c语言版bp神经网络.doc

看到学生用Matlab做了BP神经网络应用的程序，突然想起自己在上学时写了个BP的C语言版，现在真不知道当时是怎么写出来的，这里放上来做一个纪念。#include#include#include#include#include#include#include#define MAXWEIGHT (float)0.3)#define SCALEWEIGHT (float)32767)#define nInputNodes 4 #define nHiddenNodes 8 #define nOutputNodes 3 #define nPatterns 15#define nIterations 5000#define ESC 27#define ERRORLEVEL 0.0001#define ITEMS 8typedef float *PFLOAT;typedef PFLOAT VECTOR;typedef PFLOAT *MATRIX;void VectorAllocate(VECTOR *vector,int nCols);void AllocateCols(PFLOAT matrix,int nRows,int nCols);void MatrixAllocate(MATRIX *pmatrix,int nRows,int nCols);void MatrixFree(MATRIX matrix,int nRows);MATRIX out0;MATRIX out1;MATRIX delta1;MATRIX delw1;MATRIX w1;MATRIX out2;MATRIX delta2;MATRIX delw2;MATRIX w2;MATRIX target;/VECTOR PatternID;void main() float eta =0.15, alpha=0.075; int nReportErrors=100; float ErrorLevel=ERRORLEVEL; char MonitorError=0; float error; register int h; register int i; register int j; int p, q;/ r; FILE *fpPattern, *fpWeightsOut, *fpResults, *fpError, *fpTest; MatrixAllocate(&out0, nPatterns, nInputNodes); MatrixAllocate(&out1, nPatterns, nHiddenNodes); MatrixAllocate(&out2, nPatterns, nOutputNodes); MatrixAllocate(&delta2,nPatterns, nOutputNodes); MatrixAllocate(&delw2, nOutputNodes, nHiddenNodes+1); MatrixAllocate(&w2, nOutputNodes, nHiddenNodes+1); MatrixAllocate(&delta1, nPatterns, nHiddenNodes); MatrixAllocate(&delw1, nHiddenNodes,nInputNodes+1); MatrixAllocate(&w1, nHiddenNodes,nInputNodes+1); MatrixAllocate(&target,nPatterns, nOutputNodes);/ VectorAllocate(&PatternID,nPatterns);/初始化权值- srand(unsigned)time(NULL); for(h=0;hnHiddenNodes;h+) for(i=0;i=nInputNodes;i+) float frand=rand(); w1hi=MAXWEIGHT*(1.0-2*frand/SCALEWEIGHT); delw1hi=0.0; for(j=0;jnOutputNodes;j+) for(h=0;h=nHiddenNodes;h+) float frand=rand(); w2jh=MAXWEIGHT*(0.1-2*frand/SCALEWEIGHT); delw2jh=0.0; /输入训练样本- if(fpPattern=fopen(input.txt,r) for (p=0;pnPatterns;p+) for (i=0;inInputNodes;i+) fscanf(fpPattern,%f,&out0pi); for (p=0;pnPatterns;p+) for (i=0;i1 ? Training.n:Testingn); for(q=0;qnIterations;q+) for (p=0;pnPatterns;p+) for (h=0;hnHiddenNodes;h+) float sum=w1hnInputNodes; for (i=0;inInputNodes;i+) sum+=w1hi*out0pi; out1ph=1.0/(1.0+exp(-sum); for(j=0;jnOutputNodes;j+) float sum=w2jnHiddenNodes; for(h=0;hnHiddenNodes;h+) sum+=w2jh*out1ph; out2pj=1.0/(1.0+exp(-sum); for(j=0;jnOutputNodes;j+) delta2pj=(targetpj-out2pj)* out2pj*(1.0-out2pj); for(h=0;hnHiddenNodes;h+) float sum=0.0; for(j=0;jnOutputNodes;j+) sum+=delta2pj*w2jh; delta1ph=sum*out1ph*(1.0-out1ph); /for p for(j=0;jnOutputNodes;j+) float dw; float sum=0.0; for(p=0;pnPatterns;p+) sum+=delta2pj; dw=eta*sum+alpha*delw2jnHiddenNodes; w2jnHiddenNodes+=dw; delw2jnHiddenNodes=dw; for(h=0;hnHiddenNodes;h+) float sum=0.0; for(p=0;pnPatterns;p+) sum+=delta2pj*out1ph; dw =eta*sum+alpha*delw2jh; w2jh +=dw; delw2jh=dw; for(h=0;hnHiddenNodes;h+) float dw; float sum=0.0; for(p=0;pnPatterns;p+) sum+=delta1ph; dw =eta*sum+alpha*delw1hnInputNodes; w1hnInputNodes+=dw; delw1hnInputNodes=dw; for(i=0;inInputNodes;i+) float sum=0.0; for(p=0;pnPatterns;p+) sum+=delta1ph*out0pi; dw=eta*sum+alpha*delw1hi; w1hi+=dw; delw1hi=dw; if(kbhit() int c=_getch(); if(c=toupper(c)=E) MonitorError+; else if(c=ESC) break; if(MonitorError|(q%nReportErrors=0) for(error=0.0,p=0;pnPatterns;p+) for(j=0;jnOutputNodes;j+) float temp=targetpj-out2pj; error+=temp*temp; error/=(nPatterns*nOutputNodes); fprintf(stderr,Iteration %5d/%-5d Error %fr, q,nIterations,error); MonitorError=0; if(q% nReportErrors=0) fprintf(fpError,%d %fn,q,error); if(errorErrorLevel) break; /q for(p=0,error=0.0;pnPatterns;p+) for(j=0;jnPatterns;j+) float temp=targetpj-out2pj; error+=temp*temp; error/=(nPatterns*nOutputNodes); fprintf(stderr,Iteration %5d/%-5d Error %fn,q,nIterations,error); fprintf(fpError,n%d %fn,q,error); fclose(fpError); /输出权值- if(fpWeightsOut=fopen(WeightsOut.txt,w)=NULL) fprintf(stderr,cant write file %sn,WeightsOut.txt); exit(1); for(h=0;hnHiddenNodes;h+) for(i=0;i=nInputNodes;i+) fprintf(fpWeightsOut,%g%c,w1hi,i%ITEMS=ITEMS-1?n: ); for(j=0;jnOutputNodes;j+) for(h=0;hnHiddenNodes;h+) fprintf(fpWeightsOut,%g%c,w2jh,j%ITEMS=ITEMS-1?n: ); fclose(fpWeightsOut);/输出训练结果- if(fpResults=fopen(Results.txt,w)=NULL) fprintf(stderr,cant write file %sn,Results.txt); fpResults=stderr; for(p=0;pnPatterns;p+) fprintf(fpResults,%d ,p+1); for(j=0;jnOutputNodes;j+) fprintf(fpResults,%f ,targetpj); fprintf(fpResults, - ); for(j=0;jnOutputNodes;j+) fprintf(fpResults,%f ,out2pj); fprintf(fpResults,n); fclose(fpResults);/-测试数据- printf(Training has complete!n1.Test datan2.Exitn); int c=getchar(); if(c=1) char ftest64; int testnum; printf(Please input file name of data!n);test: scanf(%s,&ftest); if(fpTest=fopen(ftest,r)=NULL) printf(This file %s is not exist,ftest); goto test; fscanf(fpTest,%d,&testnum); printf(The result of you testing is:n); for(p=0;ptestnum;p+) printf(%d : ,p+1); for(h=0;hnInputNodes;h+) fscanf(fpTest,%f,&out00h); for (h=0;hnHiddenNodes;h+) float sum=w1hnInputNodes; for (i=0;inInputNodes;i+) sum+=w1hi*out00i; out10h=1.0/(1.0+exp(-sum); for(j=0;jnOutputNodes;j+) float sum=w2jnHiddenNodes; for(h=0;hnHiddenNodes;h+) sum+=w2jh*out10h; out20j=1.0/(1.0+exp(-sum); printf(%f ,out20j); printf(n); MatrixFree(out0, nPatterns); MatrixFree(out1, nPatterns); MatrixFree(delta1,nPatterns); MatrixFree(delw1, nHiddenNodes); MatrixFree(w1, nHiddenNodes); MatrixFree(out2, nPatterns); MatrixFree(delta2,nPatterns); MatrixFree(delw2, nOutputNodes); MatrixFree(w2, nOutputNodes); MatrixFre