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, 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
- 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.