private void ProcessNetwork()
{
app.WriteLine("Downsampling images...");
foreach (ImagePair pair in imageList)
{
IMLData ideal = new BasicMLData(outputCount);
int idx = pair.Identity;
for (int i = 0; i < outputCount; i++)
{
if (i == idx)
{
ideal.Data[i] = 1;
}
else
{
ideal.Data[i] = -1;
}
}
try
{
var img = new Bitmap(pair.File);
var data = new ImageMLData(img);
training.Add(data, ideal);
}
catch (Exception e)
{
app.WriteLine("Error loading: " + pair.File
+ ": " + e.Message);
}
}
String strHidden1 = GetArg("hidden1");
String strHidden2 = GetArg("hidden2");
if (training.Count == 0)
{
app.WriteLine("No images to create network for.");
return;
}
training.Downsample(downsampleHeight, downsampleWidth);
int hidden1 = int.Parse(strHidden1);
int hidden2 = int.Parse(strHidden2);
network = EncogUtility.SimpleFeedForward(training
.InputSize, hidden1, hidden2,
training.IdealSize, true);
app.WriteLine("Created network: " + network);
}
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);
}