--- title: DCN keywords: fastai sidebar: home_sidebar summary: "A pytorch implementation of Deep & Cross Network." description: "A pytorch implementation of Deep & Cross Network." nb_path: "nbs/models/models.dcn.ipynb" ---
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v1

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class CrossNetwork[source]

CrossNetwork(input_dim, num_layers) :: Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

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class DCN[source]

DCN(field_dims, embed_dim, num_layers, mlp_dims, dropout) :: Module

A pytorch implementation of Deep & Cross Network. Reference: R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017.

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v2

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class DNN[source]

DNN(inputs_dim, hidden_units, dropout_rate) :: Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

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class CrossNet[source]

CrossNet(in_features, layer_num=2, parameterization='vector', seed=2022) :: Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

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class DCNv2[source]

DCNv2(feat_size, embedding_size, linear_feature_columns, dnn_feature_columns, cross_num=2, cross_param='vector', dnn_hidden_units=(128, 128), init_std=0.0001, seed=2022, l2_reg=1e-05, drop_rate=0.5) :: Module

DCN model implementation in pytorch

Reference:

1. https://github.com/huangjunheng/recommendation_model/blob/master/DCN/dcn.py
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