deepctr.models.ccpm module

Author:
Weichen Shen,wcshen1994@163.com
Reference:
[1] Liu Q, Yu F, Wu S, et al. A convolutional click prediction model[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 1743-1746. (http://ir.ia.ac.cn/bitstream/173211/12337/1/A%20Convolutional%20Click%20Prediction%20Model.pdf)
deepctr.models.ccpm.CCPM(linear_feature_columns, dnn_feature_columns, conv_kernel_width=(6, 5), conv_filters=(4, 4), dnn_hidden_units=(256, ), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_dnn=0, dnn_dropout=0, init_std=0.0001, seed=1024, task='binary')[source]

Instantiates the Convolutional Click Prediction Model 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.
  • conv_kernel_width – list,list of positive integer or empty list,the width of filter in each conv layer.
  • conv_filters – list,list of positive integer or empty list,the number of filters in each conv layer.
  • 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_dnn – float. L2 regularizer strength applied to DNN
  • dnn_dropout – float in [0,1), the probability we will drop out a given DNN coordinate.
  • init_std – float,to use as the initialize std of embedding vector
  • seed – integer ,to use as random seed.
  • task – str, "binary" for binary logloss or "regression" for regression loss
Returns:

A Keras model instance.