Source code for deepctr.estimator.models.pnn

# -*- coding:utf-8 -*-
"""
Author:
    Weichen Shen,wcshen1994@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)
"""

import tensorflow as tf

from ..feature_column import get_linear_logit, input_from_feature_columns
from ..utils import deepctr_model_fn, DNN_SCOPE_NAME, variable_scope
from ...layers.core import DNN
from ...layers.interaction import InnerProductLayer, OutterProductLayer
from ...layers.utils import concat_func, combined_dnn_input


[docs]def PNNEstimator(dnn_feature_columns, dnn_hidden_units=(128, 128), l2_reg_embedding=1e-5, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_activation='relu', use_inner=True, use_outter=False, kernel_type='mat', task='binary', model_dir=None, config=None, linear_optimizer='Ftrl', dnn_optimizer='Adagrad', training_chief_hooks=None): """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 :param model_dir: Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. :param config: tf.RunConfig object to configure the runtime settings. :param linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to the linear part of the model. Defaults to FTRL optimizer. :param dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to the deep part of the model. Defaults to Adagrad optimizer. :param training_chief_hooks: Iterable of `tf.train.SessionRunHook` objects to run on the chief worker during training. :return: A Tensorflow Estimator instance. """ if kernel_type not in ['mat', 'vec', 'num']: raise ValueError("kernel_type must be mat,vec or num") def _model_fn(features, labels, mode, config): train_flag = (mode == tf.estimator.ModeKeys.TRAIN) linear_logits = get_linear_logit(features, [], l2_reg_linear=0) with variable_scope(DNN_SCOPE_NAME): sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding=l2_reg_embedding) inner_product = tf.keras.layers.Flatten()( InnerProductLayer()(sparse_embedding_list)) outter_product = OutterProductLayer(kernel_type)(sparse_embedding_list) # ipnn deep input linear_signal = tf.keras.layers.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 = tf.keras.layers.Concatenate()( [linear_signal, inner_product, outter_product]) elif use_inner: deep_input = tf.keras.layers.Concatenate()( [linear_signal, inner_product]) elif use_outter: deep_input = tf.keras.layers.Concatenate()( [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)(dnn_input, training=train_flag) dnn_logit = tf.keras.layers.Dense( 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(dnn_out) logits = linear_logits + dnn_logit return deepctr_model_fn(features, mode, logits, labels, task, linear_optimizer, dnn_optimizer, training_chief_hooks=training_chief_hooks) return tf.estimator.Estimator(_model_fn, model_dir=model_dir, config=config)