# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen, weichenswc@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 get_linear_logit, input_from_feature_columns
from ..utils import deepctr_model_fn, DNN_SCOPE_NAME, variable_scope
from ...layers.core import DNN
from ...layers.interaction import InteractingLayer
from ...layers.utils import concat_func, combined_dnn_input
[docs]def AutoIntEstimator(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', model_dir=None, config=None, linear_optimizer='Ftrl',
dnn_optimizer='Adagrad', training_chief_hooks=None):
"""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
:param 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.
:param config: tf.RunConfig object to configure the runtime settings.
:param linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the linear part of the model. Defaults to FTRL optimizer.
:param dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the deep part of the model. Defaults to Adagrad optimizer.
:param training_chief_hooks: Iterable of `tf.train.SessionRunHook` objects to
run on the chief worker during training.
:return: A Tensorflow Estimator instance.
"""
def _model_fn(features, labels, mode, config):
train_flag = (mode == tf.estimator.ModeKeys.TRAIN)
linear_logits = get_linear_logit(features, linear_feature_columns, l2_reg_linear=l2_reg_linear)
with variable_scope(DNN_SCOPE_NAME):
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding=l2_reg_embedding)
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=seed)(dnn_input, training=train_flag)
stack_out = tf.keras.layers.Concatenate()([att_output, deep_out])
final_logit = tf.keras.layers.Dense(
1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(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=seed)(dnn_input, training=train_flag)
final_logit = tf.keras.layers.Dense(
1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(deep_out)
elif att_layer_num > 0: # Only Interacting Layer
final_logit = tf.keras.layers.Dense(
1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(att_output)
else: # Error
raise NotImplementedError
logits = linear_logits + final_logit
return deepctr_model_fn(features, mode, logits, labels, task, linear_optimizer, dnn_optimizer,
training_chief_hooks=training_chief_hooks)
return tf.estimator.Estimator(_model_fn, model_dir=model_dir, config=config)