deepctr.estimator.models.fibinet module

Author:
Weichen Shen, weichenswc@163.com
Reference:
[1] Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019.
deepctr.estimator.models.fibinet.FiBiNETEstimator(linear_feature_columns, dnn_feature_columns, bilinear_type='interaction', reduction_ratio=3, dnn_hidden_units=(128, 128), l2_reg_linear=1e-05, l2_reg_embedding=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', task='binary', model_dir=None, config=None, linear_optimizer='Ftrl', dnn_optimizer='Adagrad', training_chief_hooks=None)[source]

Instantiates the Feature Importance and Bilinear feature Interaction 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.
  • bilinear_type – str,bilinear function type used in Bilinear Interaction Layer,can be 'all' , 'each' or 'interaction'
  • reduction_ratio – integer in [1,inf), reduction ratio used in SENET Layer
  • dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of DNN
  • l2_reg_linear – float. L2 regularizer strength applied to wide 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
  • 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.