private void Window_Loaded(object sender, RoutedEventArgs e)
{
tbTest.IsEnabled = false;
for (int i = 0; i < 10; i++)
{
MnistImage.trainingImages[i] = new MnistImage();
MnistImage.testImages[i] = new MnistImage();
MnistImage.LoadTrainingImage(i);
MnistImage.LoadTestImage(i);
}
}
private void TrainAllMnistImages(int nEpochMax, bool accuracy)
{
int counter = 101;
int trigger = 50;
int totalImages = 0;
neuralNet.learningRate = double.Parse(teLearningRate.Text.ToString(), CultureInfo.InvariantCulture);
for (int epoch = 0; epoch < nEpochMax; epoch++)
{
int n = 0;
bool anyMoreNumbers = true;
while (anyMoreNumbers)
{
for (int expected = 0; expected < NeuralNetwork.outputLayer.outputNeurons; expected++)
{
bool useTestingDataset = false;
byte[] pixels = MnistImage.GetImageNumberPixels(expected, n, useTestingDataset);
currentPixels = pixels;
if (pixels != null)
{
TrainNetOnNumber(pixels, expected);
currentColumn = MnistImage.GetCol(expected, n);
currentRow = MnistImage.GetRow(expected, n);
}
else
{
anyMoreNumbers = false;
}
}
n++;
totalImages++;
if (accuracy)
{
counter++;
if (counter > trigger)
{
AccuracyOfResult = Evaluation(nIterationLoop);
counter = 0;
}
}
}
}
}
private string Evaluation(int nTestFactor)
{
neuralNet.errors.Clear();
int n = 0;
int limit = nTestFactor;
int rightAnalysis = 0;
int analysis = 0;
int[] rightNumbers = new int[10];
int[] nNumbers = new int[10];
bool anyMoreNumbers = true;
for (int expected = 0; expected < NeuralNetwork.outputLayer.outputNeurons; expected++)
{
rightNumbers[expected] = 0;
nNumbers[expected] = 0;
}
while (anyMoreNumbers)
{
anyMoreNumbers = false;
for (int expected = 0; (expected < NeuralNetwork.outputLayer.outputNeurons); expected++)
{
bool useTestingDataset = true;
byte[] pixels = MnistImage.GetImageNumberPixels(expected, n, useTestingDataset);
currentPixels = pixels;
if (pixels != null)
{
anyMoreNumbers = true;
int estimated = 0;
double confidence = 0.0;
if (imageHasNumber(pixels))
{
if (TestNetOnNumber(pixels, expected, out estimated, out confidence))
{
rightAnalysis++;
rightNumbers[expected]++;
}
analysis++;
nNumbers[expected]++;
}
}
}
n++;
if (n > limit)
{
break;
}
}
double percentRight = (double)rightAnalysis / (double)analysis;
previousAccuracySample = accuracySample;
accuracySample = percentRight;
string result = Math.Round(percentRight, 4).ToString();
if (n > nTestFactor)
{
TotalAccuracyOfNumbers = "";
for (int i = 0; i < NeuralNetwork.outputLayer.outputNeurons; i++)
{
double percentCorrect = ((double)rightNumbers[i] / (double)nNumbers[i]) * 100;
TotalAccuracyOfNumbers += "Konfidenz der Ziffer " + i.ToString() + " beträgt " + percentCorrect.ToString("0.00") + " %\r\n";
}
}
return(result);
}
