void runMnist(){
const int nclasses = 10;
/*state and cublas handle*/
cublasHandle_t handle;
init(handle);
/*read the data from disk*/
cuMatrixVector<double>trainX;
cuMatrixVector<double>testX;
cuMatrix<double>* trainY, *testY;
Config::instance();
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);
/*build CNN net*/
int ImgSize = trainX[0]->rows;
int nsamples = trainX.size();
std::vector<cuCvl> ConvLayers;
std::vector<cuNtw> HiddenLayers;
cuSMR smr;
int batch = 200;
double start,end;
int cmd;
cuInitDistortionMemery(batch, ImgSize);
printf("random init input 0\nRead from file input 1\n");
scanf("%d", &cmd);
if(cmd == 0)
cuConvNetInitPrarms(ConvLayers, HiddenLayers, smr, ImgSize, nsamples, nclasses);
else if(cmd == 1)
cuReadConvNet(ConvLayers, HiddenLayers, smr, ImgSize, nsamples, "net.txt", nclasses);
cuInitCNNMemory(batch, trainX, testX, ConvLayers,HiddenLayers, smr, ImgSize, nclasses);
start = clock();
cuTrainNetwork(trainX, trainY, ConvLayers, HiddenLayers, smr, 4e-4, testX, testY, nsamples, batch, ImgSize, nclasses, handle);
end = clock();
printf("trainning time %lf\n", (end - start) / CLOCKS_PER_SEC);
}
void runChinese() {
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;
readChineseData(trainX, testX, trainY, testY);
Config::instance()->initPath("Config/ChineseConfig.txt");
/*build CNN net*/
int ImgSize = trainX[0]->rows;
int crop = Config::instance()->getCrop();
int nsamples = trainX.size();
int batch = Config::instance()->getBatchSize();
float start, end;
int cmd;
cuInitDistortionMemery(batch, ImgSize - crop);
printf(
"1. random init weight\n2. Read weight from file\nChoose the way to init weight:");
if(g_argv.size() >= 2)
cmd = g_argv[1];
else {
if(1 != scanf("%d", &cmd)){
LOG("scanf fail", "result/log.txt");
}
}
if (cmd == 2)
cuReadConvNet(ImgSize - crop,
"Result/checkPoint.txt", nclasses);
buildNetWork(trainX.size(), testX.size());
/*learning rate*/
std::vector<float>nlrate;
std::vector<float>nMomentum;
std::vector<int>epoCount;
float r = 0.05;
float m = 0.90;
int e = 50;
for(int i = 0; i < 20; i++){
nlrate.push_back(r);
nMomentum.push_back(m);
epoCount.push_back(e);
r = r * 0.90;
m = m + 0.005;
if(m >= 1.0) m = 0.99;
}
start = clock();
cuTrainNetwork(trainX, trainY, testX, testY, batch, ImgSize - crop, nclasses, nlrate, nMomentum, epoCount, handle);
end = clock();
char logStr[1024];
sprintf(logStr, "trainning time hours = %f\n",
(end - start) / CLOCKS_PER_SEC / 3600);
LOG(logStr, "Result/log.txt");
}
void runMnist(){
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;
Config::instance()->initPath("Config/MnistConfig.txt");
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);
MemoryMonitor::instance()->printCpuMemory();
MemoryMonitor::instance()->printGpuMemory();
/*build CNN net*/
int ImgSize = trainX[0]->rows;
Config::instance()->setImageSize(ImgSize - Config::instance()->getCrop());
int crop = Config::instance()->getCrop();
int nsamples = trainX.size();
int batch = Config::instance()->getBatchSize();
float start,end;
int cmd;
cuInitDistortionMemery(batch, ImgSize - crop);
printf("1. random init weight\n2. Read weight from file\nChoose the way to init weight:");
if(g_argv.size() >= 2)
cmd = g_argv[1];
else
if(1 != scanf("%d", &cmd)){
LOG("scanf fail", "result/log.txt");
}
buildNetWork(trainX.size(), testX.size());
if(cmd == 2)
cuReadConvNet(ImgSize - crop, "Result/checkPoint.txt", nclasses);
/*learning rate*/
std::vector<float>nlrate;
std::vector<float>nMomentum;
std::vector<int>epoCount;
nlrate.push_back(0.05f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.04f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.03f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.02f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.01f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.009f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.008f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.007f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.006f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.005f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.004f); nMomentum.push_back(0.90f); epoCount.push_back(15);
nlrate.push_back(0.003f); nMomentum.push_back(0.90f); epoCount.push_back(15);
start = clock();
cuTrainNetwork(trainX, trainY, testX, testY, batch, ImgSize - crop, nclasses, nlrate, nMomentum, epoCount, handle);
end = clock();
char logStr[1024];
sprintf(logStr, "trainning time hours = %f\n",
(end - start) / CLOCKS_PER_SEC / 3600);
LOG(logStr, "Result/log.txt");
}
