deepctr.models.nfm module¶
- Author:
- Weichen Shen, weichenswc@163.com
- Reference:
- [1] He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355-364. (https://arxiv.org/abs/1708.05027)
-
deepctr.models.nfm.
NFM
(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, bi_dropout=0, dnn_dropout=0, dnn_activation='relu', task='binary')[source]¶ Instantiates the Neural Factorization Machine 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 part.
- l2_reg_dnn – float . L2 regularizer strength applied to DNN
- seed – integer ,to use as random seed.
- biout_dropout – When not
None
, the probability we will drop out the output of BiInteractionPooling Layer. - dnn_dropout – float in [0,1), the probability we will drop out a given DNN coordinate.
- dnn_activation – Activation function to use in deep net
- task – str,
"binary"
for binary logloss or"regression"
for regression loss
Returns: A Keras model instance.