deepctr.models.pnn module

Weichen Shen,
[1] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(
deepctr.models.pnn.PNN(dnn_feature_columns, dnn_hidden_units=(256, 128, 64), l2_reg_embedding=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', use_inner=True, use_outter=False, kernel_type='mat', task='binary')[source]

Instantiates the Product-based Neural Network architecture.

  • 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 deep net
  • 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
  • use_inner – bool,whether use inner-product or not.
  • use_outter – bool,whether use outter-product or not.
  • kernel_type – str,kernel_type used in outter-product,can be 'mat' , 'vec' or 'num'
  • task – str, "binary" for binary logloss or "regression" for regression loss

A Keras model instance.