# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,wcshen1994@163.com
Reference:
[1] Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019.
"""
import tensorflow as tf
from tensorflow.python.keras.layers import Dense, Flatten
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 SENETLayer, BilinearInteraction
from ...layers.utils import concat_func, combined_dnn_input
[docs]def FiBiNETEstimator(linear_feature_columns, dnn_feature_columns, bilinear_type='interaction', reduction_ratio=3,
dnn_hidden_units=(128, 128), l2_reg_linear=1e-5,
l2_reg_embedding=1e-5, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu',
task='binary', model_dir=None, config=None, linear_optimizer='Ftrl',
dnn_optimizer='Adagrad', training_chief_hooks=None):
"""Instantiates the Feature Importance and Bilinear feature Interaction 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 bilinear_type: str,bilinear function type used in Bilinear Interaction Layer,can be ``'all'`` , ``'each'`` or ``'interaction'``
:param reduction_ratio: integer in [1,inf), reduction ratio used in SENET Layer
:param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN
:param l2_reg_linear: float. L2 regularizer strength applied to wide 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 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 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)
senet_embedding_list = SENETLayer(
reduction_ratio, seed)(sparse_embedding_list)
senet_bilinear_out = BilinearInteraction(
bilinear_type=bilinear_type, seed=seed)(senet_embedding_list)
bilinear_out = BilinearInteraction(
bilinear_type=bilinear_type, seed=seed)(sparse_embedding_list)
dnn_input = combined_dnn_input(
[Flatten()(concat_func([senet_bilinear_out, bilinear_out]))], dense_value_list)
dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
False, seed)(dnn_input, training=train_flag)
dnn_logit = Dense(
1, use_bias=False, activation=None)(dnn_out)
logits = linear_logits + dnn_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)