# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,weichenswc@163.com
Reference:
[1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247)
"""
import torch
import torch.nn as nn
from .basemodel import BaseModel
from ..inputs import combined_dnn_input
from ..layers import FM, DNN
[docs]class DeepFM(BaseModel):
"""Instantiates the DeepFM Network architecture.
:param linear_feature_columns: An iterable containing all the features used by linear part of the model.
:param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
:param use_fm: bool,use FM part or not
:param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN
:param l2_reg_linear: float. L2 regularizer strength applied to linear part
:param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
:param l2_reg_dnn: float. L2 regularizer strength applied to DNN
:param init_std: float,to use as the initialize std of embedding vector
:param seed: integer ,to use as random seed.
:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
:param dnn_activation: Activation function to use in DNN
:param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:param device: str, ``"cpu"`` or ``"cuda:0"``
:param gpus: list of int or torch.device for multiple gpus. If None, run on `device`. `gpus[0]` should be the same gpu with `device`.
:return: A PyTorch model instance.
"""
def __init__(self,
linear_feature_columns, dnn_feature_columns, use_fm=True,
dnn_hidden_units=(256, 128),
l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0, init_std=0.0001, seed=1024,
dnn_dropout=0,
dnn_activation='relu', dnn_use_bn=False, task='binary', device='cpu', gpus=None):
super(DeepFM, self).__init__(linear_feature_columns, dnn_feature_columns, l2_reg_linear=l2_reg_linear,
l2_reg_embedding=l2_reg_embedding, init_std=init_std, seed=seed, task=task,
device=device, gpus=gpus)
self.use_fm = use_fm
self.use_dnn = len(dnn_feature_columns) > 0 and len(
dnn_hidden_units) > 0
if use_fm:
self.fm = FM()
if self.use_dnn:
self.dnn = DNN(self.compute_input_dim(dnn_feature_columns), dnn_hidden_units,
activation=dnn_activation, l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout, use_bn=dnn_use_bn,
init_std=init_std, device=device)
self.dnn_linear = nn.Linear(
dnn_hidden_units[-1], 1, bias=False).to(device)
self.add_regularization_weight(
filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2=l2_reg_dnn)
self.add_regularization_weight(self.dnn_linear.weight, l2=l2_reg_dnn)
self.to(device)
[docs] def forward(self, X):
sparse_embedding_list, dense_value_list = self.input_from_feature_columns(X, self.dnn_feature_columns,
self.embedding_dict)
logit = self.linear_model(X)
if self.use_fm and len(sparse_embedding_list) > 0:
fm_input = torch.cat(sparse_embedding_list, dim=1)
logit += self.fm(fm_input)
if self.use_dnn:
dnn_input = combined_dnn_input(
sparse_embedding_list, dense_value_list)
dnn_output = self.dnn(dnn_input)
dnn_logit = self.dnn_linear(dnn_output)
logit += dnn_logit
y_pred = self.out(logit)
return y_pred