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deepfmMLMMoE.py
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deepfmMLMMoE.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/8/22 17:24
# @Author : Daishijun
# @Site :
# @File : deepfmMLMMoE.py
# @Software : PyCharm
''' 1 ML-MMOE + 1 dense'''
import tensorflow as tf
from deepctr.inputs import input_from_feature_columns, get_linear_logit, build_input_features, combined_dnn_input
from deepctr.layers.core import PredictionLayer, DNN
from deepctr.layers.interaction import FM
from deepctr.layers.utils import concat_fun
from tensorflow.python.keras.layers import Dense
from tensorflow.python.keras.regularizers import l2
from mmoe_diffgating import MMoEdiffGate
from mmoe import MMoE
def DeepFM(linear_feature_columns, dnn_feature_columns, embedding_size=8, use_fm=True, only_dnn=False, dnn_hidden_units=(128, 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'):
"""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 embedding_size: positive integer,sparse feature embedding_size
: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
:return: A Keras model instance.
"""
## 为每个特征创建Input[1,]； feature == > {'feature1': Input[1,], ...}
features = build_input_features(linear_feature_columns + dnn_feature_columns)
## [Input1, Input2, ... ]
inputs_list = list(features.values())
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
embedding_size,
l2_reg_embedding, init_std,
seed)
## [feature_1对应的embedding层，下连接对应feature1的Input[1,]层,...], [feature_1对应的Input[1,]层,...]
linear_logit = get_linear_logit(features, linear_feature_columns, l2_reg=l2_reg_linear, init_std=init_std,
seed=seed, prefix='linear')
## 线性变换层，没有激活函数
fm_input = concat_fun(sparse_embedding_list, axis=1)
## 稀疏embedding层concate在一起
fm_logit = FM()(fm_input)
## FM的二次项部分输出，不包含一次项和bias
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
# dnn_out = Dense(128, dnn_activation, l2_reg_dnn, dnn_dropout,
# dnn_use_bn, seed)(dnn_input)
# dnn_out = DNN((dnn_hidden_units[0],), dnn_activation, l2_reg_dnn, dnn_dropout,
# dnn_use_bn, seed)(dnn_input)
mmoe_out = MMoE(units=16, num_experts=8, num_tasks=8)(dnn_input)
mmoe_cat_layer = concat_fun(mmoe_out)
mmoe_high_layers = MMoEdiffGate(units=16, num_experts=8, num_tasks=2)([mmoe_cat_layer, dnn_input])
finish_in, like_in = mmoe_high_layers
finish_out_1 = Dense(128, dnn_activation, kernel_regularizer=l2(l2_reg_dnn))(finish_in)
finish_out = Dense(128, dnn_activation, kernel_regularizer=l2(l2_reg_dnn))(finish_out_1)
finish_logit = tf.keras.layers.Dense(1,use_bias=False, activation=None )(finish_out)
like_out_1 = Dense(128, dnn_activation, kernel_regularizer=l2(l2_reg_dnn))(like_in)
like_out = Dense(128, dnn_activation, kernel_regularizer=l2(l2_reg_dnn))(like_out_1)
like_logit = tf.keras.layers.Dense(1, use_bias=False, activation=None)(like_out)
# dnn_logit = tf.keras.layers.Dense(
# 1, use_bias=False, activation=None)(dnn_out)
# if len(dnn_hidden_units) > 0 and only_dnn == True:
# final_logit = dnn_logit
# elif len(dnn_hidden_units) == 0 and use_fm == False: # only linear
# final_logit = linear_logit
# elif len(dnn_hidden_units) == 0 and use_fm == True: # linear + FM
# final_logit = tf.keras.layers.add([linear_logit, fm_logit])
# elif len(dnn_hidden_units) > 0 and use_fm == False: # linear + Deep
# final_logit = tf.keras.layers.add([linear_logit, dnn_logit])
# elif len(dnn_hidden_units) > 0 and use_fm == True: # linear + FM + Deep
# final_logit = tf.keras.layers.add([linear_logit, fm_logit, dnn_logit])
# else:
# raise NotImplementedError
finish_logit = tf.keras.layers.add([linear_logit, fm_logit, finish_logit])
like_logit = tf.keras.layers.add([linear_logit, fm_logit, like_logit])
output_finish = PredictionLayer('binary', name='finish_output')(finish_logit)
output_like = PredictionLayer('binary', name='like_output')(like_logit)
model = tf.keras.models.Model(inputs=inputs_list, outputs=[output_finish, output_like])
return model