private void learnButtonClick(object sender, RoutedEventArgs e)
{
network = NetworkBuilder.GetBuilder().SetOutputLayerNeurons(5)
.SetInputLayerNeurons(103).SetMaxEras(100000).SetHiddenLayerNeurons(6)
.SetLearningRate(0.2f).SetMomentum(0.4f).SetMinError(0.0001).Build();
string[] files = Directory.GetFiles(Directory.GetCurrentDirectory() + "/Images");
foreach (string file in files)
{
if (Path.GetFileName(file).EndsWith(".bmp"))
{
continue;
}
Bitmap bmp = new Bitmap(file);
IEnumerable <double> input = readBitmapPoints(bmp, 100, Path.GetFileNameWithoutExtension(file));
int outputIndex = Array.IndexOf(usedSigns, Path.GetFileNameWithoutExtension(file).Split('_')[0]);
Console.WriteLine("Adding " + file + " as " + usedSigns[outputIndex]);
network.AddLearningPair(input.ToList(), expectedOutputs[outputIndex]);
}
network.SingleEraEnded += data =>
{
if (data.PercentOfError > 20)
{
network.SetLearningRate(0.18);
network.SetMomentum(0.35);
}
else if (data.PercentOfError > 50)
{
network.SetLearningRate(0.15);
network.SetMomentum(0.3);
}
else if (data.PercentOfError > 75)
{
network.SetLearningRate(0.1);
network.SetMomentum(0.25);
}
if ((data.CurrentEra + 1) % 2000 == 0 || data.CurrentEra == 0 || data.PercentOfError >= 100)
{
Console.WriteLine(
$"Era: {data.CurrentEra + 1}, Learn progress: {data.LearnProgress}, Overall Error: {data.OverallError}" +
$", Percentage of error: {data.PercentOfError}");
}
};
network.Learn();
}