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 <stdio.h>
#include <iostream>
#include <time.h>
#include <stdlib.h>
#include <math.h>
#include <fstream>
#include <cuda_runtime.h>
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[], prop.maxThreadsDim[], prop.maxThreadsDim[]);
    printf("maxGridSize[0 - 2] : %d %d %d.\n", prop.maxGridSize[], prop.maxGridSize[], prop.maxGridSize[]);
    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 == ) {
        fprintf(stderr, "There is no device.\n");
        return false;
    }

    int i;

    for (i = ; i < count; i++) {

        cudaDeviceProp prop;
        cudaGetDeviceProperties(&prop, i);
        //打印设备信息
        printDeviceProp(prop);

        if (cudaGetDeviceProperties(&prop, i) == cudaSuccess) {
            if (prop.major >= ) {
                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=;i<num;i++){
        a[i] = (double)(rand()%(range*)/1000.0) - offset;
    }
}

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

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

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

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

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

double outLayer(double yh[],double v[]){
    double y2;
    for(int i=;i<NEURESIZE;i++){
        y2 += yh[i] * v[i];
    }
    y2 = /(+pow(E,-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<NEURESIZE;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;
    }*/
    w1[i] += x1[i] * (-x1[i]) * (*loss) * RW;
    w2[i] += x2[i] * (-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<TESTSIZE;i++){
        ty[i] = tx1[i] + tx2[i];
    }
    normalize(TESTSIZE,tx1,m1);
    normalize(TESTSIZE,tx2,m2);
    for(int q=0;q<TESTSIZE;q++){
        hidenLayer(tx1[q],tx2[q],w1,w2,tyh);
        ty2[q] = outLayer(tyh,v);
    }

    renorm(TESTSIZE,ty2,my);
    for(int i=0;i<TESTSIZE;i++){
        if(i<10){
            printf("%2d y=%2.4f y2=%2.4f\n",i,ty[i],ty2[i]);
        }
        avLoss += pow(ty[i]-ty2[i],2);
    }
    avLoss /= TESTSIZE;
    //cout<<avLoss<<endl;
    return avLoss;
}*/

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[],m2[],my[];
    double cLoss,realLoss,minTrainLoss = 1.0,minTestLoss = 1.0;
    init(DATASIZE,,x1,0.0);
    init(DATASIZE,,x2,0.0);
    init(NEURESIZE,,w1,1.0);
    init(NEURESIZE,,w2,1.0);
    init(NEURESIZE,,v,1.0);

    for(int i=;i<DATASIZE;i++){
        y[i] = x1[i] + x2[i];
    }

    //CUDA 初始化
    if (!InitCUDA()) {
        return ;
    }
    //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=;j<EPOCH;j++){
        double tLoss = 0.0;
        for(int i=;i<DATASIZE;i++){
            hidenLayer<< < , NEURESIZE,  >> >(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] * (-y2[i]) * (y[i]-y2[i]);
            cudaMemcpy(cLoss_g,&cLoss, sizeof(double), cudaMemcpyHostToDevice);
            update<< < , NEURESIZE,  >> >(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,);
            cLoss = cLoss*(my[]-my[]+);
            tLoss += cLoss;
        }
        tLoss /= DATASIZE;
        if(tLoss<minTrainLoss){
            minTrainLoss = tLoss;
        }
        printf("EPOCH--%d, trainLoss--%0.4f\n",j,tLoss);
         outf<<j<<"\t"<<tLoss<<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<minTestLoss){
            minTestLoss = 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 / ( * );
    cout << "total time cost: " << execution_time<<endl;

    return ;
}