deepctr.models.fnn module¶
- Author:
- Weichen Shen, weichenswc@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
(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 128, 64), l2_reg_embedding=1e-05, l2_reg_linear=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', task='binary')[source]¶ Instantiates the Factorization-supported Neural Network 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 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_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
- task – str,
"binary"
for binary logloss or"regression"
for regression loss
Returns: A Keras model instance.