deepctr.layers.utils module

Author:

Weichen Shen,weichenswc@163.com

class deepctr.layers.utils.Add(**kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

build(input_shape)

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Arguments:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(inputs, **kwargs)

This is where the layer’s logic lives.

Arguments:

inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.

Returns:

A tensor or list/tuple of tensors.

class deepctr.layers.utils.Hash(num_buckets, mask_zero=False, vocabulary_path=None, default_value=0, **kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

Looks up keys in a table when setup vocabulary_path, which outputs the corresponding values. If vocabulary_path is not set, Hash will hash the input to [0,num_buckets). When mask_zero = True, input value 0 or 0.0 will be set to 0, and other value will be set in range [1,num_buckets).

The following snippet initializes a Hash with vocabulary_path file with the first column as keys and second column as values:

• 1,emerson
• 2,lake
• 3,palmer

>>> hash = Hash(
...   num_buckets=3+1,
...   vocabulary_path=filename,
...   default_value=0)
>>> hash(tf.constant('lake')).numpy()
2
>>> hash(tf.constant('lakeemerson')).numpy()
0

Args:

num_buckets: An int that is >= 1. The number of buckets or the vocabulary size + 1

when vocabulary_path is setup.

mask_zero: default is False. The Hash value will hash input 0 or 0.0 to value 0 when

the mask_zero is True. mask_zero is not used when vocabulary_path is setup.

vocabulary_path: default None. The CSV text file path of the vocabulary hash, which contains

two columns seperated by delimiter comma, the first column is the value and the second is the key. The key data type is string, the value data type is int. The path must be accessible from wherever Hash is initialized.

default_value: default ‘0’. The default value if a key is missing in the table. **kwargs: Additional keyword arguments.

build(input_shape)

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Arguments:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, mask=None, **kwargs)

This is where the layer’s logic lives.

Arguments:

inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.

Returns:

A tensor or list/tuple of tensors.

compute_output_shape(input_shape)

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Arguments:

input_shape: Shape tuple (tuple of integers)

or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns:

An input shape tuple.

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns:

Python dictionary.

class deepctr.layers.utils.Linear(l2_reg=0.0, mode=0, use_bias=False, seed=1024, **kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

build(input_shape)

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Arguments:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(inputs, **kwargs)

This is where the layer’s logic lives.

Arguments:

inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.

Returns:

A tensor or list/tuple of tensors.

compute_mask(inputs, mask)

Computes an output mask tensor.

Arguments:

inputs: Tensor or list of tensors. mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors,

one per output tensor of the layer).

compute_output_shape(input_shape)

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Arguments:

input_shape: Shape tuple (tuple of integers)

or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns:

An input shape tuple.

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns:

Python dictionary.

class deepctr.layers.utils.NoMask(**kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

build(input_shape)

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Arguments:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, mask=None, **kwargs)

This is where the layer’s logic lives.

Arguments:

inputs: Input tensor, or list/tuple of input tensors. **kwargs: Additional keyword arguments.

Returns:

A tensor or list/tuple of tensors.

compute_mask(inputs, mask)

Computes an output mask tensor.

Arguments:

inputs: Tensor or list of tensors. mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors,

one per output tensor of the layer).

deepctr.layers.utils.add_func(inputs)

deepctr.layers.utils.combined_dnn_input(sparse_embedding_list, dense_value_list)

deepctr.layers.utils.concat_func(inputs, axis=-1, mask=False)

deepctr.layers.utils.div(x, y, name=None)

deepctr.layers.utils.reduce_max(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)

deepctr.layers.utils.reduce_mean(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)

deepctr.layers.utils.reduce_sum(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)

deepctr.layers.utils.softmax(logits, dim=-1, name=None)