1、神經網絡文字識別關鍵代碼下面作者列出bp網絡訓練及識別的完整源代碼bp.h。#include <stdio.h>#include <math.h> #include <time.h>#include <stdlib.h>#define BIGRND 32767/*函數聲明部分*/隨機數產生函數double drnd();double dpn1();/S函數double squash(double x);/分配1維double型的內存double *alloc_1d_dbl(int n);/分配2維double型的內存double *alloc_2

2、d_dbl(int m, int n);/初始化BP網絡void bpnn_initialize(int seed);/隨機初始化權值void bpnn_randomize_weights(double *w, int m, int n);/零初始化權值void bpnn_zero_weights(double *w, int m, int n);/信息的前向傳輸void bpnn_layerforward(double *l1, double *l2, double *conn, int n1, int n2);/誤差輸出void bpnn_output_error(double *delt

3、a, double *target, double *output, int nj);/隱層誤差void bpnn_hidden_error(double* delta_h, int nh, double *delta_o, int no, double *who, double *hidden);/根據誤差調整權值void bpnn_adjust_weights(double *delta, int ndelta, double *ly, int nly, double* w, double *oldw, double eta, double momentum);/保存權值void w_we

4、ight(double *w,int n1,int n2,char*name);/讀取權值bool r_weight(double *w,int n1,int n2,char *name);/保存Bp網絡各層結點數目void w_num(int n1,int n2,int n3,char*name);/讀取Bp網絡各層結點數目bool r_num(int *n,char *name);/特征提取void code(BYTE*image ,int *p,int w,int h,int dw);/BP網絡訓練void BpTrain(HDIB hDIB,int n_hidden,double mi

5、n_ex,double momentum,double eta ,int width,int height);/利用BP網絡進行識別void CodeRecognize(HDIB hDIB,int width ,int height ,int n_in ,int n_hidden,int n_out);/*/* 以下是函數的實現部分*/* 返回01的雙精度隨機數 */double drnd()/BIGRND定義為隨機數的最大范圍return (double) rand() / (double) BIGRND);/* 返回-1.0到1.0之間的雙精度隨機數 */double dpn1()retu

6、rn (drnd() * 2.0) - 1.0);double squash(double x)/返回S激活函數return (1.0 / (1.0 + exp(-x);/* 申請1維雙精度實數數組 */double *alloc_1d_dbl(int n)double *new1;/申請內存new1 = (double *) malloc (unsigned) (n * sizeof (double);/申請內存失敗處理if (new1 = NULL) printf("ALLOC_1D_DBL: Couldn't allocate array of doublesn&quo

7、t;);return (NULL);/返回申請到的內存的指針return (new1);/* 申請2維雙精度實數數組 */double *alloc_2d_dbl(int m, int n)int i;/定義一二維指針double *new1;/先申請一維內存new1 = (double *) malloc (unsigned) (m * sizeof (double *);/申請失敗處理if (new1 = NULL) /printf("ALLOC_2D_DBL: Couldn't allocate array of dbl ptrsn");return (NUL

8、L);/再申請二維內存。一維內存中存放的是指針for (i = 0; i < m; i+) new1i = alloc_1d_dbl(n);/返回申請到的二維內存（可以看作矩陣）return (new1);/* 設置隨機數種子 */void bpnn_initialize(int seed)/printf("Random number generator seed: %dn", seed);srand(seed);/* 隨機初始化權值 */void bpnn_randomize_weights(double *w, int m, int n)int i, j;/調用d

9、pn1隨機初始化權值for (i = 0; i <= m; i+) for (j = 0; j <= n; j+) wij = dpn1();/* 零初始化權值 */void bpnn_zero_weights(double *w, int m, int n)int i, j;/將權值逐個賦0for (i = 0; i <= m; i+) for (j = 0; j <= n; j+) wij = 0.0;/*前向傳輸*/void bpnn_layerforward(double *l1, double *l2, double *conn, int n1, int n2

10、)double sum;int j, k;/* 設置閾值 */l10 = 1.0;/* 對于第二層的每個神經元 */for (j = 1; j <= n2; j+) /* 計算輸入的加權總和 */sum = 0.0;for (k = 0; k <= n1; k+) sum += connkj * l1k;l2j = squash(sum);/* 輸出誤差 */void bpnn_output_error(double *delta, double *target, double *output, int nj)int j;double o, t, errsum;/先將誤差歸零err

11、sum = 0.0;/循環計算deltafor (j = 1; j <= nj; j+) o = outputj;t = targetj;/計算delta值deltaj = o * (1.0 - o) * (t - o);/* 隱含層誤差 */void bpnn_hidden_error(double* delta_h, int nh, double *delta_o, int no, double *who, double *hidden)int j, k;double h, sum, errsum;/誤差歸零errsum = 0.0;/計算新deltafor (j = 1; j &l

12、t;= nh; j+) h = hiddenj;sum = 0.0;for (k = 1; k <= no; k+) sum += delta_ok * whojk;delta_hj = h * (1.0 - h) * sum;/* 調整權值 */void bpnn_adjust_weights(double *delta, int ndelta, double *ly, int nly, double* w, double *oldw, double eta, double momentum)double new_dw;int k, j;ly0 = 1.0;/請參考文章中BP網絡權值調

13、整的計算公式for (j = 1; j <= ndelta; j+) for (k = 0; k <= nly; k+) new_dw = (eta * deltaj * lyk) + (momentum * oldwkj);wkj += new_dw;oldwkj = new_dw;/*保存權值*/void w_weight(double *w,int n1,int n2,char*name)int i,j;double *buffer;/創建文件指針FILE *fp;/打開文件fp=fopen(name,"wb+");/分配緩沖區buffer=(double

14、*)malloc(n1+1)*(n2+1)*sizeof(double);/填寫緩沖區內容for(i=0;i<=n1;i+)for(j=0;j<=n2;j+)bufferi*(n2+1)+j=wij;/將緩沖區內容寫入文件fwrite(char*)buffer,sizeof(double),(n1+1)*(n2+1),fp);/關閉文件fclose(fp);/清空緩沖區free(buffer);/*讀取權值*/bool r_weight(double *w,int n1,int n2,char *name)int i,j;/臨時緩沖區指針double *buffer;/文件指針FI

15、LE *fp;/判斷是否可以正確打開文件。若失敗則返回falseif(fp=fopen(name,"rb")=NULL):MessageBox(NULL,"無法讀取權值信息",NULL,MB_ICONSTOP);return (false);/將臨時緩沖區指針執行新申請的內存buffer=(double*)malloc(n1+1)*(n2+1)*sizeof(double);/讀取文件內容到緩沖區fread(char*)buffer,sizeof(double),(n1+1)*(n2+1),fp);/由緩沖區內容填寫權值for(i=0;i<=n1;

16、i+)for(j=0;j<=n2;j+)wij=bufferi*(n2+1)+j;/關閉文件fclose(fp);/釋放臨時緩沖區free(buffer);/返回true表示已經正確讀取return(true);/*保存Bp網絡各層結點的數目*/void w_num(int n1,int n2,int n3,char*name)/文件指針FILE *fp;/打開文件fp=fopen(name,"wb+");/緩沖區指針int *buffer;/分配內存并指向緩沖區指針buffer=(int*)malloc(3*sizeof(int);/將網絡各層參數信息保存在臨時緩沖

17、區buffer0=n1;buffer1=n2;buffer2=n3;/將臨時緩沖區內容寫入文件fwrite(char*)buffer,sizeof(int),3,fp);/關閉文件fclose(fp);/清空緩沖區free(buffer);/*讀取Bp網絡各層結點數目*/bool r_num(int *n,char *name)/臨時緩沖區指針int *buffer;/文件指針FILE *fp;/分配內存空間buffer=(int *)malloc(3*sizeof(int);if(fp=fopen(name,"rb")=NULL):MessageBox(NULL,&quo

18、t;結點參數",NULL,MB_ICONSTOP);return (false);/讀取文件到緩沖區fread(char*)buffer,sizeof(int),3,fp);/從緩沖區中取出數據，存入n0n2n0=buffer0;n1=buffer1;n2=buffer2;/關閉文件fclose(fp);/清空緩沖區free(buffer);/正確讀取，返回truereturn(true);/* 函數名稱 code()* 參量：* BYTE* lpDIBBits -指向輸入圖像的像素其實位置的指針 * int num -圖片中樣本的個數* LONG lLineByte -輸入圖片每行

19、的字節數* LONG lSwidth -預處理時歸一化的寬度* LONG lSheight -預處理時歸一化的長度* 函數功能 ：* 對于輸入樣本提取特征向量，在這里把歸一化樣本的* 每一個像素都作為特征提取出來*/double* code (BYTE* lpDIBBits,int num, LONG lLineByte,LONG lSwidth,LONG lSheight) /循環變量int i,j,k;BYTE* lpSrc; / 建立保存特征向量的二維數組double *data;/ 為這個數組申請二維存儲空間data = alloc_2d_dbl(num,lSwidth*lSheigh

20、t);/ 將歸一化的樣本的每個像素作為一個特征點提取出來/逐個數據掃描for(k=0;k<num;k+) /對每個數據逐行掃描for(i=0;i<lSheight;i+) /對每個數據逐列掃描for(j=k*lSwidth;j<(k+1)*lSwidth;j+)/ 指向圖像第i行第j列個像素的指針lpSrc = lpDIBBits + i*lLineByte + j;/如果這個像素是黑色的if(*(lpSrc)=0)/將特征向量的相應位置填1dataki*lSwidth+j-k*lSwidth=1;/如果這個像素是其他的 if(*(lpSrc)!=0)/將特征向量的相應位置填

21、0dataki*lSwidth+j-k*lSwidth=0;/返回指向特征矩陣的二維指針/特征矩陣構成：每個樣本×每個樣本的特征return(data); /* 函數名稱 BpTrain()* * 參數：* double *data_in -指向輸入的特征向量數組的指針 *double *data_out -指向理想輸出數組的指針int n_in -輸入層結點的個數 * int n_hidden -BP網絡隱層結點的數目* double min_ex -訓練時允許的最大均方誤差* double momentum -BP網絡的相關系數* double eta -BP網絡的訓練步長* i

22、nt num -輸入樣本的個數* 函數功能：* 根據輸入的特征向量和期望的理想輸出對BP網絡盡行訓練* 訓練結束后將權值保存并將訓練的結果顯示出來*/void BpTrain(double * data_in, double* data_out,int n_in,int n_hidden,double min_ex,double momentum,double eta ,int num)/循環變量 int i,k,l;/輸出層結點數目int n_out=4; /指向輸入層數據的指針double* input_unites; /指向隱層數據的指針double* hidden_unites;/指向

23、輸出層數據的指針double* output_unites; /指向隱層誤差數據的指針double* hidden_deltas;/指向輸出層誤差數劇的指針double* output_deltas; /指向理想目標輸出的指針double* target; /指向輸入層于隱層之間權值的指針double* input_weights;/指向隱層與輸出層之間的權值的指針double* hidden_weights;/指向上一此輸入層于隱層之間權值的指針double* input_prev_weights ;/指向上一此隱層與輸出層之間的權值的指針double* hidden_prev_weight

24、s;/每次循環后的均方誤差誤差值 double ex;/為各個數據結構申請內存空間input_unites= alloc_1d_dbl(n_in + 1);hidden_unites=alloc_1d_dbl(n_hidden + 1);output_unites=alloc_1d_dbl(n_out + 1);hidden_deltas = alloc_1d_dbl(n_hidden + 1);output_deltas = alloc_1d_dbl(n_out + 1);target = alloc_1d_dbl(n_out + 1);input_weights=alloc_2d_dbl(

25、n_in + 1, n_hidden + 1);input_prev_weights = alloc_2d_dbl(n_in + 1, n_hidden + 1);hidden_prev_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);hidden_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);/為產生隨機序列撒種time_t t; bpnn_initialize(unsigned)time(&t);/對各種權值進行初始化初始化bpnn_randomize_weights( input_w

26、eights,n_in,n_hidden);bpnn_randomize_weights( hidden_weights,n_hidden,n_out);bpnn_zero_weights(input_prev_weights, n_in,n_hidden );bpnn_zero_weights(hidden_prev_weights,n_hidden,n_out );/開始進行BP網絡訓練/這里設定最大的迭代次數為15000次for(l=0;l<15000;l+) /對均方誤差置零ex=0;/對樣本進行逐個的掃描for(k=0;k<num;k+) /將提取的樣本的特征向量輸送到輸

27、入層上for(i=1;i<=n_in;i+)input_unitesi = data_inki-1;/將預定的理想輸出輸送到BP網絡的理想輸出單元for(i=1;i<=n_out;i+)targeti=data_outki-1;/前向傳輸激活/將數據由輸入層傳到隱層 bpnn_layerforward(input_unites,hidden_unites,input_weights, n_in,n_hidden);/將隱層的輸出傳到輸出層bpnn_layerforward(hidden_unites, output_unites,hidden_weights,n_hidden,n_

28、out);/誤差計算/將輸出層的輸出與理想輸出比較計算輸出層每個結點上的誤差bpnn_output_error(output_deltas,target,output_unites,n_out);/根據輸出層結點上的誤差計算隱層每個節點上的誤差bpnn_hidden_error(hidden_deltas,n_hidden, output_deltas, n_out,hidden_weights, hidden_unites);/權值調整/根據輸出層每個節點上的誤差來調整隱層與輸出層之間的權值 bpnn_adjust_weights(output_deltas,n_out, hidden_un

29、ites,n_hidden,hidden_weights, hidden_prev_weights, eta, momentum); /根據隱層每個節點上的誤差來調整隱層與輸入層之間的權值 bpnn_adjust_weights(hidden_deltas, n_hidden, input_unites, n_in,input_weights, input_prev_weights, eta, momentum); /誤差統計for(i=1;i<=n_out;i+)ex+=(output_unitesi-data_outki-1)*(output_unitesi-data_outki-1

30、);/計算均方誤差ex=ex/double(num*n_out);/如果均方誤差已經足夠的小，跳出循環，訓練完畢 if(ex<min_ex)break;/相關保存/保存輸入層與隱層之間的權值w_weight(input_weights,n_in,n_hidden,"win.dat");/保存隱層與輸出層之間的權值w_weight(hidden_weights,n_hidden,n_out,"whi.dat");/保存各層結點的個數w_num(n_in,n_hidden,n_out,"num");/顯示訓練結果CString st

31、r;if(ex<=min_ex)str.Format ("迭代%d次，n平均誤差%.4f",l,ex);:MessageBox(NULL,str,"訓練結果",NULL);if(ex>min_ex)str.Format("迭代%d次，平均誤差%.4fn我已經盡了最大努力了還是達不到您的要求n請調整參數重新訓練吧！",l,ex);:MessageBox(NULL,str,"訓練結果",NULL);/釋放內存空間free(input_unites);free(hidden_unites);free(outp

32、ut_unites);free(hidden_deltas);free(output_deltas);free(target);free(input_weights);free(hidden_weights);free(input_prev_weights);free(hidden_prev_weights);/* 函數名稱* CodeRecognize()* 參量* double *data_in -指向待識別樣本特征向量的指針* int num -待識別的樣本的個數 * int n_in -Bp網絡輸入層結點的個數 * int n_hidden -Bp網絡隱層結點的個數* int n_ou

33、t -Bp網絡輸出層結點的個數* 函數功能： * 讀入輸入樣本的特征相量并根據訓練所得的權值 * 進行識別，將識別的結果寫入result.txt */void CodeRecognize(double *data_in, int num ,int n_in,int n_hidden,int n_out)/循環變量int i,k;/ 指向識別結果的指針 int *recognize;/為存放識別的結果申請存儲空間recognize=(int*)malloc(num*sizeof(int);/指向輸入層數據的指針double* input_unites; /指向隱層數據的指針double* hid

34、den_unites;/指向輸出層數據的指針double* output_unites; /指向輸入層于隱層之間權值的指針double* input_weights;/指向隱層與輸出層之間的權值的指針double* hidden_weights;/為各個數據結構申請內存空間input_unites= alloc_1d_dbl(n_in + 1);hidden_unites=alloc_1d_dbl(n_hidden + 1);output_unites=alloc_1d_dbl(n_out + 1);input_weights=alloc_2d_dbl(n_in + 1, n_hidden +

35、 1);hidden_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);/讀取權值if( r_weight(input_weights,n_in,n_hidden,"win.dat")=false)return;if(r_weight(hidden_weights,n_hidden,n_out,"whi.dat")=false)return;/逐個樣本掃描for(k=0;k<num;k+) /將提取的樣本的特征向量輸送到輸入層上for(i=1;i<=n_in;i+)input_unitesi=da

36、ta_inki-1;/前向輸入激活bpnn_layerforward(input_unites,hidden_unites,input_weights, n_in,n_hidden);bpnn_layerforward(hidden_unites, output_unites,hidden_weights,n_hidden,n_out);/根據輸出結果進行識別int result=0 ;/考察每一位的輸出for(i=1;i<=n_out;i+)/如果大于0.5判為1if(output_unitesi>0.5)result+=(int)pow(2,double(4-i);/如果判定的

37、結果小于等于9，認為合理if(result<=9)recognizek=result;/如果判定的結果大于9，認為不合理將結果定位為一個特殊值20if(result>9)recognizek=20;/將識別結果寫到文本中FILE *fp;fp=fopen("result.txt","w+");for(i=0;i<num;i+) if(recognizei=20)fprintf(fp,"無法識別，");elsefprintf(fp,"%d,",recognizei);fclose(fp);/將識別的

38、結果顯示出來CString str,str1;for(i=0;i<num;i+)if(recognizei!=20)str.Format("%d ",recognizei);if(recognizei=20)str.Format("無法識別 ");str1+=str;/通知用戶訓練完成:MessageBox(NULL,str1,"識別結果",NULL);/釋放存儲空間free(input_unites);free(hidden_unites);free(output_unites);free(input_weights);fre

39、e(hidden_weights);下面是菜單事件響應函數。訓練BP網絡：void CChildView:OnBpnetTrain() OnImgprcAll();/判斷是否經過了歸一劃的處理if(gyhfinished=false)/如果沒有進行提示錯誤并返回:MessageBox(NULL,"沒有進行歸一劃預處理",NULL,MB_ICONSTOP);return;/建立BP網絡訓練參數對話框CDBpParamater BpPa;/用默認值初始化變量/動量因子BpPa.m_a=0;/學習步長BpPa.m_eta=0.015;/允許誤差BpPa.m_ex=0.001;/隱

40、層神經元數目BpPa.m_hn=10;/ 顯示對話框if(BpPa.DoModal()!=IDOK)/返回return;/用戶獲得參數信息/相關系數(動量因子)double momentum=BpPa.m_a; /最小均方誤差double min_ex=BpPa.m_ex; /隱層數目int n_hidden=BpPa.m_hn; /訓練步長double eta=BpPa.m_eta;/獲得指向DIB的指針BYTE *lpDIB=(BYTE*):GlobalLock(HGLOBAL) m_hDIB);/獲得指向DIB像素的指針，并指向像素的起始位置BYTE *lpDIBBits =(BYTE*

41、):FindDIBBits(char *)lpDIB);/獲得顏色信息int numColors=(int) :DIBNumColors(char *)lpDIB);/不是灰度圖返回if (numColors!=256) :GlobalUnlock(HGLOBAL) m_hDIB);:MessageBox(NULL,"只能處理灰度圖像",NULL,MB_ICONSTOP);return;/獲取圖像的寬度LONG lWidth = (LONG) :DIBWidth(char *)lpDIB); /獲取圖像的高度LONG lHeight = (LONG) :DIBHeight(

42、char *)lpDIB);/計算圖像每行的字節數LONG lLineByte = (lWidth+3)/4*4; /歸一化的寬度LONG lSwidth = w_sample;/歸一化的高度LONG LSheight = h_sample;/指向輸入樣本的特征向量的指針 double *data_in;/從輸入的訓練樣本中提取特征向量data_in = code ( lpDIBBits, digicount, lLineByte, lSwidth, LSheight);/計算輸入層結點的數目int n_in = LSheight*lSwidth;/設定目標輸出向量double out4= 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.9,0.1,0.1,0.9,0.1,0.1,0.1,0.9,0.9,0.1,0.9,0.1,0.1,0.1,0.9,0.1,0.9,0.1,0.9,0.9,0.1,0.1,0.9,0.9,0.9,0.9,0.1,0.1,0.1,0.9,0.1,0.1,0.9;/指向輸出double *data_out;data_out = alloc_2d_dbl(digicount,4);for(int i=0;i<digicount;i+)for(int