public void TestTrainSimple()
{
// create lenet
LenetClassifier classifier = new LenetClassifier();
classifier.Set("junk", 0); // disable junk
classifier.SetExtractor("scaledfe");
classifier.Initialize(classesNums);
StringBuilder sbout;
classifier.GetStdout(out sbout);
Console.Write(sbout);
// load RowDataset8 from file
RowDataset8 ds = new RowDataset8();
ds.Load(trainDatasetFileName);
// do train
classifier.Set("epochs", 3);
classifier.XTrain(ds);
// save classifier to file
classifier.Save(trainNetworkFileName);
// test recognize
DoTestRecognize(classifier);
}
public void TestRowDataset()
{
DRandomizer.Default.init_drand(DateTime.Now.Millisecond);
// load Mnist datasource
MnistDatasource mds = new MnistDatasource();
mds.LoadFromFile(mnistFileNamePrefix);
// convert mnist to RowDataset8
RowDataset8 ds8 = MnistDatasetConvert.GetRowDataset8(mds, classes);
// show random sample to console
Floatarray fa = new Floatarray();
int isample = (int)DRandomizer.Default.drand(ds8.nSamples(), 0);
ds8.Input(fa, isample);
Console.WriteLine("Char is '{0}'", (char)ds8.Cls(isample));
NarrayShow.ShowConsole(fa);
// compare random float sample and original mnist
StdInput inp1 = new StdInput(mds.ImagesData[isample], mds.ImgHeight, mds.ImgWidth);
StdInput inp2 = new StdInput(fa);
Console.WriteLine("Arrays is identical? {0}", Equals(inp1.GetDataBuffer(), inp2.GetDataBuffer()));
// save RowDataset8 to file
Console.WriteLine("Saving {0} samples..", ds8.nSamples());
ds8.Save(mnistFileNamePrefix + dsExt);
// load RowDataset8 from file
RowDataset8 ds = new RowDataset8();
ds.Load(mnistFileNamePrefix + dsExt);
Console.WriteLine("Loaded {0} samples", ds.nSamples());
}