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fnn.py
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fnn.py
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# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen, weichenswc@163.com
Reference:
[1] Zhang W, Du T, Wang J. Deep learning over multi-field categorical data[C]//European conference on information retrieval. Springer, Cham, 2016: 45-57.(https://arxiv.org/pdf/1601.02376.pdf)
"""
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.utils import add_func, combined_dnn_input
def FNN(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(256, 128, 64),
l2_reg_embedding=1e-5, l2_reg_linear=1e-5, l2_reg_dnn=0, seed=1024, dnn_dropout=0,
dnn_activation='relu', task='binary'):
"""Instantiates the Factorization-supported Neural 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_embedding: float. L2 regularizer strength applied to embedding vector
:param l2_reg_linear: float. L2 regularizer strength applied to linear weight
: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
:return: A Keras model instance.
"""
features = build_input_features(
linear_feature_columns + 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)
dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, False, seed=seed)(dnn_input)
dnn_logit = Dense(1, use_bias=False)(deep_out)
final_logit = add_func([dnn_logit, linear_logit])
output = PredictionLayer(task)(final_logit)
model = Model(inputs=inputs_list, outputs=output)
return model