deepctr.models.flen module¶
- Author:
- Tingyi Tan, 5636374@qq.com
- Reference:
- [1] Chen W, Zhan L, Ci Y, Lin C. FLEN: Leveraging Field for Scalable CTR Prediction . arXiv preprint arXiv:1911.04690, 2019.(https://arxiv.org/pdf/1911.04690)
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deepctr.models.flen.
FLEN
(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 128, 64), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0.0, dnn_activation='relu', dnn_use_bn=False, task='binary')[source]¶ Instantiates the FLEN 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_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.