|
public static InputDataLoader ( string InputDataFile, int nInput ) : |
||
| InputDataFile | string | |
| nInput | int | |
| return | ||
static void Main(string[] args)
{
// ======== Setup the default parameters ========
paramModel_t paramModel = new paramModel_t();
paramTrain_t paramTrain = new paramTrain_t();
SetupDefaultParams(paramModel, paramTrain);
// ---- Data Files ----
string TrainInputFile = "";
string TestInputFile = "";
string ModelFile = "";
string ResultFile = "";
// ======== Parse the input parameters ========
if (
!ParseArgument(
args,
paramModel,
paramTrain,
ref TrainInputFile,
ref TestInputFile,
ref ModelFile,
ref ResultFile
)
)
{
return;
}
paramModel.T = new float[paramModel.nHidLayer];
for (int IdxLayer = 0; IdxLayer < paramModel.nHidLayer; IdxLayer++)
{
paramModel.T[IdxLayer] = paramModel.T_value;
}
// ======== Set the number of threads ========
MatrixOperation.THREADNUM = paramTrain.ThreadNum;
MatrixOperation.MaxMultiThreadDegree = paramTrain.MaxMultiThreadDegree;
// ======== Load data from file ========
SparseMatrix TrainData = DataLoader.InputDataLoader(TrainInputFile, paramModel.nInput);
SparseMatrix TestData = DataLoader.InputDataLoader(TestInputFile, paramModel.nInput);
paramTrain.nTrain = TrainData.nCols;
paramTrain.nTest = TestData.nCols;
// ======== Unsupervised learning of LDA model: unfolding and back-propagation
// (i) Inference: Feedforward network via MDA unfolding
// (ii) Learning: Projected (mini-batch) stochastic gradient descent (P-SGD) using back propagation
LDA_Learn.TrainingBP_LDA(TrainData, TestData, paramModel, paramTrain, ModelFile, ResultFile);
}
