deepctr.models.deepfm module¶
- Author:
- Weichen Shen, weichenswc@163.com
- Reference:
- [1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247)
-
deepctr.models.deepfm.
DeepFM
(linear_feature_columns, dnn_feature_columns, fm_group=('default_group', ), dnn_hidden_units=(256, 128, 64), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', dnn_use_bn=False, task='binary')[source]¶ Instantiates the DeepFM Network architecture.
Parameters: - linear_feature_columns – An iterable containing all the features used by the linear part of the model.
- dnn_feature_columns – An iterable containing all the features used by the deep part of the model.
- fm_group – list, group_name of features that will be used to do feature interactions.
- dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of DNN
- l2_reg_linear – float. L2 regularizer strength applied to linear part
- 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
- dnn_use_bn – bool. Whether use BatchNormalization before activation or not in DNN
- task – str,
"binary"
for binary logloss or"regression"
for regression loss
Returns: A Keras model instance.