Source code for deepctr.models.autoint

# -*- coding:utf-8 -*-

    Weichen Shen,

    [1] Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.(


import tensorflow as tf

from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns
from ..layers.core import PredictionLayer, DNN
from ..layers.interaction import InteractingLayer
from ..layers.utils import concat_func, add_func, combined_dnn_input

[docs]def AutoInt(linear_feature_columns, dnn_feature_columns, att_layer_num=3, att_embedding_size=8, att_head_num=2, att_res=True, dnn_hidden_units=(256, 256), dnn_activation='relu', l2_reg_linear=1e-5, l2_reg_embedding=1e-5, l2_reg_dnn=0, dnn_use_bn=False, dnn_dropout=0, seed=1024, task='binary', ): """Instantiates the AutoInt Network architecture. :param linear_feature_columns: An iterable containing all the features used by linear part of the model. :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. :param att_layer_num: int.The InteractingLayer number to be used. :param att_embedding_size: int.The embedding size in multi-head self-attention network. :param att_head_num: int.The head number in multi-head self-attention network. :param att_res: bool.Whether or not use standard residual connections before output. :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN :param dnn_activation: Activation function to use in DNN :param l2_reg_linear: float. L2 regularizer strength applied to linear part :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param l2_reg_dnn: float. L2 regularizer strength applied to DNN :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param seed: integer ,to use as random seed. :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss :return: A Keras model instance. """ if len(dnn_hidden_units) <= 0 and att_layer_num <= 0: raise ValueError("Either hidden_layer or att_layer_num must > 0") features = build_input_features(dnn_feature_columns) inputs_list = list(features.values()) linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear', l2_reg=l2_reg_linear) sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, seed) att_input = concat_func(sparse_embedding_list, axis=1) for _ in range(att_layer_num): att_input = InteractingLayer( att_embedding_size, att_head_num, att_res)(att_input) att_output = tf.keras.layers.Flatten()(att_input) dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list) if len(dnn_hidden_units) > 0 and att_layer_num > 0: # Deep & Interacting Layer deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed)(dnn_input) stack_out = tf.keras.layers.Concatenate()([att_output, deep_out]) final_logit = tf.keras.layers.Dense( 1, use_bias=False, activation=None)(stack_out) elif len(dnn_hidden_units) > 0: # Only Deep deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed)(dnn_input, ) final_logit = tf.keras.layers.Dense( 1, use_bias=False, activation=None)(deep_out) elif att_layer_num > 0: # Only Interacting Layer final_logit = tf.keras.layers.Dense( 1, use_bias=False, activation=None)(att_output) else: # Error raise NotImplementedError final_logit = add_func([final_logit, linear_logit]) output = PredictionLayer(task)(final_logit) model = tf.keras.models.Model(inputs=inputs_list, outputs=output) return model