deepctr.models.onn module

Author:

Weichen Shen, weichenswc@163.com

Reference:

[1] Yang Y, Xu B, Shen F, et al. Operation-aware Neural Networks for User Response Prediction[J]. arXiv preprint arXiv:1904.12579, 2019. （https://arxiv.org/pdf/1904.12579）

deepctr.models.onn.ONN(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, dnn_dropout=0, seed=1024, use_bn=True, reduce_sum=False, task='binary')[source]

Instantiates the Operation-aware Neural Networks 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.

  • dnn_dropout – float in [0,1), the probability we will drop out a given DNN coordinate.

  • use_bn – bool,whether use bn after ffm out or not

  • reduce_sum – bool,whether apply reduce_sum on cross vector

  • task – str, "binary" for binary logloss or "regression" for regression loss

Returns

A Keras model instance.