提出动机

结构

特征交叉池化层

特点

工程化结构

tf2实现

# coding:utf-8
# @Email: wangguisen@infinities.com.cn
# @Time: 2022/1/4 11:12 上午
# @File: ctr_NFM.py
'''
NFM
'''
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import Model
from tensorflow.keras import optimizers
from tensorflow.keras import metrics
from sklearn.model_selection import train_test_split
import yamlfrom tools import *class NFM(Model):def __init__(self, spare_feature_columns, dense_feature_columns, hidden_units, output_dim, activation, droup_out, w_reg):super(NFM, self).__init__()self.spare_feature_columns = spare_feature_columnsself.dense_feature_columns = dense_feature_columnsself.spare_shape = len(spare_feature_columns)self.w_reg = w_reg# embeddingself.embedding_layer = {'embed_layer{}'.format(i): layers.Embedding(feat['vocabulary_size'], feat['embed_dim'])for i, feat in enumerate(self.spare_feature_columns)}# dnnself.DNN = tf.keras.Sequential()for hidden in hidden_units:self.DNN.add(layers.Dense(hidden))self.DNN.add(layers.BatchNormalization())self.DNN.add(layers.Activation(activation))self.DNN.add(layers.Dropout(droup_out))self.DNN.add(layers.Dense(output_dim, activation=None))  # output_dim=1def build(self, input_shape):self.b = self.add_weight(name='b', shape=(1,), initializer=tf.zeros_initializer(), trainable=True, )self.w = self.add_weight(name='w', shape=(self.spare_shape, 1), initializer=tf.random_normal_initializer(), trainable=True, regularizer=tf.keras.regularizers.l2(self.w_reg))def call(self, inputs, training=None, mask=None):# dense_inputs: 数值特征，13维# sparse_inputs： 类别特征，26维dense_inputs, sparse_inputs = inputs[:, :13], inputs[:, 13:]# # LR part# linear_part = tf.matmul(sparse_inputs, self.w) + self.b  # (batchsize, 1)# embeddingsparse_embeds = [self.embedding_layer['embed_layer{}'.format(i)](sparse_inputs[:, i]) for i in range(sparse_inputs.shape[1])]  # listsparse_embed = tf.convert_to_tensor(sparse_embeds)   # (26, batchsize, embed_dim)sparse_embed = tf.transpose(sparse_embed, [1, 0, 2])  # (batchsize, 26, embed_dim)# Bi-Interaction Layerbi_layer = 0.5 * (tf.pow(tf.reduce_sum(sparse_embed, axis=1), 2) - tf.reduce_sum(tf.pow(sparse_embed, 2), axis=1))  # (batchsize, embed_dim)# bi + densex = tf.concat([dense_inputs, bi_layer], axis=1)  # (batchsize, embed_dim + 13)# hidden layerhidden_part = self.DNN(x)   # (batchsize, 1)# output = tf.nn.sigmoid(linear_part + hidden_part)output = tf.nn.sigmoid(hidden_part)return outputif __name__ == '__main__':with open('config.yaml', 'r') as f:config = yaml.Loader(f).get_data()data = pd.read_csv('../../data/criteo_sampled_data_OK.csv')data_X = data.iloc[:, 1:]data_y = data['label'].values# I1-I13：总共 13 列数值型特征# C1-C26：共有 26 列类别型特征dense_features = ['I' + str(i) for i in range(1, 14)]sparse_features = ['C' + str(i) for i in range(1, 27)]dense_feature_columns = [denseFeature(feat) for feat in dense_features]spare_feature_columns = [sparseFeature(feat, data_X[feat].nunique(), config['NFM']['embed_dim']) for feat insparse_features]tmp_X, test_X, tmp_y, test_y = train_test_split(data_X, data_y, test_size=0.2, random_state=42, stratify=data_y)train_X, val_X, train_y, val_y = train_test_split(tmp_X, tmp_y, test_size=0.1, random_state=42, stratify=tmp_y)model = NFM(spare_feature_columns=spare_feature_columns,dense_feature_columns=dense_feature_columns,hidden_units=config['NFM']['hidden_units'],output_dim=config['NFM']['output_dim'],activation=config['NFM']['activation'],droup_out=config['NFM']['droup_out'],w_reg=config['NFM']['w_reg'],)adam = optimizers.Adam(lr=config['train']['adam_lr'], beta_1=0.95, beta_2=0.96,decay=config['train']['adam_lr'] / config['train']['epochs'])model.compile(optimizer=adam,loss='binary_crossentropy',metrics=[metrics.AUC(), metrics.Precision(), metrics.Recall()])model.fit(train_X.values, train_y,validation_data=(val_X.values, val_y),batch_size=config['train']['batch_size'],epochs=config['train']['epochs'],verbose=1,)model.summary()

Epoch 1/3
216/216 [==============================] - 9s 40ms/step - loss: 303.4375 - auc: 0.5100 - precision: 0.2705 - recall: 0.2608 - val_loss: 6.0378 - val_auc: 0.5098 - val_precision: 0.3144 - val_recall: 0.0579
Epoch 2/3
216/216 [==============================] - 8s 38ms/step - loss: 6.3794 - auc: 0.5833 - precision: 0.3619 - recall: 0.3607 - val_loss: 1.0595 - val_auc: 0.6949 - val_precision: 0.4574 - val_recall: 0.4347
Epoch 3/3
216/216 [==============================] - 8s 39ms/step - loss: 3.1246 - auc: 0.6743 - precision: 0.4674 - recall: 0.4705 - val_loss: 2.2340 - val_auc: 0.6381 - val_precision: 0.6591 - val_recall: 0.1596

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CTR --- NFM论文阅读笔记，及tf2复现

文章目录

提出动机

结构

特征交叉池化层

特点

工程化结构

tf2实现

CTR --- NFM论文阅读笔记，及tf2复现相关推荐

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