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)[source]¶ 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)
.
 nD tensor with shape:
 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])
.
 nD tensor with shape:
 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.

call
(inputs, training=None, **kwargs)[source]¶ 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)[source]¶ 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
()[source]¶ 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)[source]¶ 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)
 A list of two 3D tensor with shape:
 Output shape
 3D tensor with shape:
(batch_size, T, 1)
.
 3D tensor with shape:
 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 clickthrough rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 10591068.](https://arxiv.org/pdf/1706.06978.pdf)

call
(inputs, training=None, **kwargs)[source]¶ 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)[source]¶ 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)[source]¶ 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
()[source]¶ 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)[source]¶  Arguments
 task: str,
"binary"
for binary logloss or"regression"
for regression loss  use_bias: bool.Whether add bias term or not.
 task: str,

call
(inputs, **kwargs)[source]¶ 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)[source]¶ 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
()[source]¶ 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.