contrib.layers.flatten(*args, **kwargs) {#flatten}

Flattens the input while maintaining the batch_size.

Assumes that the first dimension represents the batch.

Args:
  • inputs: a tensor of size [batch_size, ...].
  • outputs_collections: collection to add the outputs.
  • scope: Optional scope for name_scope.
Returns:

a flattened tensor with shape [batch_size, k].

Raises:
  • ValueError: if inputs.dense_shape is wrong.

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

Adds a fully connected layer.

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 or long, the number of output units in the layer.
  • activation_fn: activation function, set to None to skip it and maintain a linear activation.
  • normalizer_fn: normalization function to use instead of biases. If normalizer_fn is provided then biases_initializer and biases_regularizer are ignored and biases are not created nor added. default set to None for no normalizer function
  • 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_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.

tf.contrib.layers.layer_norm(*args, **kwargs) {#layer_norm}

Adds a Layer Normalization layer from https://arxiv.org/abs/1607.06450.

"Layer Normalization"

Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton

Can be used as a normalizer function for conv2d and fully_connected.

Args:
  • inputs: a tensor with 2 or more dimensions. The normalization occurs over all but the first dimension.
  • center: If True, add offset of beta to normalized tensor. If False, beta is ignored.
  • scale: If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling can be done by the next layer.
  • activation_fn: activation function, default set to None to skip it and maintain a linear activation.
  • 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 collections for the variables.
  • outputs_collections: collections 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_scope.
Returns:

A Tensor representing the output of the operation.

Raises:
  • ValueError: if rank or last dimension of inputs is undefined.

tf.contrib.layers.max_pool2d(*args, **kwargs) {#max_pool2d}

Adds a 2D Max Pooling op.

It is assumed that the pooling is done per image but not in batch or channels.

Args:
  • inputs: A 4-D tensor of shape [batch_size, height, width, channels] if data_format is NHWC, and [batch_size, channels, height, width] if data_format is NCHW.
  • kernel_size: A list of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. 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: The padding method, either 'VALID' or 'SAME'.
  • data_format: A string. NHWC (default) and NCHW are supported.
  • outputs_collections: The collections to which the outputs are added.
  • scope: Optional scope for name_scope.
Returns:

A Tensor representing the results of the pooling operation.

Raises:
  • ValueError: if data_format is neither NHWC nor NCHW.
  • ValueError: If 'kernel_size' is not a 2-D list

tf.contrib.layers.one_hot_encoding(*args, **kwargs) {#one_hot_encoding}

Transform numeric labels into onehot_labels using tf.one_hot.

Args:
  • labels: [batch_size] target labels.
  • num_classes: total number of classes.
  • on_value: A scalar defining the on-value.
  • off_value: A scalar defining the off-value.
  • outputs_collections: collection to add the outputs.
  • scope: Optional scope for name_scope.
Returns:

one hot encoding of the labels.

tf.contrib.layers.repeat(inputs, repetitions, layer, *args, **kwargs) {#repeat}

Applies the same layer with the same arguments repeatedly.

  y = repeat(x, 3, conv2d, 64, [3, 3], scope='conv1')
  # It is equivalent to:

  x = conv2d(x, 64, [3, 3], scope='conv1/conv1_1')
  x = conv2d(x, 64, [3, 3], scope='conv1/conv1_2')
  y = conv2d(x, 64, [3, 3], scope='conv1/conv1_3')

If the scope argument is not given in kwargs, it is set to layer.__name__, or layer.func.__name__ (for functools.partial objects). If neither __name__ nor func.__name__ is available, the layers are called with scope='stack'.

Args:
  • inputs: A Tensor suitable for layer.
  • repetitions: Int, number of repetitions.
  • layer: A layer with arguments (inputs, *args, **kwargs)
  • *args: Extra args for the layer.
  • **kwargs: Extra kwargs for the layer.
Returns:

a tensor result of applying the layer, repetitions times.

Raises:
  • ValueError: if the op is unknown or wrong.

tf.contrib.layers.safe_embedding_lookup_sparse(embedding_weights, sparse_ids, sparse_weights=None, combiner=None, default_id=None, name=None, partition_strategy='div', max_norm=None) {#safe_embedding_lookup_sparse}

Lookup embedding results, accounting for invalid IDs and empty features.

The partitioned embedding in embedding_weights must all be the same shape except for the first dimension. The first dimension is allowed to vary as the vocabulary size is not necessarily a multiple of P. embedding_weights may be a PartitionedVariable as returned by using tf.get_variable() with a partitioner.

Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs with non-positive weight. For an entry with no features, the embedding vector for default_id is returned, or the 0-vector if default_id is not supplied.

The ids and weights may be multi-dimensional. Embeddings are always aggregated along the last dimension.

Args:
  • embedding_weights: A list of P float tensors or values representing partitioned embedding tensors. Alternatively, a PartitionedVariable, created by partitioning along dimension 0. The total unpartitioned shape should be [e_0, e_1, ..., e_m], where e_0 represents the vocab size and e_1, ..., e_m are the embedding dimensions.
  • sparse_ids: SparseTensor of shape [d_0, d_1, ..., d_n] containing the ids. d_0 is typically batch size.
  • sparse_weights: SparseTensor of same shape as sparse_ids, containing float weights corresponding to sparse_ids, or None if all weights are be assumed to be 1.0.
  • combiner: A string specifying how to combine embedding results for each entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean" the default.
  • default_id: The id to use for an entry with no features.
  • name: A name for this operation (optional).
  • partition_strategy: A string specifying the partitioning strategy. Currently "div" and "mod" are supported. Default is "div".
  • max_norm: If not None, all embeddings are l2-normalized to max_norm before combining.
Returns:

Dense tensor of shape [d_0, d_1, ..., d_{n-1}, e_1, ..., e_m].

Raises:
  • ValueError: if embedding_weights is empty.

tf.contrib.layers.separable_convolution2d(*args, **kwargs) {#separable_convolution2d}

Adds a depth-separable 2D convolution with optional batch_norm layer.

This op first performs a depthwise convolution that acts separately on channels, creating a variable called depthwise_weights. If num_outputs is not None, it adds a pointwise convolution that mixes channels, creating a variable called pointwise_weights. Then, if batch_norm_params is None, it adds bias to the result, creating a variable called 'biases', otherwise it adds a batch normalization layer. It finally applies an activation function to produce the end result.

Args:
  • inputs: a tensor of size [batch_size, height, width, channels].
  • num_outputs: the number of pointwise convolution output filters. If is None, then we skip the pointwise convolution stage.
  • 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.
  • depth_multiplier: the number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to num_filters_in * depth_multiplier.
  • stride: a list of length 2: [stride_height, stride_width], specifying the depthwise convolution stride. Can be an int if both strides are the same.
  • padding: one of 'VALID' or 'SAME'.
  • rate: a list of length 2: [rate_height, rate_width], specifying the dilation rates for a'trous convolution. Can be an int if both rates are the same. If any value is larger than one, then both stride values need to be one.
  • activation_fn: activation function, set to None to skip it and maintain a linear activation.
  • normalizer_fn: normalization function to use instead of biases. If normalizer_fn is provided then biases_initializer and biases_regularizer are ignored and biases are not created nor added. default set to None for no normalizer function
  • 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: whether or not the variables should be trainable or not.
  • scope: Optional scope for variable_scope.
Returns:

A Tensor representing the output of the operation.

tf.contrib.layers.unit_norm(*args, **kwargs) {#unit_norm}

Normalizes the given input across the specified dimension to unit length.

Note that the rank of input must be known.

Args:
  • inputs: A Tensor of arbitrary size.
  • dim: The dimension along which the input is normalized.
  • epsilon: A small value to add to the inputs to avoid dividing by zero.
  • scope: Optional scope for variable_scope.
Returns:

The normalized Tensor.

Raises:
  • ValueError: If dim is smaller than the number of dimensions in 'inputs'.

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}

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, or if we find no weights.

tf.contrib.layers.l1_regularizer(scale, scope=None) {#l1_regularizer}

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.
  • scope: An optional scope name.
Returns:

A function with signature l1(weights) that apply L1 regularization.

Raises:
  • ValueError: If scale is negative or if scale is not a float.

tf.contrib.layers.l2_regularizer(scale, scope=None) {#l2_regularizer}

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.
  • scope: An optional scope name.
Returns:

A function with signature l2(weights) that applies L2 regularization.

Raises:
  • ValueError: If scale is negative or if scale is not a float.

tf.contrib.layers.sum_regularizer(regularizer_list, scope=None) {#sum_regularizer}

Returns a function that applies the sum of multiple regularizers.

Args:
  • regularizer_list: A list of regularizers to apply.
  • scope: An optional scope name
Returns:

A function with signature sum_reg(weights) 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) {#xavier_initializer}

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) {#xavier_initializer_conv2d}

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) {#variance_scaling_initializer}

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))
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, learning_rate_decay_fn=None, update_ops=None, variables=None, name=None, summaries=None, colocate_gradients_with_ops=False) {#optimize_loss}

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

Various ways of passing optimizers, include:

  • string, name of the optimizer like 'SGD', 'Adam', see OPTIMIZER_CLS_NAMES for full list. E.g. optimize_loss(..., optimizer='Adam').
  • function, takes learning rate Tensor as argument and must return Optimizer instance. E.g. optimize_loss(..., optimizer=lambda lr: tf.train.MomentumOptimizer(lr, momentum=0.5)). Alternatively, if learning_rate is None, the function takes no arguments. E.g. optimize_loss(..., learning_rate=None, optimizer=lambda: tf.train.MomentumOptimizer(0.5, momentum=0.5)).
  • class, subclass of Optimizer that takes only one required argument - learning rate, such as AdamOptimizer, AdagradOptimizer. E.g. optimize_loss(..., optimizer=tf.train.AdagradOptimizer).
  • object, instance of subclass of Optimizer. E.g., optimizer_loss(..., optimizer=tf.train.AdagradOptimizer(0.5)).
Args:
  • loss: Scalar Tensor.
  • global_step: Scalar int Tensor, step counter for each update. If not supplied, it will be fetched from the default graph (see tf.contrib.framework.get_global_step for details). If it's not been created, no step will be incremented with each weight update. learning_rate_decay_fn requires global_step.
  • learning_rate: float or Tensor, magnitude of update per each training step. Can be None.
  • optimizer: string, class or optimizer instance, used as trainer. string should be name of optimizer, like 'SGD', 'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant. class should be sub-class of tf.Optimizer that implements compute_gradients and apply_gradients functions. optimizer instance should be instantiation 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, callable or None. If float, is provided, a global clipping is applied to prevent the norm of the gradient to exceed this value. Alternatively, a callable can be provided e.g.: adaptive_clipping. This callable takes a list of (gradients, variables) tuples and returns the same thing with the gradients modified.
  • 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. Ignored if learning_rate is not supplied.
  • update_ops: list of update Operations to execute at each step. If None, uses elements of UPDATE_OPS collection. The order of execution between update_ops and loss is non-deterministic.
  • 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.
  • summaries: List of internal quantities to visualize on tensorboard. If not set only the loss and the learning rate will be reported. The complete list is in OPTIMIZER_SUMMARIES.
  • colocate_gradients_with_ops: If True, try colocating gradients with the corresponding op.
Returns:

Training op.

Raises:
  • ValueError: if:
    • loss is an invalid type or shape.
    • global_step is an invalid type or shape.
    • learning_rate is an invalid type or value.
    • optimizer is wrong type.
    • clip_gradients is not float or callable.
    • learning_rate and learning_rate_decay_fn are supplied, but no global_step is available.

Summaries

Helper functions to summarize specific variables or ops.

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

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_tensor}

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_tensors}

Summarize a set of tensors.

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

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}

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}

Creates a _BucketizedColumn for discretizing dense input.

Args:
  • source_column: A _RealValuedColumn defining dense column.
  • boundaries: A list of floats specifying the boundaries. It has to be sorted.
Returns:

A _BucketizedColumn.

Raises:
  • ValueError: if 'boundaries' is empty or not sorted.

tf.contrib.layers.check_feature_columns(feature_columns) {#check_feature_columns}

Checks the validity of the set of FeatureColumns.

Args:
  • feature_columns: A set of instances or subclasses of FeatureColumn.
Raises:
  • ValueError: If there are duplicate feature column keys.

tf.contrib.layers.create_feature_spec_for_parsing(feature_columns) {#create_feature_spec_for_parsing}

Helper that prepares features config from input feature_columns.

The returned feature config can be used as arg 'features' in tf.parse_example.

Typical usage example:

# Define features and transformations
feature_a = sparse_column_with_vocabulary_file(...)
feature_b = real_valued_column(...)
feature_c_bucketized = bucketized_column(real_valued_column("feature_c"), ...)
feature_a_x_feature_c = crossed_column(
  columns=[feature_a, feature_c_bucketized], ...)

feature_columns = set(
  [feature_b, feature_c_bucketized, feature_a_x_feature_c])
batch_examples = tf.parse_example(
    serialized=serialized_examples,
    features=create_feature_spec_for_parsing(feature_columns))

For the above example, create_feature_spec_for_parsing would return the dict: { "feature_a": parsing_ops.VarLenFeature(tf.string), "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32), "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32) }

Args:
  • feature_columns: An iterable containing all the feature columns. All items should be instances of classes derived from _FeatureColumn, unless feature_columns is a dict -- in which case, this should be true of all values in the dict.
Returns:

A dict mapping feature keys to FixedLenFeature or VarLenFeature values.

tf.contrib.layers.crossed_column(columns, hash_bucket_size, combiner=None, ckpt_to_load_from=None, tensor_name_in_ckpt=None, hash_key=None) {#crossed_column}

Creates a _CrossedColumn for performing feature crosses.

Args:
  • columns: An iterable of _FeatureColumn. Items can be an instance of _SparseColumn, _CrossedColumn, or _BucketizedColumn.
  • hash_bucket_size: An int that is > 1. The number of buckets.
  • combiner: A combiner string, supports sum, mean, sqrtn.
  • ckpt_to_load_from: (Optional). String representing checkpoint name/pattern to restore the column weights. Required if tensor_name_in_ckpt is not None.
  • tensor_name_in_ckpt: (Optional). Name of the Tensor in the provided checkpoint from which to restore the column weights. Required if ckpt_to_load_from is not None.
  • hash_key: Specify the hash_key that will be used by the FingerprintCat64 function to combine the crosses fingerprints on SparseFeatureCrossOp (optional).
Returns:

A _CrossedColumn.

Raises:
  • TypeError: if any item in columns is not an instance of _SparseColumn, _CrossedColumn, or _BucketizedColumn, or hash_bucket_size is not an int.
  • ValueError: if hash_bucket_size is not > 1 or len(columns) is not > 1.

tf.contrib.layers.embedding_column(sparse_id_column, dimension, combiner=None, initializer=None, ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None) {#embedding_column}

Creates an _EmbeddingColumn for feeding sparse data into a DNN.

Args:
  • sparse_id_column: A _SparseColumn which is created by for example sparse_column_with_* or crossed_column functions. Note that combiner defined in sparse_id_column is ignored.
  • dimension: An integer specifying dimension of the embedding.
  • combiner: A string specifying how to reduce if there are multiple entries in a single row. Currently "mean", "sqrtn" and "sum" are supported. Each of this can be considered an example level normalization on the column:
    • "sum": do not normalize
    • "mean": do l1 normalization
    • "sqrtn": do l2 normalization For more information: tf.embedding_lookup_sparse.
  • initializer: A variable initializer function to be used in embedding variable initialization. If not specified, defaults to tf.truncated_normal_initializer with mean 0.0 and standard deviation 1/sqrt(sparse_id_column.length).
  • ckpt_to_load_from: (Optional). String representing checkpoint name/pattern to restore the column weights. Required if tensor_name_in_ckpt is not None.
  • tensor_name_in_ckpt: (Optional). Name of the Tensor in the provided checkpoint from which to restore the column weights. Required if ckpt_to_load_from is not None.
  • max_norm: (Optional). If not None, embedding values are l2-normalized to the value of max_norm.
Returns:

An _EmbeddingColumn.

tf.contrib.layers.scattered_embedding_column(column_name, size, dimension, hash_key, combiner=None, initializer=None) {#scattered_embedding_column}

Creates an embedding column of a sparse feature using parameter hashing.

The i-th embedding component of a value v is found by retrieving an embedding weight whose index is a fingerprint of the pair (v,i).

An embedding column with sparse_column_with_hash_bucket such as embedding_column( sparse_column_with_hash_bucket(column_name, bucket_size), dimension)

could be replaced by scattered_embedding_column( column_name, size=bucket_size * dimension, dimension=dimension, hash_key=tf.contrib.layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY)

for the same number of embedding parameters and hopefully reduced impact of collisions with a cost of slowing down training.

Args:
  • column_name: A string defining sparse column name.
  • size: An integer specifying the number of parameters in the embedding layer.
  • dimension: An integer specifying dimension of the embedding.
  • hash_key: Specify the hash_key that will be used by the FingerprintCat64 function to combine the crosses fingerprints on SparseFeatureCrossOp.
  • combiner: A string specifying how to reduce if there are multiple entries in a single row. Currently "mean", "sqrtn" and "sum" are supported. Each of this can be thought as example level normalizations on the column:
    • "sum": do not normalize features in the column
    • "mean": do l1 normalization on features in the column
    • "sqrtn": do l2 normalization on features in the column For more information: tf.embedding_lookup_sparse.
  • initializer: A variable initializer function to be used in embedding variable initialization. If not specified, defaults to tf.truncated_normal_initializer with mean 0 and standard deviation 0.1.
Returns:

A _ScatteredEmbeddingColumn.

Raises:
  • ValueError: if dimension or size is not a positive integer; or if combiner is not supported.

tf.contrib.layers.input_from_feature_columns(columns_to_tensors, feature_columns, weight_collections=None, trainable=True, scope=None) {#input_from_feature_columns}

A tf.contrib.layer style input layer builder based on FeatureColumns.

Generally a single example in training data is described with feature columns. At the first layer of the model, this column oriented data should be converted to a single tensor. Each feature column needs a different kind of operation during this conversion. For example sparse features need a totally different handling than continuous features.

Example:

  # Building model for training
  columns_to_tensor = tf.parse_example(...)
  first_layer = input_from_feature_columns(
      columns_to_tensors=columns_to_tensor,
      feature_columns=feature_columns)
  second_layer = fully_connected(inputs=first_layer, ...)
  ...

where feature_columns can be defined as follows:

  sparse_feature = sparse_column_with_hash_bucket(
      column_name="sparse_col", ...)
  sparse_feature_emb = embedding_column(sparse_id_column=sparse_feature, ...)
  real_valued_feature = real_valued_column(...)
  real_valued_buckets = bucketized_column(
      source_column=real_valued_feature, ...)

  feature_columns=[sparse_feature_emb, real_valued_buckets]
Args:
  • columns_to_tensors: A mapping from feature column to tensors. 'string' key means a base feature (not-transformed). It can have FeatureColumn as a key too. That means that FeatureColumn is already transformed by input pipeline. For example, inflow may have handled transformations.
  • feature_columns: A set containing all the feature columns. All items in the set should be instances of classes derived by FeatureColumn.
  • weight_collections: List of graph collections to which weights are added.
  • trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
  • scope: Optional scope for variable_scope.
Returns:

A Tensor which can be consumed by hidden layers in the neural network.

Raises:
  • ValueError: if FeatureColumn cannot be consumed by a neural network.

tf.contrib.layers.joint_weighted_sum_from_feature_columns(columns_to_tensors, feature_columns, num_outputs, weight_collections=None, trainable=True, scope=None) {#joint_weighted_sum_from_feature_columns}

A restricted linear prediction builder based on FeatureColumns.

As long as all feature columns are unweighted sparse columns this computes the prediction of a linear model which stores all weights in a single variable.

Args:
  • columns_to_tensors: A mapping from feature column to tensors. 'string' key means a base feature (not-transformed). It can have FeatureColumn as a key too. That means that FeatureColumn is already transformed by input pipeline. For example, inflow may have handled transformations.
  • feature_columns: A set containing all the feature columns. All items in the set should be instances of classes derived from FeatureColumn.
  • num_outputs: An integer specifying number of outputs. Default value is 1.
  • weight_collections: List of graph collections to which weights are added.
  • trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
  • scope: Optional scope for variable_scope.
Returns:

A tuple containing:

* A Tensor which represents predictions of a linear model.
* A list of Variables storing the weights.
* A Variable which is used for bias.
Raises:
  • ValueError: if FeatureColumn cannot be used for linear predictions.

tf.contrib.layers.make_place_holder_tensors_for_base_features(feature_columns) {#make_place_holder_tensors_for_base_features}

Returns placeholder tensors for inference.

Args:
  • feature_columns: An iterable containing all the feature columns. All items should be instances of classes derived from _FeatureColumn.
Returns:

A dict mapping feature keys to SparseTensors (sparse columns) or placeholder Tensors (dense columns).

tf.contrib.layers.one_hot_column(sparse_id_column) {#one_hot_column}

Creates an _OneHotColumn for a one-hot or multi-hot repr in a DNN.

Args:
  • sparse_id_column: A _SparseColumn which is created by sparse_column_with_* or crossed_column functions. Note that combiner defined in sparse_id_column is ignored.
Returns:

An _OneHotColumn.

tf.contrib.layers.parse_feature_columns_from_examples(serialized, feature_columns, name=None, example_names=None) {#parse_feature_columns_from_examples}

Parses tf.Examples to extract tensors for given feature_columns.

This is a wrapper of 'tf.parse_example'.

Example:

columns_to_tensor = parse_feature_columns_from_examples(
    serialized=my_data,
    feature_columns=my_features)

# Where my_features are:
# Define features and transformations
sparse_feature_a = sparse_column_with_keys(
    column_name="sparse_feature_a", keys=["AB", "CD", ...])

embedding_feature_a = embedding_column(
    sparse_id_column=sparse_feature_a, dimension=3, combiner="sum")

sparse_feature_b = sparse_column_with_hash_bucket(
    column_name="sparse_feature_b", hash_bucket_size=1000)

embedding_feature_b = embedding_column(
    sparse_id_column=sparse_feature_b, dimension=16, combiner="sum")

crossed_feature_a_x_b = crossed_column(
    columns=[sparse_feature_a, sparse_feature_b], hash_bucket_size=10000)

real_feature = real_valued_column("real_feature")
real_feature_buckets = bucketized_column(
    source_column=real_feature, boundaries=[...])

my_features = [embedding_feature_b, real_feature_buckets, embedding_feature_a]
Args:
  • serialized: A vector (1-D Tensor) of strings, a batch of binary serialized Example protos.
  • feature_columns: An iterable containing all the feature columns. All items should be instances of classes derived from _FeatureColumn.
  • name: A name for this operation (optional).
  • example_names: A vector (1-D Tensor) of strings (optional), the names of the serialized protos in the batch.
Returns:

A dict mapping FeatureColumn to Tensor and SparseTensor values.

tf.contrib.layers.parse_feature_columns_from_sequence_examples(serialized, context_feature_columns, sequence_feature_columns, name=None, example_name=None) {#parse_feature_columns_from_sequence_examples}

Parses tf.SequenceExamples to extract tensors for given FeatureColumns.

Args:
  • serialized: A scalar (0-D Tensor) of type string, a single serialized SequenceExample proto.
  • context_feature_columns: An iterable containing the feature columns for context features. All items should be instances of classes derived from _FeatureColumn. Can be None.
  • sequence_feature_columns: An iterable containing the feature columns for sequence features. All items should be instances of classes derived from _FeatureColumn. Can be None.
  • name: A name for this operation (optional).
  • example_name: A scalar (0-D Tensor) of type string (optional), the names of the serialized proto.
Returns:

A tuple consisting of:

  • context_features: a dict mapping FeatureColumns from context_feature_columns to their parsed Tensors/SparseTensors.
  • sequence_features: a dict mapping FeatureColumns from sequence_feature_columns to their parsed Tensors/SparseTensors.

tf.contrib.layers.real_valued_column(column_name, dimension=1, default_value=None, dtype=tf.float32, normalizer=None) {#real_valued_column}

Creates a _RealValuedColumn for dense numeric data.

Args:
  • column_name: A string defining real valued column name.
  • dimension: An integer specifying dimension of the real valued column. The default is 1. When dimension is not None, the Tensor representing the _RealValuedColumn will have the shape of [batch_size, dimension]. A None dimension means the feature column should be treat as variable length and will be parsed as a SparseTensor.
  • default_value: A single value compatible with dtype or a list of values compatible with dtype which the column takes on during tf.Example parsing if data is missing. When dimension is not None, a default value of None will cause tf.parse_example to fail if an example does not contain this column. If a single value is provided, the same value will be applied as the default value for every dimension. If a list of values is provided, the length of the list should be equal to the value of dimension. Only scalar default value is supported in case dimension is not specified.
  • dtype: defines the type of values. Default value is tf.float32. Must be a non-quantized, real integer or floating point type.
  • normalizer: If not None, a function that can be used to normalize the value of the real valued column after default_value is applied for parsing. Normalizer function takes the input tensor as its argument, and returns the output tensor. (e.g. lambda x: (x - 3.0) / 4.2). Note that for variable length columns, the normalizer should expect an input_tensor of type SparseTensor.
Returns:

A _RealValuedColumn.

Raises:
  • TypeError: if dimension is not an int
  • ValueError: if dimension is not a positive integer
  • TypeError: if default_value is a list but its length is not equal to the value of dimension.
  • TypeError: if default_value is not compatible with dtype.
  • ValueError: if dtype is not convertable to tf.float32.

tf.contrib.layers.shared_embedding_columns(sparse_id_columns, dimension, combiner=None, shared_embedding_name=None, initializer=None, ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None) {#shared_embedding_columns}

Creates a list of _EmbeddingColumn sharing the same embedding.

Args:
  • sparse_id_columns: An iterable of _SparseColumn, such as those created by sparse_column_with_* or crossed_column functions. Note that combiner defined in each sparse_id_column is ignored.
  • dimension: An integer specifying dimension of the embedding.
  • combiner: A string specifying how to reduce if there are multiple entries in a single row. Currently "mean", "sqrtn" and "sum" are supported. Each of this can be considered an example level normalization on the column:
    • "sum": do not normalize
    • "mean": do l1 normalization
    • "sqrtn": do l2 normalization For more information: tf.embedding_lookup_sparse.
  • shared_embedding_name: (Optional). A string specifying the name of shared embedding weights. This will be needed if you want to reference the shared embedding separately from the generated _EmbeddingColumn.
  • initializer: A variable initializer function to be used in embedding variable initialization. If not specified, defaults to tf.truncated_normal_initializer with mean 0.0 and standard deviation 1/sqrt(sparse_id_columns[0].length).
  • ckpt_to_load_from: (Optional). String representing checkpoint name/pattern to restore the column weights. Required if tensor_name_in_ckpt is not None.
  • tensor_name_in_ckpt: (Optional). Name of the Tensor in the provided checkpoint from which to restore the column weights. Required if ckpt_to_load_from is not None.
  • max_norm: (Optional). If not None, embedding values are l2-normalized to the value of max_norm.
Returns:

A tuple of _EmbeddingColumn with shared embedding space.

Raises:
  • ValueError: if sparse_id_columns is empty, or its elements are not compatible with each other.
  • TypeError: if sparse_id_columns is not a sequence or is a string. If at least one element of sparse_id_columns is not a SparseTensor.

tf.contrib.layers.sparse_column_with_hash_bucket(column_name, hash_bucket_size, combiner=None, dtype=tf.string) {#sparse_column_with_hash_bucket}

Creates a _SparseColumn with hashed bucket configuration.

Use this when your sparse features are in string or integer format, but you don't have a vocab file that maps each value to an integer ID. output_id = Hash(input_feature_string) % bucket_size

Args:
  • column_name: A string defining sparse column name.
  • hash_bucket_size: An int that is > 1. The number of buckets.
  • combiner: A string specifying how to reduce if the sparse column is multivalent. Currently "mean", "sqrtn" and "sum" are supported, with "sum" the default:
    • "sum": do not normalize features in the column
    • "mean": do l1 normalization on features in the column
    • "sqrtn": do l2 normalization on features in the column For more information: tf.embedding_lookup_sparse.
  • dtype: The type of features. Only string and integer types are supported.
Returns:

A _SparseColumn with hashed bucket configuration

Raises:
  • ValueError: hash_bucket_size is not greater than 2.
  • ValueError: dtype is neither string nor integer.

tf.contrib.layers.sparse_column_with_integerized_feature(column_name, bucket_size, combiner=None, dtype=tf.int64) {#sparse_column_with_integerized_feature}

Creates an integerized _SparseColumn.

Use this when your features are already pre-integerized into int64 IDs. output_id = input_feature

Args:
  • column_name: A string defining sparse column name.
  • bucket_size: An int that is > 1. The number of buckets. It should be bigger than maximum feature. In other words features in this column should be an int64 in range [0, bucket_size)
  • combiner: A string specifying how to reduce if the sparse column is multivalent. Currently "mean", "sqrtn" and "sum" are supported, with "sum" the default:
    • "sum": do not normalize features in the column
    • "mean": do l1 normalization on features in the column
    • "sqrtn": do l2 normalization on features in the column For more information: tf.embedding_lookup_sparse.
  • dtype: Type of features. It should be an integer type. Default value is dtypes.int64.
Returns:

An integerized _SparseColumn definition.

Raises:
  • ValueError: bucket_size is not greater than 1.
  • ValueError: dtype is not integer.

tf.contrib.layers.sparse_column_with_keys(column_name, keys, default_value=-1, combiner=None) {#sparse_column_with_keys}

Creates a _SparseColumn with keys.

Look up logic is as follows: lookup_id = index_of_feature_in_keys if feature in keys else default_value

Args:
  • column_name: A string defining sparse column name.
  • keys: a string list defining vocabulary.
  • default_value: The value to use for out-of-vocabulary feature values. Default is -1.
  • combiner: A string specifying how to reduce if the sparse column is multivalent. Currently "mean", "sqrtn" and "sum" are supported, with "sum" the default:
    • "sum": do not normalize features in the column
    • "mean": do l1 normalization on features in the column
    • "sqrtn": do l2 normalization on features in the column For more information: tf.embedding_lookup_sparse.
Returns:

A _SparseColumnKeys with keys configuration.

tf.contrib.layers.weighted_sparse_column(sparse_id_column, weight_column_name, dtype=tf.float32) {#weighted_sparse_column}

Creates a _SparseColumn by combining sparse_id_column with a weight column.

Example:

sparse_feature = sparse_column_with_hash_bucket(column_name="sparse_col",
                                                hash_bucket_size=1000)
weighted_feature = weighted_sparse_column(sparse_id_column=sparse_feature,
                                          weight_column_name="weights_col")

This configuration assumes that input dictionary of model contains the following two items: * (key="sparse_col", value=sparse_tensor) where sparse_tensor is a SparseTensor. * (key="weights_col", value=weights_tensor) where weights_tensor is a SparseTensor. Following are assumed to be true: * sparse_tensor.indices = weights_tensor.indices * sparse_tensor.dense_shape = weights_tensor.dense_shape

Args:
  • sparse_id_column: A _SparseColumn which is created by sparse_column_with_* functions.
  • weight_column_name: A string defining a sparse column name which represents weight or value of the corresponding sparse id feature.
  • dtype: Type of weights, such as tf.float32
Returns:

A _WeightedSparseColumn composed of two sparse features: one represents id, the other represents weight (value) of the id feature in that example.

Raises:
  • ValueError: if dtype is not convertible to float.

tf.contrib.layers.weighted_sum_from_feature_columns(columns_to_tensors, feature_columns, num_outputs, weight_collections=None, trainable=True, scope=None) {#weighted_sum_from_feature_columns}

A tf.contrib.layer style linear prediction builder based on FeatureColumns.

Generally a single example in training data is described with feature columns. This function generates weighted sum for each num_outputs. Weighted sum refers to logits in classification problems. It refers to prediction itself for linear regression problems.

Example:

# Building model for training
feature_columns = (
    real_valued_column("my_feature1"),
    ...
)
columns_to_tensor = tf.parse_example(...)
logits = weighted_sum_from_feature_columns(
    columns_to_tensors=columns_to_tensor,
    feature_columns=feature_columns,
    num_outputs=1)
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,
                                               logits=logits)
Args:
  • columns_to_tensors: A mapping from feature column to tensors. 'string' key means a base feature (not-transformed). It can have FeatureColumn as a key too. That means that FeatureColumn is already transformed by input pipeline. For example, inflow may have handled transformations.
  • feature_columns: A set containing all the feature columns. All items in the set should be instances of classes derived from FeatureColumn.
  • num_outputs: An integer specifying number of outputs. Default value is 1.
  • weight_collections: List of graph collections to which weights are added.
  • trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
  • scope: Optional scope for variable_scope.
Returns:

A tuple containing:

* A Tensor which represents predictions of a linear model.
* A dictionary which maps feature_column to corresponding Variable.
* A Variable which is used for bias.
Raises:
  • ValueError: if FeatureColumn cannot be used for linear predictions.