Source code for deepctr.models.pnn

# -*- coding:utf-8 -*-
    Weichen Shen,

    [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.(

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