deepctr.models.wdl module¶
- Author:
- Weichen Shen, weichenswc@163.com
- Reference:
- [1] Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.(https://arxiv.org/pdf/1606.07792.pdf)
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deepctr.models.wdl.
WDL
(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', task='binary')[source]¶ Instantiates the Wide&Deep Learning 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.
- 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 wide part
- l2_reg_embedding – float. L2 regularizer strength applied to embedding vector
- 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_activation – Activation function to use in DNN
- task – str,
"binary"
for binary logloss or"regression"
for regression loss
Returns: A Keras model instance.