# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen, weichenswc@163.com
Reference:
[1] He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355-364. (https://arxiv.org/abs/1708.05027)
"""
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 BiInteractionPooling
from ..layers.utils import concat_func, add_func, combined_dnn_input
[docs]def NFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128),
l2_reg_embedding=1e-5, l2_reg_linear=1e-5, l2_reg_dnn=0, seed=1024, bi_dropout=0,
dnn_dropout=0, dnn_activation='relu', task='binary'):
"""Instantiates the Neural Factorization Machine 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 part.
:param l2_reg_dnn: float . L2 regularizer strength applied to DNN
:param seed: integer ,to use as random seed.
:param biout_dropout: When not ``None``, the probability we will drop out the output of BiInteractionPooling Layer.
: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 deep net
: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)
fm_input = concat_func(sparse_embedding_list, axis=1)
bi_out = BiInteractionPooling()(fm_input)
if bi_dropout:
bi_out = tf.keras.layers.Dropout(bi_dropout)(bi_out, training=None)
dnn_input = combined_dnn_input([bi_out], dense_value_list)
dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, False, seed=seed)(dnn_input)
dnn_logit = tf.keras.layers.Dense(
1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(dnn_output)
final_logit = add_func([linear_logit, dnn_logit])
output = PredictionLayer(task)(final_logit)
model = tf.keras.models.Model(inputs=inputs_list, outputs=output)
return model