private void Learn_Click(object sender, RoutedEventArgs e)
{
var downsample = new Downsampler();
var training = new ImageMLDataSet(downsample, true, 1, -1);
for (var i = 0; i < Images.Count; ++i)
{
var ideal = new BasicMLData(DIGITS_COUNT);
for (int j = 0; j < DIGITS_COUNT; ++j)
{
if (j == i)
{
ideal[j] = 1;
}
else
{
ideal[j] = -1;
}
}
foreach (var img in Images[i])
{
MemoryStream stream = new MemoryStream();
BitmapEncoder encoder = new BmpBitmapEncoder();
encoder.Frames.Add(BitmapFrame.Create(img));
encoder.Save(stream);
var bitmap = new Drawing.Bitmap(stream);
var data = new ImageMLData(bitmap);
training.Add(data, ideal);
}
}
training.Downsample(DIGIT_HEIGHT, DIGIT_WIDTH);
network = EncogUtility.SimpleFeedForward(training.InputSize, 35, 0, training.IdealSize, true);
double strategyError = 0.01;
int strategyCycles = 2000;
var train = new ResilientPropagation(network, training);
//train.AddStrategy(new ResetStrategy(strategyError, strategyCycles));
EncogUtility.TrainDialog(train, network, training);
EncogDirectoryPersistence.SaveObject(new FileInfo("network.eg"), network);
}
private void ProcessCreateTraining()
{
String strWidth = GetArg("width");
String strHeight = GetArg("height");
String strType = GetArg("type");
downsampleHeight = int.Parse(strWidth);
downsampleWidth = int.Parse(strHeight);
if (strType.Equals("RGB"))
{
downsample = new RGBDownsample();
}
else
{
downsample = new SimpleIntensityDownsample();
}
training = new ImageMLDataSet(downsample, false, 1, -1);
app.WriteLine("Training set created");
}
