void cuVoteMnist()
{
return;
const int nclasses = 10;
/*state and cublas handle*/
cublasHandle_t handle;
init(handle);
/*read the data from disk*/
cuMatrixVector<float>trainX;
cuMatrixVector<float>testX;
cuMatrix<int>* trainY, *testY;
readMnistData(trainX, trainY, "mnist/train-images.idx3-ubyte", "mnist/train-labels.idx1-ubyte", 60000, 1);
readMnistData(testX , testY, "mnist/t10k-images.idx3-ubyte", "mnist/t10k-labels.idx1-ubyte", 10000, 1);
int ImgSize = trainX[0]->rows;
Config::instance()->setImageSize(ImgSize - Config::instance()->getCrop());
const char* path[] = {"mnist_result_cfm_5/1/checkPoint.txt",
"mnist_result_cfm_5/2/checkPoint.txt",
"mnist_result_cfm_5/3/checkPoint.txt",
"mnist_result_cfm_5/4/checkPoint.txt",
"mnist_result_cfm_5/5/checkPoint.txt",
"mnist_result_cfm_5/6/checkPoint.txt",
"mnist_result_cfm_5/7/checkPoint.txt",
"mnist_result_cfm_5/8/checkPoint.txt",
"mnist_result_cfm_5/9/checkPoint.txt"};
const char* initPath[] = {"mnist_result_cfm_4/1/MnistConfig.txt",
"mnist_result_cfm_5/2/MnistConfig.txt",
"mnist_result_cfm_5/3/MnistConfig.txt",
"mnist_result_cfm_5/4/MnistConfig.txt",
"mnist_result_cfm_5/5/MnistConfig.txt",
"mnist_result_cfm_5/6/MnistConfig.txt",
"mnist_result_cfm_5/7/MnistConfig.txt",
"mnist_result_cfm_5/8/MnistConfig.txt",
"mnist_result_cfm_5/9/MnistConfig.txt"};
/*build CNN net*/
/*
char* path[] = {"mnist_result_cfm_2/1_9967/checkPoint.txt",
"mnist_result_cfm_2/2_9964/checkPoint.txt",
"mnist_result_cfm_2/3_9966/checkPoint.txt",
"mnist_result_cfm_2/4_9970/checkPoint.txt",
"mnist_result_cfm_2/5_9967/checkPoint.txt",
"mnist_result_cfm_2/6_9963/checkPoint.txt",
"mnist_result_cfm_2/7_9965/checkPoint.txt",
"mnist_result_cfm_2/8_9963/checkPoint.txt",
"mnist_result_cfm_2/9_9964/checkPoint.txt"};
char* initPath[] = {"mnist_result_cfm_2/1_9967/MnistConfig.txt",
"mnist_result_cfm_2/2_9964/MnistConfig.txt",
"mnist_result_cfm_2/3_9966/MnistConfig.txt",
"mnist_result_cfm_2/4_9970/MnistConfig.txt",
"mnist_result_cfm_2/5_9967/MnistConfig.txt",
"mnist_result_cfm_2/6_9963/MnistConfig.txt",
"mnist_result_cfm_2/7_9965/MnistConfig.txt",
"mnist_result_cfm_2/8_9963/MnistConfig.txt",
"mnist_result_cfm_2/9_9964/MnistConfig.txt"};
*/
/*
ncfm
char* path[] = {"mnist_result/1_9973/checkPoint.txt",
"mnist_result/2_9972/checkPoint.txt",
"mnist_result/3_9969/checkPoint.txt",
"mnist_result/4_9968/checkPoint.txt",
"mnist_result/5_9974/checkPoint.txt",
"mnist_result/6_9973/checkPoint.txt",
"mnist_result/7_9968/checkPoint.txt",
"mnist_result/8_9968/checkPoint.txt",
"mnist_result/9_9968/checkPoint.txt",
"mnist_result/10_9976/checkPoint.txt",
"mnist_result/11_9969/checkPoint.txt",
"mnist_result/12_9968/checkPoint.txt",
"mnist_result/13_9973/checkPoint.txt"};
char* initPath[] = {"mnist_result/1_9973/MnistConfig.txt",
"mnist_result/2_9972/MnistConfig.txt",
"mnist_result/3_9969/MnistConfig.txt",
"mnist_result/4_9968/MnistConfig.txt",
"mnist_result/5_9974/MnistConfig.txt",
"mnist_result/6_9973/MnistConfig.txt",
"mnist_result/7_9968/MnistConfig.txt",
"mnist_result/8_9968/MnistConfig.txt",
"mnist_result/9_9968/MnistConfig.txt",
"mnist_result/10_9976/MnistConfig.txt",
"mnist_result/11_9969/MnistConfig.txt",
"mnist_result/12_9968/MnistConfig.txt",
"mnist_result/13_9973/MnistConfig.txt"};
*/
int numPath = sizeof(path) / sizeof(char*);
int * mark = new int[1 << numPath];
memset(mark, 0, sizeof(int) * (1 << numPath));
std::vector<int>vCorrect;
std::vector<cuMatrix<int>*>vPredict;
for(int i = 0; i < numPath; i++)
{
Config::instance()->initPath(initPath[i]);
int ImgSize = trainX[0]->rows;
int crop = Config::instance()->getCrop();
int nsamples = trainX.size();
int batch = Config::instance()->getBatchSize();
vPredict.push_back(new cuMatrix<int>(testY->getLen(), nclasses, 1));
buildNetWork(trainX.size(), testX.size());
cuReadConvNet(ImgSize - crop, path[i], nclasses);
int cur;
// int cur = voteTestDate(
// testX,
// testY,
// vPredict[i],
// batch,
// ImgSize,
// nclasses,
// handle);
cuFreeCNNMemory(batch, trainX, testX);
cuFreeConvNet();
vCorrect.push_back(cur);
Config::instance()->clear();
printf("%d %s\n", cur, path[i]);
}
int max = -1;
int val = 1;
int cur;
cuMatrix<int>* voteSum = new cuMatrix<int>(testY->getLen(), nclasses, 1);
cuMatrix<int>* correct = new cuMatrix<int>(1, 1, 1);
for(int m = (1 << numPath) - 1; m >= 1; m--)
{
int t = 0;
for(int i = 0; i < numPath; i++){
if(m & (1 << i)){
t++;
}
}
if(t != 5) continue;
voteSum->gpuClear();
if(mark[m] != 0)
{
cur = mark[m];
for(int i = 0; i < numPath; i++)
{
if(!(m & (1 << i)))continue;
printf("%d %s\n", vCorrect[i], path[i]);
}
}
else
{
int v = 0;
for(int i = numPath - 1; i >= 0; i--)
{
if(!(m & (1 << i)))continue;
v = v | (1 << i);
correct->gpuClear();
cur = cuVoteAdd(voteSum, vPredict[i], testY, correct, nclasses);
mark[v] = cur;
printf("%d %d %s\n", vCorrect[i], cur, path[i]);
}
}
if(cur >= max)
{
max = cur;
val = m;
}
printf("m = %d val = %d max = %d \n\n",m, val, max);
}
voteSum->gpuClear();
int m = val;
for(int i = numPath - 1; i >= 0; i--)
{
if(!(m & (1 << i)))continue;
correct->gpuClear();
cur = cuVoteAdd(voteSum, vPredict[i], testY, correct, nclasses);
printf("%d %d %s\n", vCorrect[i], cur, path[i]);
}
}
void cuPredict()
{
const int nclasses = 10;
const int ImgSize = 28;
/*state and cublas handle*/
cublasHandle_t handle;
init(handle);
/*read the data from disk*/
cuMatrixVector<double> trainX;
cuMatrixVector<double> testX;
cuMatrix<double>* trainY, *testY;
int num1 = readMnistData(trainX, trainY, "mnist/train-images.idx3-ubyte", "mnist/train-labels.idx1-ubyte", 60000, 0);
int num2 = readMnistData(testX , testY, "mnist/t10k-images.idx3-ubyte", "mnist/t10k-labels.idx1-ubyte", 10000, 1);
printf("train DataSize = %d, test DataSize = %d\n", num1, num2);
/*build CNN net*/
int imgDim = trainX[0]->rows;
int nsamples = trainX.size();
std::vector<cuCvl> ConvLayers;
std::vector<cuNtw> HiddenLayers;
cuSMR smr;
int batch = 200;
cuInitDistortionMemery(batch, ImgSize);
//char* path[] = {"4_9962", "3_9951"};
//char* initPath[] = {"P4.txt", "P3.txt"};
//char* path[] = {"p/p1/net.txt", "p/p2/net.txt", "p/p3/net.txt", "p/p4/net.txt", "p/p5/net.txt", "p/p6/net.txt", "p/p7/net.txt"};
//char* initPath[] = {"p/p1/Pnet.txt", "p/p2/Pnet.txt", "p/p3/Pnet.txt", "p/p4/Pnet.txt", "p/p5/Pnet.txt", "p/p6/Pnet.txt", "p/p7/Pnet.txt"};
// char* path[] = {"p10_20_256_256/p1/net.txt", "p10_20_256_256/p2/net.txt", "p10_20_256_256/20w/net.txt", "p10_20_256_256/dl/net.txt", "p10_20_256_256/jk/net.txt",
// "p10_20_256_256/lw/net.txt", "p10_20_256_256/lyx/net.txt", "p10_20_256_256/tdx/net.txt", "p10_20_256_256/wo/net.txt",
// "p10_20_256_256/xh/net.txt", "p10_20_256_256/yy/net.txt","p10_20_256_256/wy/net.txt","p10_20_256_256/hk/net.txt"};
// char* initPath[] = {"p10_20_256_256/p1/Pnet.txt", "p10_20_256_256/p2/Pnet.txt", "p10_20_256_256/20w/Pnet.txt", "p10_20_256_256/dl/Pnet.txt", "p10_20_256_256/jk/Pnet.txt",
// "p10_20_256_256/lw/Pnet.txt", "p10_20_256_256/lyx/Pnet.txt", "p10_20_256_256/tdx/Pnet.txt", "p10_20_256_256/wo/Pnet.txt",
// "p10_20_256_256/xh/Pnet.txt", "p10_20_256_256/yy/Pnet.txt", "p10_20_256_256/wy/Pnet.txt", "p10_20_256_256/hk/Pnet.txt"};
char* path[] = {"p10_20_256_256/hk/2/net.txt",
"p10_20_256_256/p1/net.txt", "p10_20_256_256/20w/net.txt", "p10_20_256_256/dl/net.txt",
"p10_20_256_256/lw/net.txt", "p10_20_256_256/lw/1/net.txt", "p10_20_256_256/tdx/net.txt",/*"p10_20_256_256/lyx/net.txt",*/ /*"p10_20_256_256/yy/net.txt",*/
"p10_20_256_256/yy/1/net.txt", /*"p10_20_256_256/xh/1/net.txt",*/
"p10_20_256_256/dl/1/net.txt", "p10_20_256_256/hk/1/net.txt",
"p10_20_256_256/xh/net.txt",
"p10_20_256_256/20w/2/net.txt",
"1/jk/net.txt", "1/dl/net.txt", "1/lw/net.txt", "1/xh/net.txt"};
char* initPath[] = {"p10_20_256_256/hk/2/Pnet.txt",
"p10_20_256_256/p1/Pnet.txt", "p10_20_256_256/20w/Pnet.txt", "p10_20_256_256/dl/Pnet.txt",
"p10_20_256_256/lw/Pnet.txt", "p10_20_256_256/lw/1/Pnet.txt", "p10_20_256_256/tdx/Pnet.txt", /*"p10_20_256_256/lyx/Pnet.txt",*/ /*"p10_20_256_256/yy/Pnet.txt",*/
"p10_20_256_256/yy/1/Pnet.txt",/*"p10_20_256_256/xh/1/Pnet.txt",*/
"p10_20_256_256/dl/1/Pnet.txt","p10_20_256_256/hk/1/Pnet.txt",
"p10_20_256_256/xh/Pnet.txt",
"p10_20_256_256/20w/2/Pnet.txt",
"1/jk/Pnet.txt", "1/dl/Pnet.txt","1/lw/Pnet.txt","1/xh/Pnet.txt"};
// char* path[] = {"1/jk/net.txt", "1/dl/net.txt", "1/lw/net.txt", "1/xh/net.txt", "p10_20_256_256/lw/1/net.txt"};
// char*initPath[] = {"1/jk/Pnet.txt", "1/dl/Pnet.txt","1/lw/Pnet.txt","1/xh/Pnet.txt","p10_20_256_256/lw/1/Pnet.txt"};
int numPath = sizeof(path) / sizeof(char*);
int * mark = new int[1 << numPath];
memset(mark, 0, sizeof(int) * (1 << numPath));
std::vector<int>vCorrect;
std::vector<cuMatrix<double>*>vPredict;
for(int i = 0; i < numPath; i++)
{
vPredict.push_back(new cuMatrix<double>(trainY->getLen(), 1));
cuReadConvNet(ConvLayers, HiddenLayers, smr, imgDim, nsamples, path[i], nclasses);
cuInitCNNMemory(batch, trainX, testX, ConvLayers, HiddenLayers, smr, ImgSize, nclasses);
int cur = cuPredictNetwork(trainX, trainY, ConvLayers, HiddenLayers, smr, 3e-3, testX, testY, vPredict[i],ImgSize, nsamples, batch, ImgSize, nclasses, handle);
cuFreeCNNMemory(batch, trainX, testX, ConvLayers,HiddenLayers, smr);
cuFreeConvNet(ConvLayers, HiddenLayers, smr);
vCorrect.push_back(cur);
printf("%d %s\n", cur, path[i]);
}
int max = -1;
int val = 1;
int cur;
return;
for(int m = (1 << numPath) - 1; m >= 1; m--)
{
//m = 14653;
//m = 205;
if(mark[m] != 0)
{
cur = mark[m];
for(int i = 0; i < numPath; i++)
{
if(!(m & (1 << i)))continue;
printf("%d %s\n", vCorrect[i], path[i]);
}
}
else
{
int v = 0;
for(int i = numPath - 1; i >= 0; i--)
{
if(!(m & (1 << i)))continue;
v = v | (1 << i);
cur = cuPredictAdd(vPredict[i], testY, batch, ImgSize, nclasses);
mark[v] = cur;
printf("%d %d %s\n", vCorrect[i], cur, path[i]);
}
}
if(cur >= max)
{
max = cur;
val = m;
}
cuClearCorrectCount();
printf("m = %d val = %d max = %d \n\n",m, val, max);
}
cuClearCorrectCount();
int m = val;
for(int i = numPath - 1; i >= 0; i--)
{
if(!(m & (1 << i)))continue;
cur = cuPredictAdd(vPredict[i], testY, batch, ImgSize, nclasses);
printf("%d %d %s\n", vCorrect[i], cur, path[i]);
}
cuShowInCorrect(testX, testY, ImgSize, nclasses);
}
