deepctr.models.pnn module¶
- Author:
- Weichen Shen, weichenswc@163.com
- Reference:
- [1] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(https://arxiv.org/pdf/1611.00144.pdf)
-
deepctr.models.pnn.
PNN
(dnn_feature_columns, dnn_hidden_units=(256, 128, 64), l2_reg_embedding=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', use_inner=True, use_outter=False, kernel_type='mat', task='binary')[source]¶ Instantiates the Product-based Neural Network architecture.
Parameters: - 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_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
- use_inner – bool,whether use inner-product or not.
- use_outter – bool,whether use outter-product or not.
- kernel_type – str,kernel_type used in outter-product,can be
'mat'
,'vec'
or'num'
- task – str,
"binary"
for binary logloss or"regression"
for regression loss
Returns: A Keras model instance.