# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,wcshen1994@163.com
Reference:
[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.(https://arxiv.org/abs/1810.11921)
"""
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