神经网络C++程序.doc

/*backprop.h* */#ifndef_BACKPROP_H_#define_BACKPROP_H_#define BIGRND 0x7fffffff/*神经网络的数据结构。网络被假定为一个全连接的3层前向结构，每层的单元0是阈值，这意味着真正的神经元编号为1-n*/typedef structint input_n; /*输入层的神经元个数*/int hidden_n; /*隐含层的神经元个数*/int output_n; /*输出层的神经元个数*/double *input_units; /*输入层的神经元*/double *hidden_units; /*隐含层的神经元*/double *output_units; /*输出层的神经元*/double *hidden_delta; /*隐含层的误差*/double *output_delta; /*输出层的误差*/double *target; /*目标向量*/double *input_weights; /*输入层到隐藏层的连接权*/double *hidden_weights; /*隐藏层到输出层的连接权*/ /*下面两个在迭代时使用*/ double *input_prev_weights; /*前次输入层到隐藏层权值的改变*/double *input_prev_weights; /*前次隐藏层到输出层权值的改变*/BPNN;/*用户接口*/*初始化随机数种子*/void bpnn_initialize(int seed);/*创建BP网络*/BPNN *bpnn_create(int n_in,int n_hidden,int n_out);/*释放BP网络所占地内存空间*/void bpnn_free(BPNN *net);/*训练BP网络*/void bpnn_train(BPNN *net,double eta,double momentum,double*eo,double *eh);/*前向运算*/void bpnn_feedforward(BPNN *net);/*保存BP网络到文件中*/void bpnn_save(BPNN *net,char *filename);/*从文件中读取BP网络参数*/BPNN *bpnn_read(char *filename);#endif/*backprop.cpp*仅用于学习目的*/#include StdAfx.h#include #include backprop.h#include #include #define ABX(x) (x)0.0?(x):(-(x)/*宏定义：快速拷贝*/#define fastcopy(to,from,len)register char *_to,*from;register int _i,_l;_to=(char *)(to);_from=(char *)(from);_l=(len);for(_i=0;_i_l;_i+) *_to+=*_from+;/*返回01的双精度随机数*/double drnd()return(double) rand()/(double) BIGRND);/*返回-1.0到1.0之间的双精度随机数*/double dpn1()return(drnd()*2.0)-1.0);/*作用函数，目前是S型函数*/参数：x-自变量的值double squash(double x)return(1.0/(1.0+exp(-x);/*申请1维双精度实数数组*/参数：n-数组的维数double *alloc_1d_dbl(int n)double *new1;new1=(double *)malloc (unsigned)(n*sizeof(double);if(new1=NULL)printf(ALLOC_1D_DBL:Couldnt allocate array of doublesn);return(NULL);return (new1);/*申请2维双精度实数数组*/参数：m-数组的行数/n-数组的列数double *alloc_2d_dbl(int m,int n)int i;double *new1;new1=(double *)malloc (unsigned)(m*sizeof(double);if(new1=NULL)printf(ALLOC_2D_DBL:Couldnt allocate array of dbl ptrsn);return(NULL);for(i=0;im;i+)new1i=alloc_1d_dbl(n);return (new1);/*随机初始化权值*/参数：w-保存权值的二级指针/ m-数组的行数/ n-数组的列数void bpnn_randomize_weights(double *w,int m,int n)int i,j;for(i=0;i=m;i+)for(j=0;j=n;j+)wij=dpn1();/*0初始化权值*/参数：w-保存权值的二级指针/ m-数组的行数/ n-数组的列数void bpnn_zero_weights(double *w,int m,int n)int i,j;for(i=0;i=m;i+)for(j=0;jinput_n=n_in;newnet-hidden_n=n_hidden;newnet-output_n=n_out;newnet-input_units=alloc_1d_dbl(n_in+1);newnet-hidden_units=alloc_1d_dbl(n_hidden+1);newnet-output_units=alloc_1d_dbl(n_out+1);newnet-hidden_delta=alloc_1d_dbl(n_hidden+1);newnet-output_delta=alloc_1d_dbl(n_out+1);newnet-target=alloc_1d_dbl(n_out+1);newnet-input_weights=alloc_2d_dbl(n_in+1,n_hidden+1);newnet-hidden_weights=alloc_2d_dbl(n_hidden+1,n_out+1);newnet-input_prev_weights=alloc_2d_dbl(n_in+1,n_hidden+1);newnet-hidden_prev_weights=alloc_2d_dbl(n_hidden+1,n_out+1);return(netnet);/*释放BP网络所占地内存空间*/参数：net-需要释放的内存地址void bpnn_free(BPNN *net)int n1,n2,i;n1=net-intput_n;n2=net-hidden_n;free(char *)net-input_units);free(char *)net-hidden_units);free(char *)net-output_units);free(char *)net-hidden_delta);free(char *)net-output_delta);free(char *)net-target);for(i=0;i=n1;i+)free(char *)net-input_weightsi);free(char *)net-input_prev_weightsi);free(char *)net-input_weights);free(char *)net-input_prev_weights);for(i=0;i=n2;i+)free(char *)net-hidden_weightsi);free(char *)net-hidden_prev_weightsi);free(char *)net-hidden_weights);free(char *)net-hidden_prev_weights);free(char *)net);/*创建一个BP网络，并初始化权值*/参数：n_in-输入层个数/n_hidden-隐含层神经元个数/n_out-输出层个数BNPP *bnpp_create(int n_in,int n_hidden,int n_out)BNPP *newnet;newnet=bnpp_internal_create(n_in,n_hidden,n_out);#ifdef INITZERObnpp_zero_weights(newnet-input_weights,n_in,n_hidden);#elsebnpp_randomize_weights(newnet-input_weights,n_in,n_hidden);#endifbnpp_randomize_weights(newnet-hidden_weights,n_hidden,n_out);bnpp_zero_weights(newnet-input_prev_weights,n_in,n_hidden);bnpp_zero_weights(newnet-hidden_prev_weights,n_hidden,n_out);return(newnet);/*计算从前一层到后一层的输出*/参数：l1-前一层的神经元/l2-后一层的神经元/conn-连接权值/n1-前一层的神经元个数/n2-后一层的神经元个数void bpnn_layerforward(double *l1,double *l2,double *conn,int n1,int n2)double sum;int j,k;/*设置阈值*/l10=1.0;/*对于第二层的每个神经元*/for(j=1;j=n2;j+)/*计算输入的加权总和*/sum=0.0;for(k=0;k=n1;k+)sum+=connkj*l1k;l2j=squash(sum);/*输出误差*/参数：delta-误差/target-目标数组/output-实际输出数组/nj-神经元个数/err-误差综合void bpnn_output_error(double *delta,double *target,double *output,int nj,double *err)int j;double o,t,errsum;errsum=0.0;for(j=1;j=nj;j+)o=outputj;t=targetj;deltaj=o*(1.0-o)*(t-o);errsum+=ABS(deltaj);*err=errsum;/*隐含层误差*/参数：delta_h-隐含层误差数组/nh-隐含层神经元个数/delta_0-输出层误差数组/no-输出层神经元个数/who-隐含层到输出层的连接权值/hidden-隐含层的神经元/err-总误差void bpnn_hidden_error(double *delta_h,int nh,double *delta_o,int no,double *who,double *hidden,double *err)int j,k;double h,sum,errsum;errsum=0.0;for(j=1;j=nh;j+)h=hiddenj;sum=0.0;for(k=1;k=no;k+)sum+=delta_ok*whojk;delta_hj=h*(1.0-h)*sum;errsum+=ABS(delta_hj);*err=errsum;/*调整权值*/参数：delta-误差数组/ndelta-数组长度/w-新权值数组/oldw-旧权值数组/eta-学习速率/momentum-学习动量因子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;for(j=1;j=ndelta;j+)for(k=0;kinput_n;hid=net-hidden_n;out=net-output_n;/*Feed forward input activations.*/bpnn_layerforward(net-input_units,net-hidden_units,net-input_weights,in,hid);bpnn_layerforward(net-hidden_units,net-output_units,net-hidden_weights,hid,out);/*训练BP网络*/参数：net-BP网/eta-学习速率/momentum-学习动量因子/eo-输出层误差/eh-隐含层误差void bpnn_train(BPNN *net,double eta,double momentum,double *eo,double *eh)int in,hid,out;double out_err,hid,err;in=net-input_n;hid=net-hidden_n;out=net-output_n;/*前向输入激活*/bpnn_layerforward(net-input_units,net-hidden_units,net-input_weights,in,hid);bpnn_layerforward(net-hidden_units,net-output_units,net-hidden_weights,hid,out);/*计算隐含层和输出层误差*/bpnn_output_error(net-output_delta,net-target,net-output_units,out,&out_err);bpnn_hidden_error(net-hidden_delta,hid,net-output_delta,out,net-hidden_weights,net-hidden_units,&hid_err);*eo=out_err;*eh=hid_err;/*调整输入层和隐含层权值*/bpnn_adjust_weights(net-output_delta,out,net_hidden_units,hid,net-hidden_weights,net-hidden_prev_weights,eta,momentum);bpnn_adjust_weights(net-hidden_delta,hid,net_input_units,in,net-input_weights,net-input_prev_weights,eta,momentum);/*保存BP网络*/参数：net-待保存的网络/filename-文件名void bpnn_save(BPNN *net,char *filename)int n1,n2,n3,i,j,memcnt;double dvalue, *w;char *mem;FILE *fd;if(fd=fopen(filename,w)=NULL)printf(BPNN_SAVE:Cannot creat %sn,filename);return;n1=net-input_n;n2=net-hidden_n;n3=net-output_n;printf(Saving %dx%dx%dx network to %sn,n1,n2,n3,filename);fflush(stdout);fwrite(char *)&n1,sizeof(int),1,fd);fwrite(char *)&n2,sizeof(int),1,fd);fwrite(char *)&n3,sizeof(int),1,fd);memcnt=0;w=net-input_weights;mem=(char *)malloc(unsigned)(n1+1)*(n2+1)*sizeof(double);for(i=0;i=n1;i+)for(j=0;jhidden_weights;mem=(char *)malloc(unsigned)(n2+1)*(n3+1)*sizeof(double);for(i=0;i=n2;i+)for(j=0;j=n3;j+)dvalue=wij;fastcopy(&memmemcnt,&dvalue,sizeof(double);memcnt+=sizeof(double);fwrite(mem,(n2+1)*(n3+1)*sizeof(double),1,fd);free(mem);fclose(fd);return;/*从文件中读取BP网络*/参数：filename-输入的文件名/返回：BP网络结构BPNN *bpnn_read(char *filename)char *mem;BPNN *new1;int n1,n2,n3,i,j,memcnt;FILE *fd;if (fd=fopen(filename,r)=NULL)return(NULL);printf(Reading%sn,filename);fflush(stdout);fread(char*)&n1,sizeof(i