数学建模社区-数学中国
标题:
大家,各位高手,帮帮一起讨论下我的BP神经网络C语言程序吧,急急急
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作者:
Nevermore.
时间:
2014-7-30 15:36
标题:
大家,各位高手,帮帮一起讨论下我的BP神经网络C语言程序吧,急急急
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#include<stdio.h>
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#include<stdlib.h>
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#include<math.h>
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#include<malloc.h>
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#define TRUE 1
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#define FALSE 0
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#define NUM_LAYERS 3
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#define NUM 124 //训练实例个数
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#define N 5 //输入层单元数
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#define M 1 //输出层单元数
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int Units[NUM_LAYERS] = {N,7,M}; //每层单元数
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FILE *fp,*fb;
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typedef struct //训练实例
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{
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float x[N];
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float y[M];
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}TRAIN;
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typedef struct //网络层结构
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{
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int Units; //该层中单元的个数
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float *Output; //第 i 个单元的输出
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float *Error ; //第 i 个单元的校正误差
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float **Weight; //第 i 个单元的连接权值
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}LAYER;
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typedef struct //网络
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{
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LAYER **Layer; //隐层定义
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LAYER *Inputlayer; //输入层
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LAYER *Outputlayer; //输出层
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float Error; //允许误差
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float Eta; //学习率
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}NET;
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//初始化伪随机数发生器
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void InitializeRandoms()
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{
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srand(4711);
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return;
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}
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//产生随机实数并规范化
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float RandomReal() //产生(-0.5,0.5)之间的随机数
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{
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return (float)(((rand()%200)-100)/200.0);
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}
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//初始化训练数据
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void InitializeTrainingData(TRAIN *training)
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{
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int i,j;
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char filename[20];
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printf("\n请输入训练实例的数据文件名: \n");
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gets(filename);
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fb = fopen(filename,"r");
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fprintf(fp,"\n\n--Saving initialization training datas ...\n");
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for(i=0;i<NUM;i++)
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{
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for(j=0;j<N;j++)
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{
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fscanf(fb,"%f",&(training+i)->x[j]);
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fprintf(fp,"%10.4f",(training+i)->x[j]);
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}
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for(j=0;j<M;j++)
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{
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fscanf(fb,"%f",&(training+i)->y[j]);
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fprintf(fp,"%10.4f",(training+i)->y[j]);
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}
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fprintf(fp,"\n");
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}
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fclose(fb);
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return;
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}
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//应用程序初始化
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void InitializeApplication(NET *Net)
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{
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Net->Eta = (float)0.3;
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Net->Error = (float)0.0001;
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fp = fopen("BPResultData.txt","w+");
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return;
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}
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//应用程序关闭时终止打开的文件
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void FinalizeApplication(NET *Net)
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{
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fclose(fp);
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return;
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}
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//分配内存,建立网络
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void GenerateNetwork(NET *Net)
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{
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int l,i;
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Net->Layer = (LAYER **)calloc(NUM_LAYERS,sizeof(LAYER *));
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for(l=0;l<NUM_LAYERS;l++)
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{
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Net->Layer[l] = (LAYER *)malloc(sizeof(LAYER));
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Net->Layer[l]->Units = Units[l];
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Net->Layer[l]->Output = (float *) calloc(Units[l]+1,sizeof(float));
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Net->Layer[l]->Error = (float *) calloc(Units[l]+1,sizeof(float));
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Net->Layer[l]->Weight = (float **)calloc(Units[l]+1,sizeof(float *));
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Net->Layer[l]->Output[0] = 1;
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if(l != 0)
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for(i=1;i <= Units[l];i++) //下标从"1"开始
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Net->Layer[l]->Weight[i] = (float *)calloc(Units[l-1]+1,sizeof(float));
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}
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Net->Inputlayer = Net->Layer[0];
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Net->Outputlayer = Net->Layer[NUM_LAYERS - 1];
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return;
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}
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//产生随机实数作为初始连接权值
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void RandomWeights(NET *Net)
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{
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int l,i,j;
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for(l=1;l<NUM_LAYERS;l++)
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for(i=1;i <= Net->Layer[l]->Units;i++)
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for(j=0;j <= Net->Layer[l-1]->Units;j++)
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Net->Layer[l]->Weight[i][j] = RandomReal();
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return;
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}
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//设置输入层的输出值
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void SetInput(NET *Net,float *Input)
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{
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int i;
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for(i=1;i <= Net->Inputlayer->Units;i++)
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Net->Inputlayer->Output[i] = Input[i-1]; //输入层采用 u(x) = x
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return;
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}
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//设置输出层的输出值
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void GetOutput(NET *Net,float *Output)
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{
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int i;
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for(i=1;i <= Net->Outputlayer->Units;i++)
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Output[i-1] = (float)(1/(1 + exp(-Net->Outputlayer->Output[i]))); //输出层采用 f(x)=1/(1+e^(-x))
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return;
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}
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//层间顺传播
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void PropagateLayer(NET *Net,LAYER *Lower,LAYER *Upper)
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{
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int i,j;
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float sum;
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for(i=1;i <= Upper->Units;i++)
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{
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sum = 0;
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for(j=1;j <= Lower->Units;j++)
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sum += (Upper->Weight[i][j] * Lower->Output[j]);
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Upper->Output[i] = (float)(1/(1 + exp(-sum)));
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}
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return;
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}
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//整个网络所有层间的顺传播
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void PropagateNet(NET *Net)
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{
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int l;
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for(l=0;l < NUM_LAYERS-1;l++)
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PropagateLayer(Net,Net->Layer[l],Net->Layer[l+1]);
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return;
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}
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//计算输出层误差
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void ComputeOutputError(NET *Net,float *target)
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{
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int i;
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float Out,Err;
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for(i=1;i <= Net->Outputlayer->Units;i++)
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{
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Out = Net->Outputlayer->Output[i];
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Err = target[i-1] - Out;
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Net->Outputlayer->Error[i] = Out*(1-Out)*Err;
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}
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return;
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}
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//层间逆传播
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void BackpropagateLayer(NET *Net,LAYER *Upper,LAYER *Lower)
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{
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int i,j;
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float Out,Err;
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for(i=1;i <= Lower->Units;i++)
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{
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Out = Lower->Output[i];
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Err = 0;
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for(j=1;j <= Upper->Units;j++)
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Err += (Upper->Weight[j][i] * Upper->Error[j]);
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Lower->Error[i] = Out*(1-Out)*Err;
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}
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return;
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}
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//整个网络所有层间的逆传播
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void BackpropagateNet(NET *Net)
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{
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int l;
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for(l=NUM_LAYERS-1;l>1;l--)
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BackpropagateLayer(Net,Net->Layer[l],Net->Layer[l-1]);
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return;
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}
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//权值调整
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void AdjustWeights(NET *Net)
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{
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int l,i,j;
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float Out,Err;
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for(l=1;l<NUM_LAYERS;l++)
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for(i=1;i <= Net->Layer[l]->Units;i++)
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for(j=0;j <= Net->Layer[l-1]->Units;j++)
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{
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Out = Net->Layer[l-1]->Output[j];
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Err = Net->Layer[l]->Error[i];
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Net->Layer[l]->Weight[i][j] += (Net->Eta*Err*Out);
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}
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return;
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}
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//网络处理过程
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void SimulateNet(NET *Net,float *Input,float *Output,float *target,int TrainOrNot)
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{
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SetInput(Net,Input); //输入数据
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PropagateNet(Net); //模式顺传播
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GetOutput(Net,Output); //形成输出
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ComputeOutputError(Net,target); //计算输出误差
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if(TrainOrNot)
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{
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BackpropagateNet(Net); //误差逆传播
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AdjustWeights(Net); //调整权值
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}
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return;
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}
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//训练过程
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void TrainNet(NET *Net,TRAIN *training)
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{
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int l,i,j,k;
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int count=0,flag=0;
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float Output[M],outputfront[M],ERR,err,sum;
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do
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{
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flag = 0;
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sum = 0;
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ERR = 0;
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if(count >= 1)
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for(j=0;j<M;j++)
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outputfront[j]=Output[j];
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SimulateNet(Net,(training+(count%NUM))->x,Output,(training+(count%NUM))->y,TRUE);
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if(count >= 1)
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{
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k = count%NUM;
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for(i=1;i <= Net->Outputlayer->Units;i++)
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{
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sum += Net->Outputlayer->Error[i];
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err = (training+k-1)->y[i-1] - outputfront[i-1];
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ERR += (outputfront[i-1] * (1 - outputfront[i-1]) * err);
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}
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if(sum <= ERR)
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Net->Eta = (float)(0.9999 * Net->Eta);
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else
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Net->Eta = (float)(1.0015 * Net->Eta);
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}
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if(count >= NUM)
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{
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for(k=1;k <= M;k++)
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if(Net->Outputlayer->Error[k] > Net->Error)
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{ flag=1; break; }
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if(k>M)
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flag=0;
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}
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count++;
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}while(flag || count <= NUM);
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fprintf(fp,"\n\n\n");
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fprintf(fp,"--training results ... \n");
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fprintf(fp,"training times: %d\n",count);
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fprintf(fp,"\n*****the final weights*****\n");
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for(l=1;l<NUM_LAYERS;l++)
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{
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for(i=1;i <= Net->Layer[l]->Units;i++)
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{
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for(j=1;j <= Net->Layer[l-1]->Units;j++)
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fprintf(fp,"%15.6f",Net->Layer[l]->Weight[i][j]);
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fprintf(fp,"\n");
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}
~, o8 ]* I/ y/ t! }
fprintf(fp,"\n\n");
8 h7 _3 N( w; H! r
}
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}
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* o9 y- v8 G9 c* I+ E. }% [
//评估过程
5 I& n0 l/ Z7 t( D/ g/ w p
void EvaluateNet(NET *Net)
5 h: n9 O+ j8 W
{
; \' z' c$ G/ f8 K% f
int i;
& s2 E/ j/ p4 t$ G
printf("\n\n(");
4 g/ v" y7 U( V( o1 b9 l& x, U6 h
fprintf(fp,"\n\n(");
) h7 H2 U) f0 M# z( K
for(i=1;i <= Net->Inputlayer->Units;i++)
9 m( J' B8 }6 H# o1 T0 a
{
4 g1 N2 k. l+ `: ]1 `; N
printf(" %.4f",Net->Inputlayer->Output[i]);
/ m6 y6 M. Y* ?# \1 ~9 q E4 J! x2 r' ]
fprintf(fp,"%10.4f",Net->Inputlayer->Output[i]);
5 |* |4 \9 t/ [; C
}
- p6 ^1 u5 N& W* ?6 G. f
printf(")\t");
# ~" a. b7 g# e' z7 c
fprintf(fp,")\t");
& x+ I4 z3 ]* _" K; p( T
for(i=1;i <= Net->Outputlayer->Units;i++)
& S) ]$ O( T9 p
{
2 e- r2 `/ a* b5 V. J7 n
if(fabs(Net->Outputlayer->Output[i] - 1.0) <= 0.0499)
$ N8 |& E1 I- c
{
) ^3 Y1 {7 k. B6 ~
printf("肯定是第 %d 类, ",i);
) ]1 J- B, j) \3 K1 o; S9 h4 G
fprintf(fp,"肯定是第 %d 类, ",i);
( f3 M* @+ C1 j/ \. y! z2 b |
}
5 X' c P/ f1 ~" \1 o
if(fabs(Net->Outputlayer->Output[i] - 0.9) <= 0.0499)
- `: @; V. a7 N6 R4 t
{
+ I9 W8 D( Z: ]1 t/ w/ I
printf("几乎是第 %d 类, ",i);
7 f; {/ k T+ @; e
fprintf(fp,"几乎是第 %d 类, ",i);
" G* v- i8 H- b3 f! {. I
}
: j! {& p- i! ]" J3 L0 Z
if(fabs(Net->Outputlayer->Output[i] - 0.8) <= 0.0499)
) V+ W$ T% |/ Y! H/ r4 R
{
7 e% v' U8 j! [) M9 l% H
printf("极是第 %d 类, ",i);
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fprintf(fp,"极是第 %d 类, ",i);
; d2 J/ h2 ?' I: G
}
# }* j! q$ w# f/ o' l
if(fabs(Net->Outputlayer->Output[i] - 0.7) <= 0.0499)
6 Q& D7 K2 w3 n+ f
{
7 \6 t& S, K( Q0 u9 ]
printf("很是第 %d 类, ",i);
* i7 ^: K9 q5 D+ e3 L* x
fprintf(fp,"很是第 %d 类, ",i);
2 M7 r1 S6 L+ c Y( n8 p
}
5 ~( i8 b# l( Y
if(fabs(Net->Outputlayer->Output[i] - 0.6) <= 0.0499)
! r. C! j. h$ u2 D/ _
{
5 c5 F O8 d) z7 C" p- Q
printf("相当是第 %d 类, ",i);
* l* k2 [* n7 f9 H. X
fprintf(fp,"相当是第 %d 类, ",i);
; b3 t1 ?- N. ~1 I6 W
}
% m& Y% b' D( [6 H
if(fabs(Net->Outputlayer->Output[i] - 0.5) <= 0.0499)
0 S: A. m& N! e
{
7 z, K! Q" w; h, ]) q
printf("差不多是第 %d 类, ",i);
+ a& O6 |7 P3 g0 {& z6 G* L
fprintf(fp,"差不多是第 %d 类, ",i);
" y( b; e: V5 p* S9 Y7 W2 p; n" w
}
$ J% N# s7 d; \" N& f& j) s
if(fabs(Net->Outputlayer->Output[i] - 0.4) <= 0.0499)
8 w, a& A. P q! x5 g K2 A
{
K' G3 _& m. U- z! a
printf("比较像是第 %d 类, ",i);
9 d; P2 s6 O. `& K U1 b7 ], g
fprintf(fp,"比较像是第 %d 类, ",i);
* x m& h+ ]: T6 ?5 l
}
! w, [3 B8 |2 \4 \# c
if(fabs(Net->Outputlayer->Output[i] - 0.3) <= 0.0499)
! A7 L/ a6 \: k" t$ d M
{
# g, @: ~* O* z5 A4 ?. K4 A
printf("有些像是第 %d 类, ",i);
C! N: B5 Y, [
fprintf(fp,"有些像是第 %d 类, ",i);
6 k1 p9 N: `5 r0 }( j
}
) \+ n, k! g. H2 _2 X+ {! }4 S. U
if(fabs(Net->Outputlayer->Output[i] - 0.2) <= 0.0499)
" B7 f, ~2 S2 f0 j8 H, l. m, f
{
) N9 e! w9 j" [# @& a8 B
printf("有点像是第 %d 类, ",i);
5 w3 z. @0 j2 ^# l
fprintf(fp,"有点像是第 %d 类, ",i);
3 d- t( g. v1 {2 R6 ]) _3 |
}
* c* {' e1 G& e) \$ D
if(fabs(Net->Outputlayer->Output[i] - 0.1) <= 0.0499)
; S, d% @ U- }& S# v6 @% _$ Y" p2 Q% t
{
; } m5 U& j& w+ i [! `2 t( a
printf("稍稍像是第 %d 类, ",i);
' i% O/ X: ^3 H& g
fprintf(fp,"稍稍像是第 %d 类, ",i);
* x/ {% M3 T/ O: r
}
4 ^5 Y: z5 W8 x0 b3 t# i
if(Net->Outputlayer->Output[i] <= 0.0499)
5 {5 {) E. Q; r$ f2 ?
{
* ~. K1 [2 c8 {. T4 A5 R
printf("肯定不是第 %d 类, ",i);
" c; G9 {/ Y2 Z- |5 e' p- B
fprintf(fp,"肯定不是第 %d 类, ",i);
P$ H1 R0 A# q z! l: _1 a9 ]
}
% \: x9 Z+ R6 G. Y9 o
}
' ?- @% _6 c7 J$ a4 |* f6 G" g' u% ^
printf("\n\n");
( q7 I& [8 ]% U* _2 F% j
fprintf(fp,"\n\n\n");
, o6 p; L) G. i6 m! \
return;
9 \1 P, Y' b' `- O. c( b o
}
7 q4 t! ?. b3 A- c I3 G
" l6 t" {& ^6 E2 M" f( ?1 a
//测试过程
3 f* `+ H4 N" P" M# d9 ?6 P' ~
void TestNet(NET *Net)
& A( p+ E! P! e7 A- v$ p! J# K2 B
{
; o6 J; ]8 V ^( X) E+ `! c- E
TRAIN Testdata;
' h" r# H* q- w% Z/ q8 p; Y
float Output[M];
' L, t M5 X6 I
int i,j,flag=0;
2 u, ~6 W" P* t8 q) C8 Q4 {' ]% P
char select;
% o+ p8 w( v6 ^ ?. c# t1 V
fprintf(fp,"\n\n--Saving test datas ...\n");
6 {: I9 H; z0 ]! {( y7 `
do
! A7 [' M1 G# h }# q% Y7 K* {
{
0 C/ U1 D3 ^, D+ Z+ C0 k6 _
printf("\n请输入测试数据(x1,x2,x3,x4,x5,y): \n");
. f' I% V8 f1 {# y6 r5 g
for(j=0;j<N;j++)
0 R$ U4 R4 u" s
{
) V& J; E: M: Q2 S$ N& K
scanf("%f",&Testdata.x[j]);
R2 o. J6 [0 D9 ^
fprintf(fp,"%10.4f",Testdata.x[j]);
% g: w$ P# N1 P. K- A
}
z& i6 _) O0 }" Y" i6 a3 p
for(j=0;j<M;j++)
- e- L" c: J; V
{
2 v" x1 d L1 C1 g& w
scanf("%f",&Testdata.y[j]);
# _7 k$ U5 Z* y$ q3 \: y
fprintf(fp,"%10.4f",Testdata.y[j]);
, f/ R6 u, h" N& J6 Y; Q7 u2 r
}
8 }+ s1 {6 Q+ t9 U y1 T+ v
fprintf(fp,"\n");
. F6 Z, G7 u( }! {/ |/ h3 _
SimulateNet(Net,Testdata.x,Output,Testdata.y,FALSE);
4 h) X4 \/ @! ~+ y; r
fprintf(fp,"\n--NET Output and Error of the Test Data ....\n");
0 w& i$ x# M" O5 Z: [2 V
for(i=1;i <= Net->Outputlayer->Units;i++)
0 j) W0 E/ J4 F1 Y
fprintf(fp,"%10.6f %10.6f\n",Net->Outputlayer->Output[i],Net->Outputlayer->Error[i]);
, T5 p- F& s3 L& D( ?1 g3 B' j" I
EvaluateNet(Net);
* g0 s# h4 Z' k W/ j% q# `4 W
printf("\n继续测试?(y/n):\n");
- w; m5 S1 Q3 i- Y* K
getchar();
8 R* m4 O( K+ O
scanf("%c",&select);
4 A+ @) E) u7 o$ t7 ~' V/ D
printf("\n");
2 M( s) }: m7 C+ ^! u+ X
if((select == 'y')||(select == 'Y'))
$ a+ H0 o0 r! n8 [, z @
flag = 1;
- I5 {6 P" N U, f& ^* |+ g6 f
else
' V3 K) G4 d$ q9 ^% c, {
flag=0;
; `) w+ e* ~! F6 |
}while(flag);
2 W/ c& F6 c: d4 g$ e
return;
- s; B# e" v1 K: Z
}
; Z% N6 J G( o& C
* `' A& ~& N9 k8 g0 I
. S0 M0 J6 {* \* b8 H
void OUTPUT(NET *Net)
# x, d) \7 _) Q4 N+ n
{
$ C2 \; [3 B4 s7 U: j4 P8 F
float a[NUM_LAYERS][9]={0.0};
: x& Y$ a8 b. @! C1 [; h! t
float b[NUM_LAYERS][9]={0.0};
$ I! }) k% f, \4 F/ G
float sum1[NUM_LAYERS][9]={0.0};
' ]% v. \0 o: J# N* `/ G+ }, c
float sum2[NUM_LAYERS][9]={0.0};;
" A( N# }3 L$ C4 W3 y/ ]
float test[N];
, Z0 U7 O3 W! ~1 {% l" `
//int i,j,k;
* R) h( I$ }9 j" L
fprintf(fp,"\n\n--true input datas ...\n");
, L- c- ~3 [ s" k1 E# u
printf("\n请输入要判别岩性的自然伽马值、密度值、中子值、声波时差值、深电阻率值:\n\n");
- G6 m6 j/ h1 w% d5 g
for(int i=0;i<N;i++)
9 V) L: [& W* ~% z) ]: h
{
$ l+ j" E3 G& g
scanf("%f",&test[i]);
* Y2 P& N1 h7 k j% g1 S
fprintf(fp,"%10.4f",test[i]);
p% L! Z' Z) I+ [* b
}
1 m5 P9 P/ S' C, S3 u T* J
7 ?. l% J- F5 y/ H# g
% S' V1 Y) J1 u) O* {& q5 u
for(int l=1;l<NUM_LAYERS;l++)
. J! t' c* ? t- L& ~
{
, u6 u, [( R% L% ]. i
if(l==1)
) F- |+ I# \: q1 A' t' q
{
6 s$ L2 ]7 G8 s! Q* S) J7 ?6 T
for(int i=1;i <= Net->Layer[l]->Units;i++)
7 e( {* O$ T# X# K. q
{
. m' p2 S4 m& d
for(int j=1;j <= Net->Layer[l-1]->Units;j++)
2 L' L( i8 j: w# c% S2 z3 G1 s
(float)sum1[l][i-1]+=test[j-1]*Net->Layer[l]->Weight[i][j];
; u/ n" S3 |7 H4 Q
(float)a[l][i-1]=1/(1+exp(-sum1[l][i-1]));
& }' y& x$ c( }4 u& W2 q
}
" E7 U; C( S* _9 I
printf("\n");
! z: D+ g( {% G, Y% Q4 E, A
}
2 A% W% s4 E1 k t
+ O8 D, ~7 e$ n! f( P% a4 P4 X, D
if(l==2)
9 e8 v6 U; o* j T& ]5 N0 B- p
{
+ I9 r4 D% l8 G5 G g+ l2 A
for(int i=1;i <= Net->Layer[l]->Units;i++)
* B7 z' ], C( k! G1 K
{
) D* s: }/ M$ @
for(int j=1;j <= Net->Layer[l-1]->Units;j++)
/ M( I* X& s" ~- g2 P1 D/ n7 V
(float)sum1[l][i-1]+=a[l-1][j-1]*Net->Layer[l]->Weight[i][j];
2 E. N! T: s( g/ Y: k3 @
(float)a[l][i-1]=1/(1+exp(-sum1[l][i-1]));
' x$ G3 t ^6 G5 J" ^' E
printf("%f\t",a[l][i-1]);
f/ }4 l. N+ `4 Z
}
8 ~6 J1 Q. N) k" a e/ D. D% X
}
) B9 e+ t' ^, g0 E0 K6 j
}
: \* b1 e. X! e. _; q
}
5 u# X, ^2 H" f% [; s3 v- i
. K# M7 S8 n& R% N
) W2 O. I2 c1 Y( Y
//主函数
9 i, q4 u! O, f; @) ~. q/ ~; [& O# b
void main()
6 `: G7 E0 t' s2 x0 B- `' p
{
5 E, p+ P$ B9 I" o( Z% f6 ~. X
TRAIN TrainingData[NUM];
: R6 A+ N9 K2 O( Q5 {2 y: X
NET Net;
4 y" _9 H( |0 H6 m L) S- ~/ d$ l
InitializeRandoms(); //初始化伪随机数发生器
2 R# t( k6 A! B( E! {6 G- E- c/ B
GenerateNetwork(&Net); //建立网络
" n7 [7 V" L) o2 k
RandomWeights(&Net); //形成初始权值
/ Q$ P4 A: f' h P
InitializeApplication(&Net); //应用程序初始化,准备运行
' n! c( @# c% Y1 z5 U5 N$ c# M
InitializeTrainingData(TrainingData); //记录训练数据
( b B& c' e+ l7 i1 m' {$ i- G( P
TrainNet(&Net,TrainingData); //开始训练
2 i. \; u5 P; }2 X. j- q" E( h: M
TestNet(&Net);
) B: x& o$ M) h8 [' i
OUTPUT(&Net);
. g, a0 g( P2 X; d5 T: e) D9 n
FinalizeApplication(&Net); //程序关闭,完成善后工作
# N9 K3 p$ R3 R9 D) d
return;
! F0 u: D: r4 w' {. N6 e! G
}
0 H6 }1 d' M' Q2 t3 y( J3 J
7 b- ?, P" _8 W+ V. u6 z
# L S: y, U: K5 {
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