Source code for deepctr.models.dcn

# -*- coding:utf-8 -*-
"""
Author:
    Weichen Shen, weichenswc@163.com

    Shuxun Zan, zanshuxun@aliyun.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)

    [2] Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020. (https://arxiv.org/abs/2008.13535)
"""
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Concatenate

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, cross_parameterization='vector', dnn_hidden_units=(256, 128, 64), 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 cross_parameterization: str, ``"vector"`` or ``"matrix"``, how to parameterize the cross network. :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_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=seed)(dnn_input) cross_out = CrossNet(cross_num, parameterization=cross_parameterization, l2_reg=l2_reg_cross)(dnn_input) stack_out = Concatenate()([cross_out, deep_out]) final_logit = Dense(1, use_bias=False)(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=seed)(dnn_input) final_logit = Dense(1, use_bias=False)(deep_out) elif cross_num > 0: # Only Cross cross_out = CrossNet(cross_num, parameterization=cross_parameterization, l2_reg=l2_reg_cross)(dnn_input) final_logit = Dense(1, use_bias=False)(cross_out) else: # Error raise NotImplementedError final_logit = add_func([final_logit, linear_logit]) output = PredictionLayer(task)(final_logit) model = Model(inputs=inputs_list, outputs=output) return model