deepctr.estimator.models.wdl module

Author:
Weichen Shen, weichenswc@163.com
Reference:
[1] Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.(https://arxiv.org/pdf/1606.07792.pdf)
deepctr.estimator.models.wdl.WDLEstimator(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 128, 64), 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 Wide&Deep Learning 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 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.