神经网络 opencv实现

我们使用了opencv2.7.13用C++实现的，参考自《Neural Networks and Deep Learning》一书，使用的数据是网上下载的一些图片，大家可以自行查找。

建议大家看了上面提到的那本书后再看本代码，本代码和原书用python实现的代码有类似之处，大体框架仿照了原书的代码，但是额外增加了一些内容，大家可以对比查看区别。

另外，欢迎指正本代码的不足之处。

#include<cstdio>#include<iostream>#include<cv.h>#include<cmath>#include<highgui.hpp>#include<core.hpp>#include<imgproc.hpp>#include<algorithm>#include<ctime>using namespace std;using namespace cv;struct Traning_data{    Mat x;    Mat y;};class Network{public:    //读取训练数据和测试数据    void read_data();    /* sizes 指定神经网络每一层的神经元数量       eta    训练速度       cost 指定成本函数C，1代表使用交叉商，2代表使用二次成本函数       lmbda 指定L2正则化参数，默认为0，不使用       dropout 指定是否使用dropout，1为不使用，否则请指定0到1的实数，代表有多少比例的参数在每个周期参与训练，1-dropout的参数不参与训练    */    void init(vector<int> &sizes,double _eta,int _cost=1,double _lmbda=0.0,double _dropout=1.0);    /*       epchos 训练周期       mini_batch_num   批量计算数量       track_cost   是否持续跟踪训练数据，若为true，则每个周期训练结束对网络进行测试       test 指定是否跟踪测试，若为true，则每个周期训练结束对网络进行测试    */    void SGD(int epchos,int mini_batch_num,bool track_cost=false,bool test=false);private:    vector<Mat>weights;//权重集合    vector<Mat>bases;//偏移集合    vector<Traning_data>test_data;//测试数据    vector<Traning_data>traning_data;//训练数据    int traning_num;//训练数据数量    int test_num;//测试数据数量    double eta;//学习效率    int cost;//成本函数类型    double lmbda;//L2正则化系数    double dropout;//屏蔽系数    Mat feedforward(Mat &x);//前向传播    void weights_init();//初始化权重    void updata_mini_data(vector<Traning_data>mini_batch);//部分训练数据训练    int accury(vector<Traning_data>&_data);    //统计目前成功率    double total_cost(vector<Traning_data>&_data);//计算目前成本函数值    int getMaxnum(Mat &t);//返回t向量中最大的值对应的数字    void backprop(Mat &batch_traning_x,Mat &batch_traning_y,vector<Mat>& nabla_w,vector<Mat>& nabla_b);//反向传播大法    Mat drop_out(int rows,int cols);//返回dropout矩阵    Mat sigmoid(Mat &z);    Mat sigmoid_prime(Mat &z);};Mat Network::feedforward(Mat &x){    int i;    Mat y=x;    for(i=0;i<weights.size();i++)    {        y=weights[i]*y+bases[i];        y=sigmoid(y);    }    return y;}void Network::read_data(){    int i,j;    for(i=0;i<10;i++)        for(j=1;j<=500;j++)        {            String c;            c="data\\"+to_string(i)+"\\"+to_string(i)+"_"+to_string(j)+".bmp";            Mat img=imread(c,IMREAD_GRAYSCALE);            if(!img.data)                continue;            Traning_data _traning_data;            _traning_data.x.create(img.rows*img.cols,1,CV_64FC1);            int num=0;            int k,l;            for(k=0;k<img.rows;k++)                for(l=0;l<img.cols;l++)                {                    _traning_data.x.at<double>(num,0)=(double)img.at <uchar>(k,l);                    num++;                }            _traning_data.x=_traning_data.x/255.0;            _traning_data.y=Mat::zeros(10,1,CV_64FC1);            _traning_data.y.at<double>(i,0)=1.0;            if(j<=400)            {                traning_data.push_back(_traning_data);            }            else            {                test_data.push_back(_traning_data);            }        }    traning_num=traning_data.size();    test_num=test_data.size();    cout<<"traning_data_num="<<traning_num<<endl;    cout<<"test_data_num="<<test_num<<endl;}void Network::weights_init(){    int k;    int i,j;    RNG rng(time(0)%100);    for(k=0;k<bases.size();k++)    {        double sqrt_w_cols=sqrt(weights[k].cols);        for(i=0;i<bases[k].rows;i++)            for(j=0;j<bases[k].cols;j++)                bases[k].at<double>(i,j)=rng.gaussian(1.0/sqrt_w_cols);        for(i=0;i<weights[k].rows;i++)            for(j=0;j<weights[k].cols;j++)                weights[k].at<double>(i,j)=rng.gaussian(1.0/sqrt_w_cols);    }}void Network::init(vector<int> &sizes,double _eta,int _cost,double _lmbda,double _dropout){    eta=_eta;    cost=_cost;    lmbda=_lmbda;    dropout=_dropout;    int i;    for(i=1;i<sizes.size();i++)    {        Mat _w;        Mat _b;        _w.create(sizes[i],sizes[i-1],CV_64FC1);        _b.create(sizes[i],1,CV_64FC1);        weights.push_back(_w);        bases.push_back(_b);    }    weights_init();}void Network::SGD(int epchos,int mini_batch_num,bool track_cost,bool test){    int i,j;    for(i=0;i<epchos;i++)    {        random_shuffle(traning_data.begin(),traning_data.end());        for(j=0;j<traning_num;j+=mini_batch_num)        {            vector<Traning_data>mini_batch;            if(j+mini_batch_num<=traning_num)            {                mini_batch.assign(traning_data.begin()+j,traning_data.begin()+j+mini_batch_num);                updata_mini_data(mini_batch);            }        }        cout<<"Epoch "<<i+1<<" training complete"<<endl;        if(track_cost)        {            int correct_traning_num=accury(traning_data);            double total_traning_cost=total_cost(traning_data);            cout<<"    Cost on traning data:"<<total_traning_cost<<endl;            cout<<"    Accuracy on traning data:"<<correct_traning_num<<"/"<<traning_num<<endl;        }        if(test)        {            int correct_test_num=accury(test_data);            double total_test_cost=total_cost(test_data);            cout<<"    Cost on test data:"<<total_test_cost<<endl;            cout<<"    Accuracy on test data:"<<correct_test_num<<"/"<<test_num<<endl;        }    }}int Network::accury(vector<Traning_data>&_data){    int ans=0;    int i;    for(i=0;i<_data.size();i++)        if(getMaxnum(feedforward(_data[i].x))==getMaxnum(_data[i].y))            ans++;    return ans;}int Network::getMaxnum(Mat &t){    int ans=0;    int i;    for(i=1;i<t.rows;i++)        if(t.at<double>(i,0)>t.at<double>(ans,0))            ans=i;    return ans;}double Network::total_cost(vector<Traning_data>&_data){    double ans=0;    int i;        Mat t;    if(cost==2)    {        for(i=0;i<_data.size();i++)        {            pow(feedforward(_data[i].x)-_data[i].y,2.0,t);            ans+=0.5*sum(t).val[0];        }        ans/=2.0*_data.size();    }    else if(cost==1)    {        Mat t1,t2;        for(i=0;i<_data.size();i++)        {            t=feedforward(_data[i].x);            log(t,t1);            log(1-t,t2);            ans+=sum(_data[i].y.mul(t1)+(1-_data[i].y).mul(t2)).val[0];        }        ans/=-1.0*_data.size();    }    if(lmbda!=0.0)    {        for(i=0;i<weights.size();i++)        {            pow(weights[i],2.0,t);            ans+=lmbda/_data.size()/2.0*sum(t).val[0];        }    }    return ans;}void Network::updata_mini_data(vector<Traning_data>mini_batch){    vector<Mat>nabla_w;    vector<Mat>nabla_b;    int i;    for(i=0;i<weights.size();i++)    {        Mat _w,_b;        _w.create(weights[i].rows,weights[i].cols,CV_64FC1);        _b.create(bases[i].rows,bases[i].cols,CV_64FC1);        nabla_w.push_back(_w);        nabla_b.push_back(_b);    }    Mat batch_traning_data_x;    Mat batch_traning_data_y;    batch_traning_data_x.create(mini_batch[0].x.rows,mini_batch.size(),CV_64FC1);    batch_traning_data_y.create(mini_batch[0].y.rows,mini_batch.size(),CV_64FC1);    for(i=0;i<mini_batch.size();i++)    {        mini_batch[i].x.col(0).copyTo(batch_traning_data_x.col(i));        mini_batch[i].y.col(0).copyTo(batch_traning_data_y.col(i));    }    backprop(batch_traning_data_x,batch_traning_data_y,nabla_w,nabla_b);    for(i=0;i<weights.size();i++)    {        weights[i]=(1.0-eta*lmbda/traning_num)*weights[i]-eta/mini_batch.size()*nabla_w[i];        bases[i]=bases[i]-eta/mini_batch.size()*nabla_b[i];    }}void Network::backprop(Mat &batch_traning_x,Mat &batch_traning_y,vector<Mat>& nabla_w,vector<Mat>& nabla_b){    vector<Mat>as;    vector<Mat>zs;    Mat a=batch_traning_x;    as.push_back(a);    int i,j;    for(i=0;i<weights.size();i++)    {        Mat z=weights[i]*a;        for(j=0;j<z.cols;j++)            z.col(j)+=bases[i];        zs.push_back(z);        a=sigmoid(z);        as.push_back(a);    }    Mat delta;    if(cost==1)        delta=as[as.size()-1]-batch_traning_y;    else if(cost==2)        delta=(as[as.size()-1]-batch_traning_y)*sigmoid_prime(zs[zs.size()-1]);    nabla_w[nabla_w.size()-1]=delta*as[as.size()-2].t();    reduce(delta,nabla_b[nabla_b.size()-1],1,CV_REDUCE_SUM);    for(i=2;i<=weights.size();i++)    {        delta=(weights[weights.size()-i+1].t()*delta).mul(sigmoid_prime(zs[zs.size()-i]));        nabla_w[nabla_w.size()-i]=delta*as[as.size()-i-1].t();        reduce(delta,nabla_b[nabla_b.size()-i],1,CV_REDUCE_SUM);    }    if(dropout<1.0)    {        Mat drop_out_mat;        for(i=0;i<nabla_w.size();i++)        {            drop_out_mat=drop_out(nabla_w[i].rows,nabla_w[i].cols);            nabla_w[i]=nabla_w[i].mul(drop_out_mat);            drop_out_mat=drop_out(nabla_b[i].rows,nabla_b[i].cols);            nabla_b[i]=nabla_b[i].mul(drop_out_mat);        }    }}Mat Network::drop_out(int rows,int cols){    Mat ans;    RNG rng(time(0)%100);    ans.create(rows,cols,CV_64FC1);    int i,j;    for(i=0;i<rows;i++)        for(j=0;j<cols;j++)        {            double t=rng.uniform(double(0.0),double(1.0));            if(t>=dropout)                t=0;            else                t=1;            ans.at<double>(i,j)=t;        }    return ans;}Mat Network::sigmoid(Mat &z){    Mat ans;    exp(-z,ans);    return 1.0/(1.0+ans);}Mat Network::sigmoid_prime(Mat &z){    Mat ans=sigmoid(z);    return ans.mul(1.0-ans);}int main(){    Network t;    t.read_data();    vector<int>sizes;    sizes.push_back(784);    sizes.push_back(50);    sizes.push_back(10);    t.init(sizes,0.1,1,1,0.9);    t.SGD(100,20,true,true);    getchar();    return 0;}