static async Task Main(string[] args)
{
var csvReader = new CsvReader();
var trainSets = await csvReader.Read(TrainFile);
var testSets = await csvReader.Read(TestFile);
var nn = new Network(784, 30, 10);
var bp = new BackpropagationAlgorithm(nn);
var error = 0.0;
var count = 0;
do
{
count++;
var sw = Stopwatch.StartNew();
error = bp.Train(trainSets, testSets);
sw.Stop();
Console.WriteLine(sw.ElapsedMilliseconds);
Console.WriteLine($"Count: {count} - MSE: {error}.");
} while (error > 0.005);
Console.WriteLine(nn.Save());
Console.ReadLine();
}
private void trainNetwork()
{
bool trainComplete = false;
int currentEpoch;
double currentError;
var watch = Stopwatch.StartNew();
do
{
trainComplete = backpropagationAlgorithm.Train();
if (refreshProgress)
{
Dispatcher.Invoke(() =>
{
currentEpoch = backpropagationAlgorithm.GetCurrentEpoch();
currentError = backpropagationAlgorithm.GetCurrentError();
epochLabel.Content = currentEpoch.ToString();
errorLabel.Content = String.Format("{0:0.00000}", currentError);
learningProgressBar.Value = (int)((ErrorThreshold / currentError * 100));
chart.Series["series"].Points.AddXY(currentEpoch, currentError);
});
}
}while (!trainComplete && !stopSignal);
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
Dispatcher.Invoke(() =>
{
abortButton.IsEnabled = false;
});
if (stopSignal)
{
backpropagationAlgorithm.Abort();
MessageBox.Show("Przerwano uczenie sieci (Czas: " + elapsedMs + " ms)");
}
else
{
MessageBox.Show("Sieć została nauczona (Czas: " + elapsedMs + " ms)");;
}
}