100、TensorFlow实现FFM Field-awared FM模型

'''

Created on 2017年11月15日

@author: weizhen

'''

import tensorflow as tf

import pandas as pd

import numpy as np

import os

input_x_size = 80;

field_size = 8;

vector_dimension = 3;

total_plan_train_steps = 1000;

MODEL_SAVE_PATH = "TFModel"

MODEL_NAME = "FFM"

BATCH_SIZE = 1;

def createTwoDimensionWeight(input_x_size,

                   field_size,

                   vector_dimension):

    weights = tf.truncated_normal([int(input_x_size * (input_x_size + 1) / 2),

                                   field_size,

                                   vector_dimension

                                   ])

    tf_weights = tf.Variable(weights);

    return tf_weights;

def createOneDimensionWeight(input_x_size):

    weights = tf.truncated_normal([input_x_size])

    tf_weights = tf.Variable(weights)

    return tf_weights;

def createZeroDimensionWeight():

    weights = tf.truncated_normal([1])

    tf_weights = tf.Variable(weights)

    return tf_weights;

def inference(input_x, input_x_field):

    """计算回归模型输出的值"""

    zeroWeights = createZeroDimensionWeight();  # 随机初始化常数项的权重

    oneDimWeights = createOneDimensionWeight(input_x_size);  # 随机初始化一次项的权重

    secondValue = tf.reduce_sum(tf.multiply(oneDimWeights, input_x, name="secondVale"));  # 计算一次项的权重和x的点积，和点积后的和

    firstTwoValue = tf.add(zeroWeights, secondValue, name="firstTwoValue");  # 常数项和一次项的值

    thirdWeight = createTwoDimensionWeight(input_x_size,  # 创建二次项的权重变量

                                           field_size,

                                           vector_dimension);

    thirdValue = tf.Variable(0.0, dtype=tf.float32);  # 初始化二次项的和为0

    input_shape = input_x_size;  # 得到输入训练数据的大小

    for i in range(input_shape):

        featureIndex1 = i;  # 第一个特征的索引编号

        fieldIndex1 = int(input_x_field[i]);  # 第一个特征所在域的索引编号

        for j in range(i + 1, input_shape):

            featureIndex2 = j;  # 第二个特征的索引编号

            fieldIndex2 = int(input_x_field[j]);  # 第二个特征的所在域索引编号

            vectorLeft = tf.convert_to_tensor([[featureIndex1, fieldIndex2, 0], [featureIndex1, fieldIndex2, 1], [featureIndex1, fieldIndex2, 2]])

            weightLeft = tf.gather_nd(thirdWeight, vectorLeft)

            weightLeftAfterCut = tf.squeeze(weightLeft)

            vectorRight = tf.convert_to_tensor([[featureIndex2, fieldIndex1, 0], [featureIndex2, fieldIndex1, 1], [featureIndex2, fieldIndex1, 2]])

            weightRight = tf.gather_nd(thirdWeight, vectorRight)

            weightRightAfterCut = tf.squeeze(weightRight)

            tempValue = tf.reduce_sum(tf.multiply(weightLeftAfterCut, weightRightAfterCut))

            indices2 = [i]

            indices3 = [j]

            xi = tf.squeeze(tf.gather_nd(input_x, indices2));

            xj = tf.squeeze(tf.gather_nd(input_x, indices3));

            product = tf.reduce_sum(tf.multiply(xi, xj));

            secondItemVal = tf.multiply(tempValue, product)

            tf.assign(thirdValue, tf.add(thirdValue, secondItemVal))

    fowardY = tf.add(firstTwoValue, thirdValue)

    return fowardY;

def read_csv():

    f = open('train_sales_data.csv')

    df = pd.read_csv(f)

    y = np.array(df['UNIT_SALES'])

    x1 = np.array(df['ITEM_NBR'])

    x2 = np.array(df['STORE_NBR'])

    x3 = np.array(df['CITY_GUAYAQUIL'])

    x4 = np.array(df['CITY_BABAHOY'])

    x5 = np.array(df['CITY_PLAYAS'])

    x6 = np.array(df['CITY_LOJA'])

    x7 = np.array(df['CITY_EL_CARMEN'])

    x8 = np.array(df['CITY_LATACUNGA'])

    x9 = np.array(df['CITY_GUARAN'])

    x10 = np.array(df['CITY_CUENC'])

    x11 = np.array(df['CITY_ESMERALDAS'])

    x12 = np.array(df['CITY_QUITO'])

    x13 = np.array(df['CITY_CAYAMBE'])

    x14 = np.array(df['CITY_SALINAS'])

    x15 = np.array(df['CITY_RIOBAMBA'])

    x16 = np.array(df['CITY_SANTO_DOMINGO'])

    x17 = np.array(df['CITY_DAULE'])

    x18 = np.array(df['CITY_MACHALA'])

    x19 = np.array(df['CITY_MACHALA_1'])

    x20 = np.array(df['CITY_QUEVEDO'])

    x21 = np.array(df['STATE_AZUAY'])

    x22 = np.array(df['STATE_BOLIVAR'])

    x23 = np.array(df['STATE_CHIMBORAZO'])

    x24 = np.array(df['STATE_COTOPAXI'])

    x25 = np.array(df['STATE_EL_ORO'])

    x26 = np.array(df['STATE_ESMERALDAS'])

    x27 = np.array(df['STATE_GUAYAS'])

    x28 = np.array(df['STATE_IMBABURA'])

    x29 = np.array(df['STATE_LOJA'])

    x30 = np.array(df['STATE_LOS_RIOS'])

    x31 = np.array(df['STATE_MANABI'])

    x32 = np.array(df['STATE_PICHINCHA'])

    x33 = np.array(df['STATE_SANTA_ELENA'])

    x34 = np.array(df['STATE_SANTO_DOMINGO_DE_LOS'])

    x35 = np.array(df['STATE_TUNGURAHUA'])

    x36 = np.array(df['N_CLUSTER_1'])

    x37 = np.array(df['N_CLUSTER_2'])

    x38 = np.array(df['N_CLUSTER_3'])

    x39 = np.array(df['N_CLUSTER_4'])

    x40 = np.array(df['N_CLUSTER_5'])

    x41 = np.array(df['N_CLUSTER_6'])

    x42 = np.array(df['N_CLUSTER_7'])

    x43 = np.array(df['N_CLUSTER_8'])

    x44 = np.array(df['N_CLUSTER_9'])

    x45 = np.array(df['N_CLUSTER_10'])

    x46 = np.array(df['N_CLUSTER_11'])

    x47 = np.array(df['N_CLUSTER_12'])

    x48 = np.array(df['N_CLUSTER_13'])

    x49 = np.array(df['N_CLUSTER_14'])

    x50 = np.array(df['N_CLUSTER_15'])

    x51 = np.array(df['N_CLUSTER_16'])

    x52 = np.array(df['N_CLUSTER_17'])

    x53 = np.array(df['FAMILY_CLEANING'])

    x54 = np.array(df['FAMILY_BREAD_BAKERY'])

    x55 = np.array(df['FAMILY_LIQUOR_WINE_BEER'])

    x56 = np.array(df['FAMILY_PREPARED_FOODS'])

    x57 = np.array(df['FAMILY_MEATS'])

    x58 = np.array(df['FAMILY_BEAUTY'])

    x59 = np.array(df['FAMILY_HARDWARE'])

    x60 = np.array(df['FAMILY_BEVERAGES'])

    x61 = np.array(df['FAMILY_DAIRY'])

    x62 = np.array(df['FAMILY_GROCERY_II'])

    x63 = np.array(df['FAMILY_POULTRY'])

    x64 = np.array(df['FAMILY_SEAFOOD'])

    x65 = np.array(df['FAMILY_LAWN_AND_GARDEN'])

    x66 = np.array(df['FAMILY_EGGS'])

    x67 = np.array(df['FAMILY_DELI'])

    x68 = np.array(df['FAMILY_LINGERIE'])

    x69 = np.array(df['FAMILY_FROZEN_FOODS'])

    x70 = np.array(df['FAMILY_AUTOMOTIVE'])

    x71 = np.array(df['FAMILY_GROCERY_I'])

    x72 = np.array(df['FAMILY_PERSONAL_CARE'])

    x73 = np.array(df['PERISHABLE_TRUE'])

    x74 = np.array(df['TYPE_HOLIDAY'])

    x75 = np.array(df['TYPE_WORK_DAY'])

    x76 = np.array(df['LOCALE_NATIONAL'])

    x77 = np.array(df['LOCALE_NAME_ECUADOR'])

    x78 = np.array(df['LOCALE_PRIMER_DIA_DEL_ANO'])

    x79 = np.array(df['LOCALE_RECUPERO_PUENTE_NAVIDAD'])

    x80 = np.array(df['LOCALE_RECUPERO_PUENTE'])

    x81 = np.array(df["FIELD_CATEGORY"])

    train_x, train_y, train_x_field = [], [], []

    for j in range(80):

        train_x_field.append(x81[j])

        print(x81[j])

    for i in range(y.shape[0]):

        train_x_temp = []

        train_y_temp = []

        train_x_temp.append(x1[i])

        train_x_temp.append(x2[i])

        train_x_temp.append(x3[i])

        train_x_temp.append(x4[i])

        train_x_temp.append(x5[i])

        train_x_temp.append(x6[i])

        train_x_temp.append(x7[i])

        train_x_temp.append(x8[i])

        train_x_temp.append(x9[i])

        train_x_temp.append(x10[i])

        train_x_temp.append(x11[i])

        train_x_temp.append(x12[i])

        train_x_temp.append(x13[i])

        train_x_temp.append(x14[i])

        train_x_temp.append(x15[i])

        train_x_temp.append(x16[i])

        train_x_temp.append(x17[i])

        train_x_temp.append(x18[i])

        train_x_temp.append(x19[i])

        train_x_temp.append(x20[i])

        train_x_temp.append(x21[i])

        train_x_temp.append(x22[i])

        train_x_temp.append(x23[i])

        train_x_temp.append(x24[i])

        train_x_temp.append(x25[i])

        train_x_temp.append(x26[i])

        train_x_temp.append(x27[i])

        train_x_temp.append(x28[i])

        train_x_temp.append(x29[i])

        train_x_temp.append(x30[i])

        train_x_temp.append(x31[i])

        train_x_temp.append(x32[i])

        train_x_temp.append(x33[i])

        train_x_temp.append(x34[i])

        train_x_temp.append(x35[i])

        train_x_temp.append(x36[i])

        train_x_temp.append(x37[i])

        train_x_temp.append(x38[i])

        train_x_temp.append(x39[i])

        train_x_temp.append(x40[i])

        train_x_temp.append(x41[i])

        train_x_temp.append(x42[i])

        train_x_temp.append(x43[i])

        train_x_temp.append(x44[i])

        train_x_temp.append(x45[i])

        train_x_temp.append(x46[i])

        train_x_temp.append(x47[i])

        train_x_temp.append(x48[i])

        train_x_temp.append(x49[i])

        train_x_temp.append(x50[i])

        train_x_temp.append(x51[i])

        train_x_temp.append(x52[i])

        train_x_temp.append(x53[i])

        train_x_temp.append(x54[i])

        train_x_temp.append(x55[i])

        train_x_temp.append(x56[i])

        train_x_temp.append(x57[i])

        train_x_temp.append(x58[i])

        train_x_temp.append(x59[i])

        train_x_temp.append(x60[i])

        train_x_temp.append(x61[i])

        train_x_temp.append(x62[i])

        train_x_temp.append(x63[i])

        train_x_temp.append(x64[i])

        train_x_temp.append(x65[i])

        train_x_temp.append(x66[i])

        train_x_temp.append(x67[i])

        train_x_temp.append(x68[i])

        train_x_temp.append(x69[i])

        train_x_temp.append(x70[i])

        train_x_temp.append(x71[i])

        train_x_temp.append(x72[i])

        train_x_temp.append(x73[i])

        train_x_temp.append(x74[i])

        train_x_temp.append(x75[i])

        train_x_temp.append(x76[i])

        train_x_temp.append(x77[i])

        train_x_temp.append(x78[i])

        train_x_temp.append(x79[i])

        train_x_temp.append(x80[i])

        train_y_temp.append(y[i])

        train_x.append(train_x_temp);

        train_y.append(train_y_temp);

    f.close();

    return (train_x, train_y, train_x_field)

 if __name__ == "__main__":

     global_step = tf.Variable(0, trainable=False)

     (train_x, train_y, train_x_field) = read_csv();

     input_x = tf.placeholder(tf.float32, [None, 80])

     input_y = tf.placeholder(tf.float32, [None, 1])

     y_ = inference(input_x, train_x_field)

     cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=y_, labels=input_y);

     train_step = tf.train.GradientDescentOptimizer(0.001, name="GradientDescentOptimizer").minimize(cross_entropy, global_step=global_step);

     saver = tf.train.Saver();

     with tf.Session() as sess:

         tf.global_variables_initializer().run()

         for i in range(total_plan_train_steps):

             input_x_batch = train_x[int(i * BATCH_SIZE):int((i + 1) * BATCH_SIZE)]

             input_y_batch = train_y[int(i * BATCH_SIZE):int((i + 1) * BATCH_SIZE)]

             predict_loss , steps = sess.run([train_step, global_step], feed_dict={input_x:input_x_batch, input_y:input_y_batch})

             if (i + 1) % 2 == 0:

                 print("After  {step} training   step(s)   ,   loss    on    training    batch   is  {predict_loss} "

                        .format(step=steps, predict_loss=predict_loss))

                 saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=steps)

                 writer = tf.summary.FileWriter(os.path.join(MODEL_SAVE_PATH, MODEL_NAME), tf.get_default_graph())

                 writer.close()

100、TensorFlow实现FFM Field-awared FM模型的更多相关文章

field方法属于模型的连贯操作方法之一
field方法属于模型的连贯操作方法之一,主要目的是标识要返回或者操作的字段,可以用于查询和写入操作. 1.用于查询指定字段在查询操作中field方法是使用最频繁的. $Model->fie ...
三分钟快速上手TensorFlow 2.0 （下）——模型的部署、大规模训练、加速
前文:三分钟快速上手TensorFlow 2.0 (中)——常用模块和模型的部署 TensorFlow 模型导出使用 SavedModel 完整导出模型不仅包含参数的权值,还包含计算的流程(即计算 ...
Tensorflow Mask-RCNN训练识别箱子的模型运行结果（练习）
Tensorflow Mask-RCNN训练识别箱子的模型
聊聊推荐系统，FM模型效果好在哪里？
本文始发于公众号:Coder梁大家好,我们今天继续来聊聊推荐系统. 在上一回当中我们讨论了LR模型对于推荐系统的应用,以及它为什么适合推荐系统,并且对它的优点以及缺点进行了分析.最后我们得出了结论, ...
深度学习Tensorflow生产环境部署（下·模型部署篇）
前一篇讲过环境的部署篇,这一次就讲讲从代码角度如何导出pb模型,如何进行服务调用. 1 hello world篇部署完docker后,如果是cpu环境,可以直接拉取tensorflow/servin ...
TensorFlow使用记录 (九）：模型保存与恢复
模型文件 tensorflow 训练保存的模型注意包含两个部分:网络结构和参数值. .meta .meta 文件以 “protocol buffer”格式保存了整个模型的结构图,模型上定义的操作等信息 ...
ubuntu16.04 使用tensorflow object detection训练自己的模型
一.构建自己的数据集 1.格式必须为jpg.jpeg或png. 2.在models/research/object_detection文件夹下创建images文件夹,在images文件夹下创建trai ...
吴裕雄--天生自然TensorFlow高层封装：Estimator-自定义模型
# 1. 自定义模型并训练. import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist i ...
TensorFlow文本与序列的深度模型
TensorFlow深度学习笔记文本与序列的深度模型 Deep Models for Text and Sequence 转载请注明作者:梦里风林Github工程地址:https://github. ...

随机推荐

Docker问题方案收集
1.问题: Unable to connect to unix:///var/run/docker.sock (Permission denied) from PHP code 解决方法: Make ...
Mac上VMWare Fusion配置多台cent os
一.创建虚拟机(准备工作) 1.使用VMWare Fusion 创建第一台虚拟机 2.选择操作系统(本次使用的是使用cent os 6.5 64bit 系统) 3.选择磁盘大小(楼主mac上的磁盘大小 ...
117、TensorFlow变量共享
# sharing variables # Tensorflow supports two ways of sharing variables # 1.Explicitly passing tf.Va ...
/proc/interrupts /proc/stat 查看中断信息
/proc/interrupts列出当前所以系统注册的中断,记录中断号,中断发生次数,中断设备名称如下图:从左至右:中断号中断次数中断设备名称从上图可知中断号为19的arch_timer ...
测开之路五十七：实现runner和测试报告
准备测试用例 from fox.case import Casefrom src import Calculator class TestCalculator(Case): def setUp(sel ...
php如何获取服务器端的一些信息
<?php echo "服务器端的IP地址是:<br />"; echo $_SERVER['SERVER_ADDR']; echo "服务器端的端口号 ...
17. Jmeter-取样器一
jmeter-sampler介绍与使用 HTTP请求 Test Action Debug Sampler AJP/1.3 Sampler Access Log Sampler BeanShell Sa ...
Python3函数中特殊形参的使用：*、*args、**kwargs
Python3函数中特殊形参的使用:*.*args.**kwargs ==用法1:不定长参数== 当函数需要的参数数量不确定的时候,可以使用*args 和 **kwargs , 所有的位置参数保存在* ...
python列表之添加、修改和删除元素
修改列表中的元素: subject= ['math', 'Chinese', 'English'] subject[0] = 'history' # 列表名[要修改元素的下标]=修改后的元素 prin ...
Codeforces 497B Tennis Game( 枚举+ 二分)
B. Tennis Game time limit per test 2 seconds memory limit per test 256 megabytes input standard inpu ...

100、TensorFlow实现FFM Field-awared FM模型

100、TensorFlow实现FFM Field-awared FM模型的更多相关文章

随机推荐

热门专题