deepctr.layers.core module

Author:

Weichen Shen,wcshen1994@163.com

class deepctr.layers.core.DNN(hidden_units, activation='relu', l2_reg=0, dropout_rate=0, use_bn=False, seed=1024, **kwargs)

The Multi Layer Percetron

Input shape

• nD tensor with shape: (batch_size, ..., input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim).

Output shape

• nD tensor with shape: (batch_size, ..., hidden_size[-1]). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, hidden_size[-1]).

Arguments

• hidden_units:list of positive integer, the layer number and units in each layer.
• activation: Activation function to use.
• l2_reg: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix.
• dropout_rate: float in [0,1). Fraction of the units to dropout.
• use_bn: bool. Whether use BatchNormalization before activation or not.
• seed: A Python integer to use as random seed.

build(input_shape)

Creates the variables of the layer.

call(inputs, training=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.

Assumes that the layer will be built to match that input shape provided.

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.core.LocalActivationUnit(hidden_units=(64, 32), activation='sigmoid', l2_reg=0, dropout_rate=0, use_bn=False, seed=1024, **kwargs)

The LocalActivationUnit used in DIN with which the representation of user interests varies adaptively given different candidate items.

Input shape

• A list of two 3D tensor with shape: (batch_size, 1, embedding_size) and (batch_size, T, embedding_size)

Output shape

• 3D tensor with shape: (batch_size, T, 1).

Arguments

• hidden_units:list of positive integer, the attention net layer number and units in each layer.
• activation: Activation function to use in attention net.
• l2_reg: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix of attention net.
• dropout_rate: float in [0,1). Fraction of the units to dropout in attention net.
• use_bn: bool. Whether use BatchNormalization before activation or not in attention net.
• seed: A Python integer to use as random seed.

References

• [Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059-1068.](https://arxiv.org/pdf/1706.06978.pdf)

build(input_shape)

Creates the variables of the layer.

call(inputs, training=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).

compute_output_shape(input_shape)

Computes the output shape of the layer.

Assumes that the layer will be built to match that input shape provided.

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.core.PredictionLayer(task='binary', use_bias=True, **kwargs)

Arguments

• task: str, "binary" for binary logloss or "regression" for regression loss
• use_bias: bool.Whether add bias term or not.

build(input_shape)

Creates the variables of the layer.

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_output_shape(input_shape)

Computes the output shape of the layer.

Assumes that the layer will be built to match that input shape provided.

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.