deepctr.models.multitask.sharedbottom module¶

Author:

Reference:

[1] Ruder S. An overview of multi-task learning in deep neural networks[J]. arXiv preprint arXiv:1706.05098, 2017.(https://arxiv.org/pdf/1706.05098.pdf)

deepctr.models.multitask.sharedbottom.SharedBottom(dnn_feature_columns, bottom_dnn_hidden_units=(256, 128), tower_dnn_hidden_units=(64, ), l2_reg_embedding=1e-05, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', dnn_use_bn=False, task_types=('binary', 'binary'), task_names=('ctr', 'ctcvr'))[source]¶

Instantiates the SharedBottom multi-task learning Network architecture.

Parameters:

dnn_feature_columns – An iterable containing all the features used by deep part of the model.
bottom_dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of shared bottom DNN.
tower_dnn_hidden_units – list,list of positive integer or empty list, the layer number and units in each layer of task-specific DNN.
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
dnn_use_bn – bool. Whether use BatchNormalization before activation or not in DNN
task_types – list of str, indicating the loss of each tasks, "binary" for binary logloss or "regression" for regression loss. e.g. [‘binary’, ‘regression’]
task_names – list of str, indicating the predict target of each tasks

Returns:

A Keras model instance.