Source code for deepctr.models.multitask.sharedbottom

    Mincai Lai,

    Weichen Shen,

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

from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense

from ...feature_column import build_input_features, input_from_feature_columns
from ...layers.core import PredictionLayer, DNN
from ...layers.utils import combined_dnn_input

[docs]def SharedBottom(dnn_feature_columns, bottom_dnn_hidden_units=(256, 128), tower_dnn_hidden_units=(64,), l2_reg_embedding=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', dnn_use_bn=False, task_types=('binary', 'binary'), task_names=('ctr', 'ctcvr')): """Instantiates the SharedBottom multi-task learning Network architecture. :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. :param bottom_dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of shared bottom DNN. :param tower_dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of task-specific DNN. :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param l2_reg_dnn: float. L2 regularizer strength applied to DNN :param seed: integer ,to use as random seed. :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param dnn_activation: Activation function to use in DNN :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN :param task_types: list of str, indicating the loss of each tasks, ``"binary"`` for binary logloss or ``"regression"`` for regression loss. e.g. ['binary', 'regression'] :param task_names: list of str, indicating the predict target of each tasks :return: A Keras model instance. """ num_tasks = len(task_names) if num_tasks <= 1: raise ValueError("num_tasks must be greater than 1") if len(task_types) != num_tasks: raise ValueError("num_tasks must be equal to the length of task_types") for task_type in task_types: if task_type not in ['binary', 'regression']: raise ValueError("task must be binary or regression, {} is illegal".format(task_type)) features = build_input_features(dnn_feature_columns) inputs_list = list(features.values()) sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, seed) dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list) shared_bottom_output = DNN(bottom_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)( dnn_input) tasks_output = [] for task_type, task_name in zip(task_types, task_names): tower_output = DNN(tower_dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed, name='tower_' + task_name)(shared_bottom_output) logit = Dense(1, use_bias=False)(tower_output) output = PredictionLayer(task_type, name=task_name)(logit) tasks_output.append(output) model = Model(inputs=inputs_list, outputs=tasks_output) return model