# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen, weichenswc@163.com
Reference:
[1] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(https://arxiv.org/pdf/1611.00144.pdf)
"""
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Dense, Reshape, Flatten
from ..feature_column import build_input_features, input_from_feature_columns
from ..layers.core import PredictionLayer, DNN
from ..layers.interaction import InnerProductLayer, OutterProductLayer
from ..layers.utils import concat_func, combined_dnn_input
[docs]def PNN(dnn_feature_columns, dnn_hidden_units=(256, 128, 64), l2_reg_embedding=0.00001, l2_reg_dnn=0,
seed=1024, dnn_dropout=0, dnn_activation='relu', use_inner=True, use_outter=False, kernel_type='mat',
task='binary'):
"""Instantiates the Product-based Neural Network architecture.
: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_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 use_inner: bool,whether use inner-product or not.
:param use_outter: bool,whether use outter-product or not.
:param kernel_type: str,kernel_type used in outter-product,can be ``'mat'`` , ``'vec'`` or ``'num'``
:param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss
:return: A Keras model instance.
"""
if kernel_type not in ['mat', 'vec', 'num']:
raise ValueError("kernel_type must be mat,vec or num")
features = build_input_features(dnn_feature_columns)
inputs_list = list(features.values())
sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
l2_reg_embedding, seed)
inner_product = Flatten()(
InnerProductLayer()(sparse_embedding_list))
outter_product = OutterProductLayer(kernel_type)(sparse_embedding_list)
# ipnn deep input
linear_signal = Reshape(
[sum(map(lambda x: int(x.shape[-1]), sparse_embedding_list))])(concat_func(sparse_embedding_list))
if use_inner and use_outter:
deep_input = concat_func([linear_signal, inner_product, outter_product])
elif use_inner:
deep_input = concat_func([linear_signal, inner_product])
elif use_outter:
deep_input = concat_func([linear_signal, outter_product])
else:
deep_input = linear_signal
dnn_input = combined_dnn_input([deep_input], dense_value_list)
dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, False, seed=seed)(dnn_input)
dnn_logit = Dense(1, use_bias=False)(dnn_out)
output = PredictionLayer(task)(dnn_logit)
model = Model(inputs=inputs_list,
outputs=output)
return model