deepctr.estimator.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.estimator.models.pnn.PNNEstimator(dnn_feature_columns, dnn_hidden_units=(128, 128), 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', model_dir=None, config=None, linear_optimizer='Ftrl', dnn_optimizer='Adagrad', training_chief_hooks=None)[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
  • model_dir – Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
  • config – tf.RunConfig object to configure the runtime settings.
  • linear_optimizer – An instance of tf.Optimizer used to apply gradients to the linear part of the model. Defaults to FTRL optimizer.
  • dnn_optimizer – An instance of tf.Optimizer used to apply gradients to the deep part of the model. Defaults to Adagrad optimizer.
  • training_chief_hooks – Iterable of tf.train.SessionRunHook objects to run on the chief worker during training.
Returns:

A Tensorflow Estimator instance.