deepctr.estimator.models.deepfefm module

Author:

Harshit Pande

Reference:

[1] Field-Embedded Factorization Machines for Click-through Rate Prediction] (https://arxiv.org/abs/2009.09931)

deepctr.estimator.models.deepfefm.DeepFEFMEstimator(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 128, 64), l2_reg_linear=1e-05, l2_reg_embedding_feat=1e-05, l2_reg_embedding_field=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0.0, dnn_activation='relu', dnn_use_bn=False, task='binary', model_dir=None, config=None, linear_optimizer='Ftrl', dnn_optimizer='Adagrad', training_chief_hooks=None)[source]

Instantiates the DeepFEFM Network architecture or the shallow FEFM architecture (Ablation support not provided as estimator is meant for production, Ablation support provided in DeepFEFM implementation in models

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 linear part

  • l2_reg_embedding_feat – float. L2 regularizer strength applied to embedding vector of features

  • l2_reg_embedding_field – float, L2 regularizer to field embeddings

  • 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

  • dnn_use_bn – bool. Whether use BatchNormalization before activation or not 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.