c++和cuda混合编程 实现传统神经网络

直接放代码了。。。

实现的是x1+x2=y的预测，但梯度下降很慢。。。233333，gpu运行时间很快！！

//
//  main.cpp
//  bp
//
//  Created by jzc on 2018/4/18.
//  Copyright © 2018年 jzc. All rights reserved.
//
#include 
#include 
#include 
#include 
#include 
#include 
#include 
using namespace std;
#define DATASIZE 10000
#define TESTSIZE 100
#define NEURESIZE 50
#define RW 0.1
#define EPOCH 1000
#define E 2.71828
//打印设备信息
void printDeviceProp(const cudaDeviceProp &prop)
{
    printf("Device Name : %s.\n", prop.name);
    printf("totalGlobalMem : %ld.\n", prop.totalGlobalMem);
    printf("sharedMemPerBlock : %ld.\n", prop.sharedMemPerBlock);
    printf("regsPerBlock : %d.\n", prop.regsPerBlock);
    printf("warpSize : %d.\n", prop.warpSize);
    printf("memPitch : %ld.\n", prop.memPitch);
    printf("maxThreadsPerBlock : %d.\n", prop.maxThreadsPerBlock);
    printf("maxThreadsDim[0 - 2] : %d %d %d.\n", prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
    printf("maxGridSize[0 - 2] : %d %d %d.\n", prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
    printf("totalConstMem : %ld.\n", prop.totalConstMem);
    printf("major.minor : %d.%d.\n", prop.major, prop.minor);
    printf("clockRate : %d.\n", prop.clockRate);
    printf("textureAlignment : %ld.\n", prop.textureAlignment);
    printf("deviceOverlap : %d.\n", prop.deviceOverlap);
    printf("multiProcessorCount : %d.\n", prop.multiProcessorCount);
}

//CUDA 初始化
bool InitCUDA()
{
    int count;
    
    //取得支持Cuda的装置的数目
    cudaGetDeviceCount(&count);
    
    if (count == 0) {
        fprintf(stderr, "There is no device.\n");
        return false;
    }
    
    int i;
    
    for (i = 0; i < count; i++) {
        
        cudaDeviceProp prop;
        cudaGetDeviceProperties(&prop, i);
        //打印设备信息
        printDeviceProp(prop);
        
        if (cudaGetDeviceProperties(&prop, i) == cudaSuccess) {
            if (prop.major >= 1) {
                break;
            }
        }
    }
    
    if (i == count) {
        fprintf(stderr, "There is no device supporting CUDA 1.x.\n");
        return false;
    }
    
    cudaSetDevice(i);
    
    return true;
}
void init(int num,int range,double a[],double offset){
    for(int i=0;i){
        a[i] = (double)(rand()%(range*1000)/1000.0) - offset;
    }
}

void getM(int num,double a[],double m[]){
    m[0] = m[1] = 0.0;
    for(int i=0;i){
        if(a[i]0]){
            m[0] = a[i];
        }else if(a[i]>m[1]){
            m[1] = a[i];
        }
    }
}


void normalize(int num,double a[],double m[]){
    for(int i =0;i){
        a[i] = (a[i]-m[0]+1)/(m[1]-m[0]+1);
    }
}


void renorm(int num,double a[],double m[]){
    for(int i =0;i){
        a[i] = a[i]*(m[1]-m[0]+1) + m[0] - 1;
    }
}


void printArray(int num,double a[]){
    for(int i=0;i){
        printf("%6.4lf ",a[i]);
        if((i+1)%10==0){
            cout<<endl;
        }
    }
}

__global__ static void hidenLayer(double x1,double x2,double w1[],double w2[],double yh[]){
    /*for(int i=0;i*/
    const int tid = threadIdx.x;
    int i =tid;
    yh[i] = w1[i]*x1 + w2[i]*x2;
    yh[i] = 1/(1+pow(E,0-yh[i]));
}

double outLayer(double yh[],double v[]){
    double y2;
    for(int i=0;i){
        y2 += yh[i] * v[i];
    }
    y2 = 1/(1+pow(E,0-y2));
    return y2;
    
}

__global__ static void update(double x1[],double x2[],double yh[],double v[],double w1[],double w2[],double *loss){
    const int tid = threadIdx.x;
    int i = tid;
    /*for(int i=0;i*/
    w1[i] += x1[i] * (1-x1[i]) * (*loss) * RW;
    w2[i] += x2[i] * (1-x2[i]) * (*loss) * RW;
    v[i] += yh[i] * (*loss) * RW;
}

/*double test(double w1[],double w2[],double v[],double m1[],double m2[],double my[]){
    double tx1[TESTSIZE],tx2[TESTSIZE],ty[TESTSIZE],tyh[NEURESIZE],ty2[TESTSIZE];
    double avLoss = 0.0;
    
    init(TESTSIZE,10,tx1,0.0);
    init(TESTSIZE,10,tx2,0.0);
    
    for(int i=0;i*/


int main(){
    ofstream outf;
    outf.open("trainloss.txt");
    srand( (unsigned)time(NULL) );
    long starttime = clock();
    double x1[DATASIZE],x2[DATASIZE],y[DATASIZE],y2[DATASIZE];
    double w1[NEURESIZE],w2[NEURESIZE],v[NEURESIZE],yh[NEURESIZE];
    double m1[2],m2[2],my[2];                                      
    double cLoss,realLoss,minTrainLoss = 1.0,minTestLoss = 1.0;
    init(DATASIZE,10,x1,0.0);
    init(DATASIZE,10,x2,0.0);
    init(NEURESIZE,2,w1,1.0);
    init(NEURESIZE,2,w2,1.0);
    init(NEURESIZE,2,v,1.0);
    
    for(int i=0;i){
        y[i] = x1[i] + x2[i];
    }
    
    //CUDA 初始化
    if (!InitCUDA()) {
        return 0;
    }
    //cudaMalloc 取得一块显卡内存
    double *x1_g,*x2_g,*y_g,*y2_g;
    double *w1_g,*w2_g,*v_g,*yh_g;
    double *cLoss_g;
    cudaMalloc((void**)&x1_g, sizeof(double)* DATASIZE);
    cudaMalloc((void**)&x2_g, sizeof(double)* DATASIZE);
    cudaMalloc((void**)&y_g, sizeof(double)* DATASIZE);
    cudaMalloc((void**)&y2_g, sizeof(double)* DATASIZE);
    cudaMalloc((void**)&w1_g, sizeof(double)* NEURESIZE);
    cudaMalloc((void**)&w2_g, sizeof(double)* NEURESIZE);
    cudaMalloc((void**)&v_g, sizeof(double)* NEURESIZE);
    cudaMalloc((void**)&yh_g, sizeof(double)* NEURESIZE);
    cudaMalloc((void**)&cLoss_g, sizeof(double));
    
    //cudaMemcpy 将产生的随机数复制到显卡内存中
    //cudaMemcpyHostToDevice - 从内存复制到显卡内存
    //cudaMemcpyDeviceToHost - 从显卡内存复制到内存
    cudaMemcpy(w1_g,w1, sizeof(double)*NEURESIZE, cudaMemcpyHostToDevice);
    cudaMemcpy(w2_g,w2, sizeof(double)*NEURESIZE, cudaMemcpyHostToDevice);
    cudaMemcpy(v_g,v, sizeof(double)*NEURESIZE, cudaMemcpyHostToDevice);
    cudaMemcpy(x1_g,x1, sizeof(double)*DATASIZE, cudaMemcpyHostToDevice);
    cudaMemcpy(x2_g,x2, sizeof(double)*DATASIZE, cudaMemcpyHostToDevice);
    cudaMemcpy(y_g,y, sizeof(double)*DATASIZE, cudaMemcpyHostToDevice);
    cudaMemcpy(yh_g,yh, sizeof(double)*NEURESIZE, cudaMemcpyHostToDevice);
    cudaMemcpy(cLoss_g,&cLoss, sizeof(double), cudaMemcpyHostToDevice);
    
    getM(DATASIZE,x1,m1);
    getM(DATASIZE,x2,m2);
    getM(DATASIZE,y,my);
    normalize(DATASIZE,x1,m1);
    normalize(DATASIZE,x2,m2);
    normalize(DATASIZE,y,my);
    
    
    for(int j=0;j){
        double tLoss = 0.0;
        for(int i=0;i){
            hidenLayer<< < 1, NEURESIZE, 0 >> >(x1_g[i],x2_g[i],w1_g,w2_g,yh_g);
            cudaMemcpy(yh,yh_g, sizeof(double)*NEURESIZE, cudaMemcpyDeviceToHost);
            cudaMemcpy(v,v_g, sizeof(double)*NEURESIZE, cudaMemcpyDeviceToHost);
            y2[i] = outLayer(yh,v);
            cLoss = y2[i] * (1-y2[i]) * (y[i]-y2[i]);
            cudaMemcpy(cLoss_g,&cLoss, sizeof(double), cudaMemcpyHostToDevice);
            update<< < 1, NEURESIZE, 0 >> >(x1_g,x2_g,yh_g,v_g,w1_g,w2_g,cLoss_g);
            cudaMemcpy(&cLoss,cLoss_g, sizeof(double)*NEURESIZE, cudaMemcpyDeviceToHost);
            cLoss = pow(cLoss,2);
            cLoss = cLoss*(my[1]-my[0]+1);
            tLoss += cLoss;
        }
        tLoss /= DATASIZE;
        if(tLoss<minTrainLoss){
            minTrainLoss = tLoss;
        }
        printf("EPOCH--%d, trainLoss--%0.4f\n",j,tLoss);
         outf<"\t"<endl;
        
        /*cudaMemcpy(w1,w1_g, sizeof(double)*NEURESIZE, cudaMemcpyDeviceToHost);
        cudaMemcpy(w2,w2_g, sizeof(double)*NEURESIZE, cudaMemcpyDeviceToHost);
        cudaMemcpy(v,v_g, sizeof(double)*NEURESIZE, cudaMemcpyDeviceToHost);
        double avLoss = test(w1,w2,v,m1,m2,my);
        printf("EPOCH--%d, avLoss--%0.4f\n",j,avLoss);
        if(avLoss*/
        cout<<"------------------"<<endl;
    }
    printf("minTrainLoss--%0.4f\n",minTrainLoss);
    //printf("minTestLoss--%0.4f\n",minTestLoss);
    outf.close();
    
    //Free
    cudaFree(x1_g);
    cudaFree(x2_g);
    cudaFree(y_g);
    cudaFree(w1_g);
    cudaFree(w2_g);
    cudaFree(v_g);
    cudaFree(yh_g);
    cudaFree(cLoss_g);
    
    long endtime = clock()-starttime;
    float execution_time = (float)endtime / (1024 * 1058500);
    cout << "total time cost: " << execution_time<<endl;
 
    
    
    return 0;
}

 

转载于:https://www.cnblogs.com/jzcbest1016/p/9006650.html