deepctr.models.fnn module

Author:
Weichen Shen,wcshen1994@163.com
Reference:
[1] Zhang W, Du T, Wang J. Deep learning over multi-field categorical data[C]//European conference on information retrieval. Springer, Cham, 2016: 45-57.(https://arxiv.org/pdf/1601.02376.pdf)
deepctr.models.fnn.FNN(feature_dim_dict, embedding_size=8, hidden_size=(128, 128), l2_reg_embedding=1e-05, l2_reg_linear=1e-05, l2_reg_deep=0, init_std=0.0001, seed=1024, keep_prob=1, activation='relu', final_activation='sigmoid')[source]

Instantiates the Factorization-supported Neural Network architecture.

Parameters:
  • 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_linear – float. L2 regularizer strength applied to linear weight
  • 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'
Returns:

A Keras model instance.