deepctr.models.dcn module

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)
deepctr.models.dcn.DCN(linear_feature_columns, dnn_feature_columns, cross_num=2, dnn_hidden_units=(128, 128), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_cross=1e-05, l2_reg_dnn=0, init_std=0.0001, 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
  • dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of DNN
  • 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
  • init_std – float,to use as the initialize std of embedding vector
  • 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.