BP神经网络

转载的大佬的BP神经网络 代码

原文：http://www.cnblogs.com/jzhlin/archive/2012/07/30/bp_c.html

#define Data  820

#define In 2
#define Out 1
#define Neuron 45
#define TrainC 20000
#define A  0.2
#define B  0.4
#define a  0.2
#define b  0.3


double d_in[Data][In],d_out[Data][Out];
double w[Neuron][In],o[Neuron],v[Out][Neuron];
double Maxin[In],Minin[In],Maxout[Out],Minout[Out];
double OutputData[Out];
double dv[Out][Neuron],dw[Neuron][In];
double e;






Data 用来表示已经知道的数据样本的数量，也就是训练样本的数量。
In 表示对于每个样本有多少个输入变量;
 Out 表示对于每个样本有多少个输出变量。
Neuron 表示神经元的数量，
TrainC 来表示训练的次数。再来我们看对神经网络描述的数据定义，来看下面这张图里面的数据类型都是 double 型。


d_in[Data][In] 存储 Data 个样本，每个样本的 In 个输入。
d_out[Data][Out] 存储 Data 个样本，每个样本的 Out 个输出。
我们用邻接表法来表示 图1 中的网络，
w[Neuron][In]  表示某个输入对某个神经元的权重，

v[Out][Neuron] 来表示某个神经元对某个输出的权重；与之对应的保存它们两个修正量的数组 dw[Neuron][In] 和 dv[Out][Neuron]。数组 o[Neuron] 记录的是神经元通过激活函数对外的输出，OutputData[Out]  存储BP神经网络的输出。

#include <stdio.h>#include <time.h>#include <math.h>#include <stdlib.h>#define Data  820#define In 2#define Out 1#define Neuron 45#define TrainC 20000#define A  0.2#define B  0.4#define a  0.2#define b  0.3double d_in[Data][In],d_out[Data][Out];double w[Neuron][In],o[Neuron],v[Out][Neuron];double Maxin[In],Minin[In],Maxout[Out],Minout[Out];double OutputData[Out];double dv[Out][Neuron],dw[Neuron][In];double e;void writeTest(){    //建模FILE *fp1,*fp2;double r1,r2;int i;srand((unsigned)time(NULL)); if((fp1=fopen("D:\\in.txt","w"))==NULL){printf("can not open the in file\n");exit(0);}if((fp2=fopen("D:\\out.txt","w"))==NULL){printf("can not open the out file\n");exit(0);}for(i=0;i<Data;i++){r1=rand()%1000/100.0;r2=rand()%1000/100.0;fprintf(fp1,"%lf  %lf\n",r1,r2);fprintf(fp2,"%lf \n",r1+r2);}fclose(fp1);fclose(fp2);}void readData(){   //读取数值FILE *fp1,*fp2;int i,j;if((fp1=fopen("D:\\in.txt","r"))==NULL){printf("can not open the in file\n");exit(0);}for(i=0;i<Data;i++)for(j=0; j<In; j++)fscanf(fp1,"%lf",&d_in[i][j]);fclose(fp1);if((fp2=fopen("D:\\out.txt","r"))==NULL){printf("can not open the out file\n");exit(0);}for(i=0;i<Data;i++)for(j=0; j<Out; j++)fscanf(fp1,"%lf",&d_out[i][j]);fclose(fp2);}void initBPNework(){  //初始化神经网络int i,j;for(i=0; i<In; i++){Minin[i]=Maxin[i]=d_in[0][i];for(j=0; j<Data; j++){Maxin[i]=Maxin[i]>d_in[j][i]?Maxin[i]:d_in[j][i];Minin[i]=Minin[i]<d_in[j][i]?Minin[i]:d_in[j][i];}}for(i=0; i<Out; i++){Minout[i]=Maxout[i]=d_out[0][i];for(j=0; j<Data; j++){Maxout[i]=Maxout[i]>d_out[j][i]?Maxout[i]:d_out[j][i];Minout[i]=Minout[i]<d_out[j][i]?Minout[i]:d_out[j][i];}}for (i = 0; i < In; i++)for(j = 0; j < Data; j++)d_in[j][i]=(d_in[j][i]-Minin[i]+1)/(Maxin[i]-Minin[i]+1);for (i = 0; i < Out; i++)for(j = 0; j < Data; j++)d_out[j][i]=(d_out[j][i]-Minout[i]+1)/(Maxout[i]-Minout[i]+1);for (i = 0; i < Neuron; ++i)for (j = 0; j < In; ++j){w[i][j]=rand()*2.0/RAND_MAX-1;dw[i][j]=0;}for (i = 0; i < Neuron; ++i)for (j = 0; j < Out; ++j){v[j][i]=rand()*2.0/RAND_MAX-1;dv[j][i]=0;}}void computO(int var){int i,j;double sum,y;for (i = 0; i < Neuron; ++i){sum=0;for (j = 0; j < In; ++j)sum+=w[i][j]*d_in[var][j];o[i]=1/(1+exp(-1*sum));}for (i = 0; i < Out; ++i){sum=0;for (j = 0; j < Neuron; ++j)sum+=v[i][j]*o[j];OutputData[i]=sum;}}void backUpdate(int var){int i,j;double t;for (i = 0; i < Neuron; ++i){t=0;for (j = 0; j < Out; ++j){t+=(OutputData[j]-d_out[var][j])*v[j][i];dv[j][i]=A*dv[j][i]+B*(OutputData[j]-d_out[var][j])*o[i];v[j][i]-=dv[j][i];}for (j = 0; j < In; ++j){dw[i][j]=a*dw[i][j]+b*t*o[i]*(1-o[i])*d_in[var][j];w[i][j]-=dw[i][j];}}}double result(double var1,double var2){int i,j;double sum,y;var1=(var1-Minin[0]+1)/(Maxin[0]-Minin[0]+1);var2=(var2-Minin[1]+1)/(Maxin[1]-Minin[1]+1);for (i = 0; i < Neuron; ++i){sum=0;sum=w[i][0]*var1+w[i][1]*var2;o[i]=1/(1+exp(-1*sum));}sum=0;for (j = 0; j < Neuron; ++j)sum+=v[0][j]*o[j];return sum*(Maxout[0]-Minout[0]+1)+Minout[0]-1;}void writeNeuron(){FILE *fp1;int i,j;if((fp1=fopen("D:\\neuron.txt","w"))==NULL){printf("can not open the neuron file\n");exit(0);}for (i = 0; i < Neuron; ++i)for (j = 0; j < In; ++j){fprintf(fp1,"%lf ",w[i][j]);}fprintf(fp1,"\n\n\n\n");for (i = 0; i < Neuron; ++i)for (j = 0; j < Out; ++j){fprintf(fp1,"%lf ",v[j][i]);}fclose(fp1);}void  trainNetwork(){  //训练引擎int i,c=0,j;do{e=0;for (i = 0; i < Data; ++i){computO(i);for (j = 0; j < Out; ++j)e+=fabs((OutputData[j]-d_out[i][j])/d_out[i][j]);backUpdate(i);}printf("%d  %lf\n",c,e/Data);c++;}while(c<TrainC && e/Data>0.01);}int  main(int argc, char const *argv[]){writeTest();readData();initBPNework();trainNetwork();printf("%lf \n",result(1,1) );printf("%lf \n",result(2,2) );printf("%lf \n",result(4,4) );writeNeuron();return 0;}