deepctr.models.wdl module

Author:
Weichen Shen,wcshen1994@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)
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, init_std=0.0001, 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
  • 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_activation – Activation function to use in DNN
  • task – str, "binary" for binary logloss or "regression" for regression loss
Returns:

A Keras model instance.