# -*- coding:utf-8 -*-
"""
Author:
Tingyi Tan, 5636374@qq.com
Reference:
[1] Chen W, Zhan L, Ci Y, Lin C. FLEN: Leveraging Field for Scalable CTR Prediction . arXiv preprint arXiv:1911.04690, 2019.(https://arxiv.org/pdf/1911.04690)
"""
from itertools import chain
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense
from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns
from ..layers.core import PredictionLayer, DNN
from ..layers.interaction import FieldWiseBiInteraction
from ..layers.utils import concat_func, add_func, combined_dnn_input
[docs]def FLEN(linear_feature_columns,
dnn_feature_columns,
dnn_hidden_units=(256, 128, 64),
l2_reg_linear=0.00001,
l2_reg_embedding=0.00001,
l2_reg_dnn=0,
seed=1024,
dnn_dropout=0.0,
dnn_activation='relu',
dnn_use_bn=False,
task='binary'):
"""Instantiates the FLEN 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 dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net
: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 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: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
features = build_input_features(linear_feature_columns +
dnn_feature_columns)
inputs_list = list(features.values())
group_embedding_dict, dense_value_list = input_from_feature_columns(
features,
dnn_feature_columns,
l2_reg_embedding,
seed,
support_group=True)
linear_logit = get_linear_logit(features,
linear_feature_columns,
seed=seed,
prefix='linear',
l2_reg=l2_reg_linear)
fm_mf_out = FieldWiseBiInteraction(seed=seed)(
[concat_func(v, axis=1) for k, v in group_embedding_dict.items()])
dnn_input = combined_dnn_input(
list(chain.from_iterable(group_embedding_dict.values())),
dense_value_list)
dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input)
dnn_logit = Dense(1, use_bias=False)(concat_func([fm_mf_out, dnn_output]))
final_logit = add_func([linear_logit, dnn_logit])
output = PredictionLayer(task)(final_logit)
model = Model(inputs=inputs_list, outputs=output)
return model