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(feature_dim_dict, embedding_size=8, hidden_size=(128, 128), l2_reg_embedding=1e-05, l2_reg_deep=0, init_std=0.0001, seed=1024, keep_prob=1, activation='relu', final_activation='sigmoid', use_inner=True, use_outter=False, kernel_type='mat')[source]

Instantiates the Product-based Neural Network architecture.

  • feature_dim_dict – dict,to indicate sparse field and dense field like {‘sparse’:{‘field_1’:4,’field_2’:3,’field_3’:2},’dense’:[‘field_4’,’field_5’]}
  • embedding_size – positive integer,sparse feature embedding_size
  • hidden_size – 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_deep – float. L2 regularizer strength applied to deep net
  • init_std – float,to use as the initialize std of embedding vector
  • seed – integer ,to use as random seed.
  • keep_prob – float in (0,1]. keep_prob used in deep net
  • activation – Activation function to use in deep net
  • final_activation – str,output activation,usually 'sigmoid' or 'linear'
  • 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'

A Keras model instance.