deepctr.layers.interaction module¶
 Authors:
 Weichen Shen,weichenswc@163.com, Harshit Pande

class
deepctr.layers.interaction.
AFMLayer
(attention_factor=4, l2_reg_w=0, dropout_rate=0, seed=1024, **kwargs)[source]¶ Attentonal Factorization Machine models pairwise (order2) feature interactions without linear term and bias.
 Input shape
 A list of 3D tensor with shape:
(batch_size,1,embedding_size)
.
 A list of 3D tensor with shape:
 Output shape
 2D tensor with shape:
(batch_size, 1)
.
 2D tensor with shape:
 Arguments
 attention_factor : Positive integer, dimensionality of the
attention network output space. l2_reg_w : float between 0 and 1. L2 regularizer strength
applied to attention network. dropout_rate : float between in [0,1). Fraction of the attention net output units to dropout.
 seed : A Python integer to use as random seed.
 References
 [Attentional Factorization Machines : Learning the Weight of Feature
Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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, 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.
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
()[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.interaction.
BiInteractionPooling
(**kwargs)[source]¶ BiInteraction Layer used in Neural FM,compress the pairwise elementwise product of features into one single vector.
 Input shape
 A 3D tensor with shape:
(batch_size,field_size,embedding_size)
.
 A 3D tensor with shape:
 Output shape
 3D tensor with shape:
(batch_size,1,embedding_size)
.
 3D tensor with shape:
 References
 [He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355364.](http://arxiv.org/abs/1708.05027)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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.

class
deepctr.layers.interaction.
BilinearInteraction
(bilinear_type='interaction', seed=1024, **kwargs)[source]¶ BilinearInteraction Layer used in FiBiNET.
 Input shape
 A list of 3D tensor with shape:
(batch_size,1,embedding_size)
. Its length isfiled_size
.
 A list of 3D tensor with shape:
 Output shape
 3D tensor with shape:
(batch_size,filed_size*(filed_size1)/2,embedding_size)
.
 3D tensor with shape:
 Arguments
 bilinear_type : String, types of bilinear functions used in this layer.
 seed : A Python integer to use as random seed.
 References
 [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for ClickThrough Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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
()[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.interaction.
CIN
(layer_size=(128, 128), activation='relu', split_half=True, l2_reg=1e05, seed=1024, **kwargs)[source]¶ Compressed Interaction Network used in xDeepFM.This implemention is adapted from code that the author of the paper published on https://github.com/Leavingseason/xDeepFM.
 Input shape
 3D tensor with shape:
(batch_size,field_size,embedding_size)
.
 3D tensor with shape:
 Output shape
 2D tensor with shape:
(batch_size, featuremap_num)
featuremap_num = sum(self.layer_size[:1]) // 2 + self.layer_size[1]
ifsplit_half=True
,elsesum(layer_size)
.
 2D tensor with shape:
 Arguments
 layer_size : list of int.Feature maps in each layer.
 activation : activation function used on feature maps.
 split_half : bool.if set to False, half of the feature maps in each hidden will connect to output unit.
 seed : A Python integer to use as random seed.
 References
 [Lian J, Zhou X, Zhang F, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems[J]. arXiv preprint arXiv:1803.05170, 2018.] (https://arxiv.org/pdf/1803.05170.pdf)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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
()[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.interaction.
CrossNet
(layer_num=2, parameterization='vector', l2_reg=0, seed=1024, **kwargs)[source]¶ The Cross Network part of Deep&Cross Network model, which leans both low and high degree cross feature.
 Input shape
 2D tensor with shape:
(batch_size, units)
.
 2D tensor with shape:
 Output shape
 2D tensor with shape:
(batch_size, units)
.
 2D tensor with shape:
 Arguments
 layer_num: Positive integer, the cross layer number
 l2_reg: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix
 parameterization: string,
"vector"
or"matrix"
, way to parameterize the cross network.  seed: A Python integer to use as random seed.
 References
 [Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD’17. ACM, 2017: 12.](https://arxiv.org/abs/1708.05123)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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
()[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.interaction.
CrossNetMix
(low_rank=32, num_experts=4, layer_num=2, l2_reg=0, seed=1024, **kwargs)[source]¶ The Cross Network part of DCNMix model, which improves DCNM by: 1 add MOE to learn feature interactions in different subspaces 2 add nonlinear transformations in lowdimensional space
 Input shape
 2D tensor with shape:
(batch_size, units)
.
 2D tensor with shape:
 Output shape
 2D tensor with shape:
(batch_size, units)
.
 2D tensor with shape:
 Arguments
 low_rank : Positive integer, dimensionality of lowrank sapce.
 num_experts : Positive integer, number of experts.
 layer_num: Positive integer, the cross layer number
 l2_reg: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix
 seed: A Python integer to use as random seed.
 References
 [Wang R, Shivanna R, Cheng D Z, et al. DCNM: Improved Deep & Cross Network for Feature Cross Learning in Webscale Learning to Rank Systems[J]. 2020.](https://arxiv.org/abs/2008.13535)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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
()[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.interaction.
FGCNNLayer
(filters=(14, 16), kernel_width=(7, 7), new_maps=(3, 3), pooling_width=(2, 2), **kwargs)[source]¶ Feature Generation Layer used in FGCNN,including Convolution,MaxPooling and Recombination.
 Input shape
 A 3D tensor with shape:
(batch_size,field_size,embedding_size)
.
 A 3D tensor with shape:
 Output shape
 3D tensor with shape:
(batch_size,new_feture_num,embedding_size)
.
 3D tensor with shape:
 References
 [Liu B, Tang R, Chen Y, et al. Feature Generation by Convolutional Neural Network for ClickThrough Rate Prediction[J]. arXiv preprint arXiv:1904.04447, 2019.](https://arxiv.org/pdf/1904.04447)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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
()[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.interaction.
FM
(**kwargs)[source]¶ Factorization Machine models pairwise (order2) feature interactions without linear term and bias.
 Input shape
 3D tensor with shape:
(batch_size,field_size,embedding_size)
.
 3D tensor with shape:
 Output shape
 2D tensor with shape:
(batch_size, 1)
.
 2D tensor with shape:
 References
 [Factorization Machines](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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.

class
deepctr.layers.interaction.
FieldWiseBiInteraction
(use_bias=True, seed=1024, **kwargs)[source]¶ FieldWise BiInteraction Layer used in FLEN,compress the pairwise elementwise product of features into one single vector.
 Input shape
 A list of 3D tensor with shape:
(batch_size,field_size,embedding_size)
.
 A list of 3D tensor with shape:
 Output shape
 2D tensor with shape:
(batch_size,embedding_size)
.
 2D tensor with shape:
 Arguments
 use_bias : Boolean, if use bias.
 seed : A Python integer to use as random seed.
 References
 [FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/pdf/1911.04690)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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
()[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.interaction.
FwFMLayer
(num_fields=4, regularizer=1e06, **kwargs)[source]¶ Fieldweighted Factorization Machines
 Input shape
 3D tensor with shape:
(batch_size,field_size,embedding_size)
.
 3D tensor with shape:
 Output shape
 2D tensor with shape:
(batch_size, 1)
.
 2D tensor with shape:
 Arguments
 num_fields : integer for number of fields
 regularizer : L2 regularizer weight for the field strength parameters of FwFM
 References
 [Fieldweighted Factorization Machines for ClickThrough Rate Prediction in Display Advertising]

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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
()[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.interaction.
InnerProductLayer
(reduce_sum=True, **kwargs)[source]¶ InnerProduct Layer used in PNN that compute the elementwise product or inner product between feature vectors.
 Input shape
 a list of 3D tensor with shape:
(batch_size,1,embedding_size)
.
 a list of 3D tensor with shape:
 Output shape
 3D tensor with shape:
(batch_size, N*(N1)/2 ,1)
if use reduce_sum. or 3D tensor with shape:(batch_size, N*(N1)/2, embedding_size )
if not use reduce_sum.
 3D tensor with shape:
 Arguments
 reduce_sum: bool. Whether return inner product or elementwise product
 References
 [Qu Y, Cai H, Ren K, et al. Productbased neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 11491154.](https://arxiv.org/pdf/1611.00144.pdf)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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
()[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.interaction.
InteractingLayer
(att_embedding_size=8, head_num=2, use_res=True, seed=1024, **kwargs)[source]¶ A Layer used in AutoInt that model the correlations between different feature fields by multihead selfattention mechanism.
 Input shape
 A 3D tensor with shape:
(batch_size,field_size,embedding_size)
.
 A 3D tensor with shape:
 Output shape
 3D tensor with shape:
(batch_size,field_size,att_embedding_size * head_num)
.
 3D tensor with shape:
 Arguments
 att_embedding_size: int.The embedding size in multihead selfattention network.
 head_num: int.The head number in multihead selfattention network.
 use_res: bool.Whether or not use standard residual connections before output.
 seed: A Python integer to use as random seed.
 References
 [Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via SelfAttentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.](https://arxiv.org/abs/1810.11921)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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
()[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.interaction.
OutterProductLayer
(kernel_type='mat', seed=1024, **kwargs)[source]¶ OutterProduct Layer used in PNN.This implemention is adapted from code that the author of the paper published on https://github.com/Atomu2014/productnets.
 Input shape
 A list of N 3D tensor with shape:
(batch_size,1,embedding_size)
.
 A list of N 3D tensor with shape:
 Output shape
 2D tensor with shape:
(batch_size,N*(N1)/2 )
.
 2D tensor with shape:
 Arguments
 kernel_type: str. The kernel weight matrix type to use,can be mat,vec or num
 seed: A Python integer to use as random seed.
 References
 [Qu Y, Cai H, Ren K, et al. Productbased neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 11491154.](https://arxiv.org/pdf/1611.00144.pdf)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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)[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.
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
()[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.interaction.
SENETLayer
(reduction_ratio=3, seed=1024, **kwargs)[source]¶ SENETLayer used in FiBiNET.
 Input shape
 A list of 3D tensor with shape:
(batch_size,1,embedding_size)
.
 A list of 3D tensor with shape:
 Output shape
 A list of 3D tensor with shape:
(batch_size,1,embedding_size)
.
 A list of 3D tensor with shape:
 Arguments
 reduction_ratio : Positive integer, dimensionality of the
attention network output space. seed : A Python integer to use as random seed.
 References
 [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for ClickThrough Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf)

build
(input_shape)[source]¶ 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 statecreation step inbetween 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, 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=None)[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.
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
()[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.