deepctr.models.dcn module¶

Author:

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)

deepctr.models.dcn.DCN(linear_feature_columns, dnn_feature_columns, cross_num=2, cross_parameterization='vector', dnn_hidden_units=(256, 128, 64), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_cross=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_use_bn=False, dnn_activation='relu', task='binary')[source]¶

Instantiates the Deep&Cross Network architecture.

Parameters

linear_feature_columns – An iterable containing all the features used by linear part of the model.
dnn_feature_columns – An iterable containing all the features used by deep part of the model.
cross_num – positive integet,cross layer number
cross_parameterization – str, "vector" or "matrix", how to parameterize the cross network.
dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of DNN
l2_reg_linear – float. L2 regularizer strength applied to linear part
l2_reg_embedding – float. L2 regularizer strength applied to embedding vector
l2_reg_cross – float. L2 regularizer strength applied to cross net
l2_reg_dnn – float. L2 regularizer strength applied to DNN
seed – integer ,to use as random seed.
dnn_dropout – float in [0,1), the probability we will drop out a given DNN coordinate.
dnn_use_bn – bool. Whether use BatchNormalization before activation or not DNN
dnn_activation – Activation function to use in DNN
task – str, "binary" for binary logloss or "regression" for regression loss

Returns

A Keras model instance.