C++ 实现全连接神经网络算法识别 Mnist 手写数字

/** * @Author: fengqi * @Email: 2607546441@qq.com */ #include <iostream> #include <time.h> #include <string> #include <opencv2/opencv.hpp> #include "network.h" using namespace std; using namespace cv; void saveWeight(string file,Network *network){ //保存各层权重到文件 ofstream outfile(file); for(int i=0;i<network->mNumLayers;i++){ Layer *layer=network->mLayers[i]; for(int m=0;m<layer->mNumNodes;m++){ for(int n=0;n<layer->mNumInputNodes+1;n++){ outfile<<layer->mWeights[m][n]<<" "; } } } outfile.close(); cout<<"save weight file to <"<<file<<"> done."<<endl; } void loadWeight(string file,Network *network){ //加载权重文件 ifstream infile(file); if(!infile.is_open()){ cout<<"open weight file failed!"<<endl; exit(-1); } for(int i=0;i<network->mNumLayers;i++){ Layer *layer=network->mLayers[i]; for(int m=0;m<layer->mNumNodes;m++){ for(int n=0;n<layer->mNumInputNodes+1;n++){ infile>>layer->mWeights[m][n]; } } } infile.close(); cout<<"load weight from <"<<file<<"> done."<<endl; } float train(Network *network,string path,int imageSize, int numImages) //训练网络，使用训练数据集 { srand(time(0)); float *temp = new float[imageSize]; string la=path; ifstream labelFile(path.append("trainLabel.txt")); //标签文件 int label; for (int i = 0; i < numImages; i++) { if(i%(numImages/10)==0){ //每 6000 张图片统计错误率，并显示训练进度 network->mErrorSum=0; cout << setfill('=') << setw(2) << ">"<<(i/(numImages/10))*10<<"%"<<flush; } if(i==numImages-1) cout<<"====>100%"<< endl; int k=rand()%numImages; //随机选取图片训练 string l=la; Mat x=imread(l.append(to_string(k)).append(".jpg"),0); //使用 opencv 读取图片 if(!x.data){cout<<"read image error."<<endl;return -1;} for(int m=0;m<x.rows;m++){ for(int n=0;n<x.cols;n++){ float a=(x.at<uchar>(m,n))/255.0; //归一化 temp[m*x.cols+n]=a; } } labelFile.seekg(2*k); //标签和图片对应 labelFile>>label; network->compute(temp,label); //每次训练一张图片 } cout << "the error is:" << network->mErrorSum/(numImages/10); labelFile.close(); delete [] temp; return network->mErrorSum; } int validate(Network *network,string path,int imageSize, int numImages) //验证网络准确率，使用测试数据集 { int ok_cnt = 0; float* temp = new float[imageSize]; string la=path; ifstream labelFile(path.append("testLabel.txt")); //标签文件 int label,idx=0; for (int i = 0; i < numImages; i++) { if(i%(numImages/10)==0) //显示进度 cout << setfill('=') << setw(2) << ">"<<(i/(numImages/10))*10<<"%"<<flush; if(i==numImages-1) cout<<"====>100%"<< endl; string l=la; Mat x=imread(l.append(to_string(i)).append(".jpg"),0); //按顺序读取图片测试 if(!x.data){cout<<"read image error."<<endl;return -1;} for(int m=0;m<x.rows;m++){ for(int n=0;n<x.cols;n++){ float a=(x.at<uchar>(m,n))/255.0; //归一化 temp[m*x.cols+n]=a; } } labelFile>>label; network->compute(temp,label); //验证 float *out=network->mOutputs; //获得计算输出 float max_value = -9999; for (int i = 0; i < network->mNumOutputs; i++) { if (out[i] > max_value) { max_value = out[i]; //最大输出位置即图片所属类别 idx = i; } } if (idx == label) //判断与标签是否相同，即预测是否准确 { ok_cnt++; } } labelFile.close(); delete [] temp; return ok_cnt; } int main(int argc, char* argv[]) //主入口函数 { if(argc<2||(strcmp(argv[1],"train")!=0&&strcmp(argv[1],"test")!=0)){ //判断调用参数是否合法 cout<<"usage: ./run [train/test] [weight_file]\nwrong parameter!!!"<<endl; return -1; } bool load_weight=false; //是否加载权重文件 int imageRow=28,imageCol=28; //输入图片大小 int imageSize=imageRow*imageCol; int trainNumImages = 60000; //训练集大小 int testNumImages = 10000; //测试集大小 int networkInputs=imageSize; //网络参数设置 int networkOutputs=10; int epoches=10; float learningRate=0.1; Network *network = new Network(epoches,learningRate,networkInputs,networkOutputs); network->addLayer(256,SIGMOID); //加入全连接层，参数有神经元个数和激活函数类型 network->addLayer(128,SIGMOID); network->addLayer(network->mNumOutputs,SIGMOID); cout <<"\nnetwork framework: (input)"<< network->mNumInputs; for(int i=0;i<network->mNumLayers;i++){ cout<<"=>"<<network->mLayers[i]->mNumNodes; } cout<<"(output)"<<endl<<endl; if(argc>2){ //加载预训练权重文件 loadWeight(argv[2],network); load_weight=true; } if(strcmp(argv[1],"train")==0) //训练网络 { time_t time0=time(0); cout<<"start training..."<<endl<<endl; cout<<"total epoches: "<<network->mEpoches<<", NO.1 epoches. begin learning rate: "<<network->mLearningRate<< endl; for(int i = 0; i < network->mEpoches; i++) //共训练 epoches 轮次 { string weightFile="backup/mnist.weight_"; time_t time1=time(0); network->mTrain=true; //训练标志 cout<<"\nep: "<<i+1<<", lr: "<<network->mLearningRate<<" "; float err = train(network,"trainData/",imageSize,trainNumImages); //开始训练 cout<<", cost time: "<<time(0)-time1<<" seconds."<<endl; network->mTrain=false; //验证测试标志 cout<<"\nvalidate..."; int ok = validate(network,"testData/",imageSize,testNumImages); //开始验证 cout<<"validate accuracy: "<<(float)ok/testNumImages*100<< "%, true: "<<ok<<", total: "<<testNumImages<< endl; if(network->mLearningRate>0.01) network->mLearningRate-= 0.01; //学习率变化调整 else network->mLearningRate=0.01; if(i<network->mEpoches-1) saveWeight(weightFile.append(to_string(i+1)).append("_").append(to_string(ok)),network); //一轮训练结束，保存权重文件 } saveWeight("mnist.weight",network); //网络训练结束，保存权重文件 cout<<"\ntraining network success...cost time: "<<(time(0)-time0)<<" seconds.\n"<<endl; } else if(strcmp(argv[1],"test")==0) //测试预测图片 { if(!load_weight){ //必须先加载网络权重 cout<<"no weight file loaded in, can't start prediction.\n"<<endl; return -1; } string name; while(1){ //循环测试图片 cout<<"\nplease enter the image path...(ctrl-c to exit.)"<<endl; getline(cin,name); //输入图片名 Mat m=imread(name,0); //使用 opencv 读入图片 if(!m.data){ cout<<"read image wrong. please check image file name..."<<endl; continue; } network->mTrain=false; if(m.cols!=imageCol||m.rows!=imageRow) resize(m,m,Size(imageCol,imageRow)); //resize 图片到网络接受输入大小 float *d=new float[imageSize]; for(int i=0;i<imageRow;i++){ for(int j=0;j<imageCol;j++){ float x=(m.at<uchar>(i,j))/255.0; //将二维像素值转成一维向量，并归一化 d[i*imageCol+j]=x; } } float max=-9999; int idx=10