R语言——实验4-人工神经网络

时间：2021-10-24 04:14:03

带包实现：

rm(list=ls())

setwd("C:/Users/Administrator/Desktop/R语言与数据挖掘作业/实验4-人工神经网络")

Data=read.csv("sales_data.csv")[,2:5]

library(nnet)

colnames(Data)<-c("x1","x2","x3","y")

model1=nnet(y~.,data=Data,size=6,decay=5e-4,maxit=1000)

pred=predict(model1,Data[,1:3],type="class")

(p=sum(as.numeric(pred==Data$y))/nrow(Data))

table(Data$y,pred)

prop.table(table(Data$y,pred),1)

2. 深入理解BP人工神经网络算法，并用R语言实现该算法

自己打出一个简单的神经网络

rm(list=ls())

#install.packages("sampling")

library(sampling)

setwd("C:/Users/Administrator/Desktop/R语言与数据挖掘作业/实验4-人工神经网络")

data("iris")

#as.numeric(data[,5])

#这里我们按照每种“Species”抽取3/5个样本进行抽样。

n=round(3/5*nrow(iris)/3)

sub_train=strata(iris,stratanames=("Species"),size=rep(n,3),method="srswor")

head(sub_train)

data_train=iris[sub_train$ID_unit,]

data_test=iris[-sub_train$ID_unit,]

dim(data_train)

dim(data_test)

#保存数据集

write.csv(data_train,"./iris_data_train.csv")

write.csv(data_test,"./iris_data_test.csv")

#对data_train归一化处理

#colnames()

data_train$Sepal.Length=(data_train$Sepal.Length-min(data_train$Sepal.Length))*1.0/

  (max(data_train$Sepal.Length)-min(data_train$Sepal.Length))

data_train$Sepal.Width=(data_train$Sepal.Width-min(data_train$Sepal.Width))*1.0/

  (max(data_train$Sepal.Width)-min(data_train$Sepal.Width))

data_train$Petal.Length=(data_train$Petal.Length-min(data_train$Petal.Length))*1.0/

  (max(data_train$Petal.Length)-min(data_train$Petal.Length))

data_train$Petal.Width=(data_train$Petal.Width-min(data_train$Petal.Width))*1.0/

  (max(data_train$Petal.Width)-min(data_train$Petal.Width))

#4个输入，5个的隐藏层，3个输出层

#第一块连接的地方需要4*5个w，5个a,第二块需要5*3个w,5个b

f<-function(x)

{

  x=1*1.0/(1+exp(-1*x))

  return(x)

}

#定义训练次数

global_time=100

#定义学习率

learning_rate=1.8

#随机生成第一块的w1

w1=matrix(sample((0:100)*1.0/100,size=20),4,5)

a1=matrix(sample((0:100)*1.0/100,size=5),1,5)

w2=matrix(sample((0:100)*1.0/100,size=15),5,3)

a2=matrix(sample((0:100)*1.0/100,size=3),1,3)

#遍历每一条数据，每扔进一条数据就调参数

for(time in 1:global_time)

{

  for(i in 1:length(data_train$Species))

  {

    #1*4

    x1=matrix(c(data_train$Sepal.Length[i],data_train$Sepal.Width[i],data_train$Petal.Length[i],data_train$Petal.Width[i]),1,4)

    #矩阵相乘 %*%

    #得到经过第一个隐藏层的输出，也就是最后输出层的输入

    x2=f(x1%*%w1-a1)

    #得到最后的输出层，是1*3的矩阵

    x3=f(x2%*%w2-a2)

    #把标签变成1*3的矩阵

    y=matrix(0.1,1,3)

    if(data_train$Species[i]=="setosa" ){y[1]=0.9}

    if(data_train$Species[i]=="versicolor"){y[2]=0.9}

    if(data_train$Species[i]=="virginica"){y[3]=0.9}

    #与标签比较调参,输出层的误差项为output*(1-output)*(y-output)

    #print(y)

    #print(data_train$Species[i])

    cha=x3*(1-x3)*(y-x3)

    #cat("loss",mean(cha))

    #print("")

    #更新隐藏层和输出层之间的w2,dw2=w2+learning_rate * cha * xi

    #通过x2(竖着),t-o(横着)相乘得到5*3的矩阵和w2相加来更新

    tx2=t(x2)

    dw2=learning_rate * (tx2 %*% cha)

    #把之前的w2存下来,之后更新要用

    before_w2=w2

    w2=w2+dw2

    #更新输入层和隐藏层之间的w1,dw1=w1+learning_rate * cha * xi

    #隐藏层的误差项不是直接得到的，需要通过后一层的误差项计算,为(和(cha1*wi))

    #隐藏层的误差项为 w2(5*3) %*% cha(3*1),的cha2(5*1),注:用的是每更新前的w2

    cha2 = before_w2 %*% t(cha)

    tx1=t(x1)

    dw1=learning_rate * (tx1 %*% t(cha2))

    w1=w1+dw1

  }

}

#看看拟合度

SUM=length(data_train$Species)

right=0

for(i in 1:length(data_train$Species))

{

  #1*4

  x1=matrix(c(data_train$Sepal.Length[i],data_train$Sepal.Width[i],data_train$Petal.Length[i],data_train$Petal.Width[i]),1,4)

  #矩阵相乘 %*%

  #得到经过第一个隐藏层的输出，也就是最后输出层的输入

  x2=f(x1%*%w1-a1)

  #得到最后的输出层，是1*3的矩阵

  x3=f(x2%*%w2-a2)

  print(x3)

  y1=matrix(c(0.9,0.1,0.1),1,3)

  y2=matrix(c(0.1,0.9,0.1),1,3)

  y3=matrix(c(0.1,0.1,0.9),1,3)

  # cha11=x3*(1-x3)*(y1-x3)

  # cha22=x3*(1-x3)*(y2-x3)

  # cha33=x3*(1-x3)*(y3-x3)

  cha11=(y1-x3)

  cha22=(y2-x3)

  cha33=(y3-x3)

  cha1=0

  cha2=0

  cha3=0

  for(j in 1:3)

  {

    cha1=cha1+abs(cha11[j])

    cha2=cha2+abs(cha22[j])

    cha3=cha3+abs(cha33[j])

  }

  micha=min(cha1,cha2,cha3)

  #cat("micha",micha,"\n")

  #cat("cha1",cha1,"\n")

  #cat("cha2",cha2,"\n")

  #cat("cha3",cha3,"\n")

  if(micha==cha1 & data_train$Species[i]=="setosa") {print(1)

    right=right+1}

  if(micha==cha2 & data_train$Species[i]=="versicolor"){print(2)

    right=right+1}

  if(micha==cha3 & data_train$Species[i]=="virginica") {print(3)

    right=right+1}

}

print("拟合度为：")

print((right*1.0/SUM))

print("sum")

print(SUM)

print("right")

print(right)

#训练结束,看看参数

print("w1")

print(w1)

print("w2")

print(w2)

data_test$Sepal.Length=(data_test$Sepal.Length-min(data_test$Sepal.Length))*1.0/

  (max(data_test$Sepal.Length)-min(data_test$Sepal.Length))

data_test$Sepal.Width=(data_test$Sepal.Width-min(data_test$Sepal.Width))*1.0/

  (max(data_test$Sepal.Width)-min(data_test$Sepal.Width))

data_test$Petal.Length=(data_test$Petal.Length-min(data_test$Petal.Length))*1.0/

  (max(data_test$Petal.Length)-min(data_test$Petal.Length))

data_test$Petal.Width=(data_test$Petal.Width-min(data_test$Petal.Width))*1.0/

  (max(data_test$Petal.Width)-min(data_test$Petal.Width))

#用测试数据测试一下准确率如何

SUM=length(data_test$Species)

right=0

for(i in 1:length(data_test$Species))

{

  #1*4

  x1=matrix(c(data_test$Sepal.Length[i],data_test$Sepal.Width[i],data_test$Petal.Length[i],data_test$Petal.Width[i]),1,4)

  #矩阵相乘 %*%

  #得到经过第一个隐藏层的输出，也就是最后输出层的输入

  x2=f(x1%*%w1-a1)

  #得到最后的输出层，是1*3的矩阵

  x3=f(x2%*%w2-a2)

  cha11=(y1-x3)

  cha22=(y2-x3)

  cha33=(y3-x3)

  cha1=0

  cha2=0

  cha3=0

  for(j in 1:3)

  {

    cha1=cha1+abs(cha11[j])

    cha2=cha2+abs(cha22[j])

    cha3=cha3+abs(cha33[j])

  }

  micha=min(cha1,cha2,cha3)

  #cat("micha",micha,"\n")

  #cat("cha1",cha1,"\n")

  #cat("cha2",cha2,"\n")

  #cat("cha3",cha3,"\n")

  if(micha==cha1 & data_test$Species[i]=="setosa") {print(1)

    right=right+1}

  if(micha==cha2 & data_test$Species[i]=="versicolor"){print(2)

    right=right+1}

  if(micha==cha3 & data_test$Species[i]=="virginica") {print(3)

    right=right+1}

}

print("accuracy:")

print((right*1.0/SUM))

cat("right",right)

print("")

cat("SUM",SUM)

2. 带包实现BP人工神经完成iris

rm(list=ls())

#install.packages("sampling")

library(nnet)

library(sampling)

setwd("C:/Users/Administrator/Desktop/R???????????ฺพ???าต/สต??4-?หน?????????")

data("iris")

iris$Sepal.Length=(iris$Sepal.Length-min(iris$Sepal.Length))*1.0/

  (max(iris$Sepal.Length)-min(iris$Sepal.Length))

iris$Sepal.Width=(iris$Sepal.Width-min(iris$Sepal.Width))*1.0/

  (max(iris$Sepal.Width)-min(iris$Sepal.Width))

iris$Petal.Length=(iris$Petal.Length-min(iris$Petal.Length))*1.0/

  (max(iris$Petal.Length)-min(iris$Petal.Length))

iris$Petal.Width=(iris$Petal.Width-min(iris$Petal.Width))*1.0/

  (max(iris$Petal.Width)-min(iris$Petal.Width))

n=round(3/5*nrow(iris)/3)

sub_train=strata(iris,stratanames=("Species"),size=rep(n,3),method="srswor")

head(sub_train)

colnames(iris)<-c("x1","x2","x3","x4","y")

data_train=iris[sub_train$ID_unit,]

data_test=iris[-sub_train$ID_unit,]

dim(data_train)

dim(data_test)

model1=nnet(y~.,data=data_train,size=6,decay=5e-5,maxit=1000)

pred=predict(model1,data_test[,1:4],type="class")

P=sum(as.numeric(pred==data_test$y))/nrow(data_test)

cat("accuracy",P*100,"%\n")

table(data_test$y,pred)

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