BP神经网络 java代码实现
package backp;
import java.*;
import java.awt.*;
import java.io.*;
import java.util.Scanner;
//by realmagician
import org.omg.CORBA.portable.InputStream;
public class backpro {
public static void main(String args[])
{
String filename=new String("delta.in");
try {
FileInputStream fileInputStream=new FileInputStream(filename);
Scanner sinScanner=new Scanner(fileInputStream);
int attN,hidN,outN,samN;
attN=sinScanner.nextInt();
outN=sinScanner.nextInt();
hidN=sinScanner.nextInt();
samN=sinScanner.nextInt();
//System.out.println(attN+" "+outN+" "+hidN+" "+samN);
double samin[][]=new double[samN][attN];
double samout[][]=new double[samN][outN];
for(int i=0;i
{
for(int j=0;j
{
samin[i][j]=sinScanner.nextDouble();
}
for(int j=0;j
{
samout[i][j]=sinScanner.nextDouble();
}
}
int times=10000;
double rate=0.5;
BP2 bp2=new BP2(attN,outN,hidN,samN,times,rate);
bp2.train(samin, samout);
for(int i=0;i
{
for(int j=0;j
System.out.print(bp2.dw1[i][j]+" ");
System.out.println();
}
for(int i=0;i
{
for(int j=0;j
System.out.print(bp2.dw2[i][j]+" ");
System.out.println();
}
while(true)
{
double testout[]=new double[outN];
double testin[]=new double[attN];
Scanner testinScanner=new Scanner(System.in);
for(int i=0;i
{
testin[i]=testinScanner.nextDouble();
}
testout=bp2.getResault(testin);
for(int i=0;i
System.out.print(testout[i]+" ");
System.out.println(outN);
}
} catch (IOException e) {
// TODO: handle exception
}
System.out.println("End");
}
}
class BP2//包含一个隐含层的神经网络
{
double dw1[][],dw2[][];
int hidN;//隐含层单元个数
int samN;//学习样例个数
int attN;//输入单元个数
int outN;//输出单元个数
int times;//迭代次数
double rate;//学习速率
boolean trained=false;//保证在得结果前，先训练
BP2(int attN,int outN,int hidN,int samN,int times,double rate)
{
this.attN=attN;
this.outN=outN;
this.hidN=hidN;
this.samN=samN;
dw1=new double[hidN][attN+1];//每行最后一个是阈值w0
for(int i=0;i
{
for(int j=0;j<=attN;++j)
dw1[i][j]=Math.random()/2;
}
dw2=new double[outN][hidN+1];//输出层权值,每行最后一个是阈值w0
for(int i=0;i
{
for(int j=0;j<=hidN;++j)
dw2[i][j]=Math.random()/2;
}
this.times=times;
this.rate=rate;
}
public void train(double samin[][],double samout[][])
{
double dis=0;//总体误差
int count=times;
double temphid[]=new double[hidN];
double tempout[]=new double[outN];
double wcout[]=new double[outN];
double wchid[]=new double[hidN];
while((count--)>0)//迭代训练
{
dis=0;
for(int i=0;i
{
for(int j=0;j
{
temphid[j]=0;
for(int k=0;k
temphid[j]+=dw1[j][k]*samin[i][k];
temphid[j]+=dw1[j][attN];//计算阈值产生的隐含层结果
temphid[j]=1.0/(1+Math.exp(-temphid[j] ));
}
for(int j=0;j
{
tempout[j]=0;
for(int k=0;k
tempout[j]+=dw2[j][k]*temphid[k];
tempout[j]+=dw2[j][hidN];//计算阈值产生的输出结果
tempout[j]=1.0/(1+Math.exp( -tempout[j] ));
}
//计算每个输出单元的误差项
for(int j=0;j
{
wcout[j]=tempout[j]*(1-tempout[j])*(samout[i][j]-tempout[j]);
dis+=Math.pow((samout[i][j]-tempout[j]),2);
}
//计算每个隐藏单元的误差项
for(int j=0;j
{
double wche=0;
for(int k=0;k
{
wche+=wcout[k]*dw2[k][j];
}
wchid[j]=temphid[j]*(1-temphid[j])*wche;
}
//改变输出层的权值
for(int j=0;j
{
for(int k=0;k
{
dw2[j][k]+=rate*wcout[j]*temphid[k];
}
dw2[j][hidN]=rate*wcout[j];
}
//改变隐含层的权值
for(int j=0;j
{
for(int k=0;k
{
dw1[j][k]+=rate*wchid[j]*samin[i][k];
}
dw1[j][attN]=rate*wchid[j];
}
}
if(dis<0.003)
break;
}
trained=true;
}
public double[] getResault(double samin[])
{
double temphid[]=new double[hidN];
double tempout[]=new double[outN];
if(trained==false)
return null;
for(int j=0;j
{
temphid[j]=0;
for(int k=0;k
temphid[j]+=dw1[j][k]*samin[k];
temphid[j]+=dw1[j][attN];//计算阈值产生的隐含层结果
temphid[j]=1.0/(1+Math.exp(-temphid[j] ));
}
for(int j=0;j
{
tempout[j]=0;
for(int k=0;k
tempout[j]+=dw2[j][k]*temphid[k];
tempout[j]+=dw2[j][hidN];//计算阈值产生的输出结果
tempout[j]=1.0/(1+Math.exp( -tempout[j]));
//System.out.print(tempout[j]+" ");
}
return tempout;
}
}