# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,wcshen1994@163.com
Reference:
[1] 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)
"""
import tensorflow as tf
from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns
from ..layers.core import PredictionLayer, DNN
from ..layers.interaction import CrossNet
from ..layers.utils import add_func, combined_dnn_input
[docs]def DCN(linear_feature_columns, dnn_feature_columns, cross_num=2, dnn_hidden_units=(128, 128,), l2_reg_linear=1e-5,
l2_reg_embedding=1e-5,
l2_reg_cross=1e-5, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_use_bn=False,
dnn_activation='relu', task='binary'):
"""Instantiates the Deep&Cross 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 cross_num: positive integet,cross layer number
: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_embedding: float. L2 regularizer strength applied to embedding vector
:param l2_reg_cross: float. L2 regularizer strength applied to cross net
:param l2_reg_dnn: float. L2 regularizer strength applied to DNN
: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_use_bn: bool. Whether use BatchNormalization before activation or not DNN
:param dnn_activation: Activation function to use in DNN
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
if len(dnn_hidden_units) == 0 and cross_num == 0:
raise ValueError("Either hidden_layer or cross layer must > 0")
features = build_input_features(dnn_feature_columns)
inputs_list = list(features.values())
linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear',
l2_reg=l2_reg_linear)
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding, seed)
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
if len(dnn_hidden_units) > 0 and cross_num > 0: # Deep & Cross
deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
dnn_use_bn, seed)(dnn_input)
cross_out = CrossNet(cross_num, l2_reg=l2_reg_cross)(dnn_input)
stack_out = tf.keras.layers.Concatenate()([cross_out, deep_out])
final_logit = tf.keras.layers.Dense(
1, use_bias=False, activation=None)(stack_out)
elif len(dnn_hidden_units) > 0: # Only Deep
deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
dnn_use_bn, seed)(dnn_input)
final_logit = tf.keras.layers.Dense(
1, use_bias=False, activation=None)(deep_out)
elif cross_num > 0: # Only Cross
cross_out = CrossNet(cross_num, l2_reg=l2_reg_cross)(dnn_input)
final_logit = tf.keras.layers.Dense(
1, use_bias=False, activation=None)(cross_out)
else: # Error
raise NotImplementedError
final_logit = add_func([final_logit, linear_logit])
output = PredictionLayer(task)(final_logit)
model = tf.keras.models.Model(inputs=inputs_list, outputs=output)
return model