deepctr.models.edcn module¶
- Author:
- Yi He, heyi_jack@163.com
- Reference:
- [1] Chen, B., Wang, Y., Liu, et al. Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models. CIKM, 2021, October (https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf)
-
deepctr.models.edcn.
EDCN
(linear_feature_columns, dnn_feature_columns, cross_num=2, cross_parameterization='vector', bridge_type='concatenation', tau=1.0, l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_cross=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_use_bn=False, dnn_activation='relu', task='binary')[source]¶ Instantiates the Enhanced Deep&Cross 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.
- cross_num – positive integet,cross layer number
- cross_parameterization – str,
"vector"
or"matrix"
, how to parameterize the cross network. - bridge_type – The type of bridge interaction, one of
"pointwise_addition"
,"hadamard_product"
,"concatenation"
,"attention_pooling"
- tau – Positive float, the temperature coefficient to control distribution of field-wise gating unit
- l2_reg_linear – float. L2 regularizer strength applied to linear part
- l2_reg_embedding – float. L2 regularizer strength applied to embedding vector
- l2_reg_cross – float. L2 regularizer strength applied to cross net
- 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_use_bn – bool. Whether use BatchNormalization before activation or not DNN
- dnn_activation – Activation function to use in DNN
- task – str,
"binary"
for binary logloss or"regression"
for regression loss
Returns: A Keras model instance.