public void PrepareTrainingData(TrainingRate learningParameters, int epoch, ref int index, ref bool stop)
{
if (learningParameters.Distorted)
{
if (((epoch-1) % learningParameters.SameDistortionsForNEpochs) == 0)
{
bool parameterChanged = false;
if (SeverityFactor != learningParameters.SeverityFactor)
{
SeverityFactor = learningParameters.SeverityFactor;
parameterChanged = true;
}
if (MaxScaling != learningParameters.MaxScaling)
{
MaxScaling = learningParameters.MaxScaling;
parameterChanged = true;
}
if (MaxRotation != learningParameters.MaxRotation)
{
MaxRotation = learningParameters.MaxRotation;
parameterChanged = true;
}
if (ElasticSigma != learningParameters.ElasticSigma)
{
ElasticSigma = learningParameters.ElasticSigma;
parameterChanged = true;
}
if (ElasticScaling != learningParameters.ElasticScaling)
{
ElasticScaling = learningParameters.ElasticScaling;
parameterChanged = true;
}
if (parameterChanged)
CalculateGaussianKernel();
PreparedTrainingPatterns = new DoubleImageData[TrainingPatternsCount];
for (index = 0; index < TrainingPatternsCount; index++)
{
if (stop)
break;
PreparedTrainingPatterns[index] = new DoubleImageData(TrainingPatterns[index].Label, GetDistortedPattern(index, true));
}
}
}
else
{
PreparedTrainingPatterns = TrainingPatterns;
}
}
private void Calculate()
{
(MainView.DataContext as PageViewModel).CommandToolBar[0].IsEnabled = false;
calculatePageViewModel.IsValid = false;
MainView.Status.Text = "Calculating...";
Mouse.OverrideCursor = Cursors.Wait;
DoubleImageData pattern;
if (calculatePageViewModel.UseTrainingSet)
{
if (calculatePageViewModel.UseDistortions)
{
pageViewModel.DataProvider.SeverityFactor = calculatePageViewModel.SeverityFactor;
pageViewModel.DataProvider.MaxRotation = calculatePageViewModel.MaxRotation;
pageViewModel.DataProvider.MaxScaling = calculatePageViewModel.MaxScaling;
pageViewModel.DataProvider.ElasticScaling = calculatePageViewModel.ElasticScaling;
pageViewModel.DataProvider.ElasticSigma = calculatePageViewModel.ElasticSigma;
pageViewModel.DataProvider.CalculateGaussianKernel();
pattern = new DoubleImageData(pageViewModel.DataProvider.TrainingPatterns[calculatePageViewModel.PatternIndex].Label, pageViewModel.DataProvider.GetDistortedPattern(calculatePageViewModel.PatternIndex, true));
}
else
pattern = pageViewModel.DataProvider.TrainingPatterns[calculatePageViewModel.PatternIndex];
}
else
{
if (calculatePageViewModel.UseDistortions)
{
pageViewModel.DataProvider.SeverityFactor = calculatePageViewModel.SeverityFactor;
pageViewModel.DataProvider.MaxRotation = calculatePageViewModel.MaxRotation;
pageViewModel.DataProvider.MaxScaling = calculatePageViewModel.MaxScaling;
pageViewModel.DataProvider.ElasticScaling = calculatePageViewModel.ElasticScaling;
pageViewModel.DataProvider.ElasticSigma = calculatePageViewModel.ElasticSigma;
pageViewModel.DataProvider.CalculateGaussianKernel();
pattern = new DoubleImageData(pageViewModel.DataProvider.TestingPatterns[calculatePageViewModel.PatternIndex].Label, pageViewModel.DataProvider.GetDistortedPattern(calculatePageViewModel.PatternIndex, false));
}
else
pattern = pageViewModel.DataProvider.TestingPatterns[calculatePageViewModel.PatternIndex];
}
Layers firstLayer = pageViewModel.NeuralNetwork.Layers.First();
for (int i = 0; i < pageViewModel.DataProvider.PatternSize; ++i)
{
//pattern[i] = ((pattern[i] * 0.6375D) + 0.5375D); // normalization (-0.1 --> 1.175)
firstLayer.Neurons[i].Output = pattern.Image[i];
}
string result = String.Empty;
result += "Input: " + pattern.Label.ToString() + "\r\n";
int bestIndex = 0;
System.Diagnostics.Stopwatch timer = new Stopwatch();
timer.Restart();
pageViewModel.NeuralNetwork.Calculate();
bestIndex = Recognised();
timer.Stop();
result += "\r\n";
Layers lastLayer = pageViewModel.NeuralNetwork.Layers.Last();
for (int i = 0; i < lastLayer.NeuronCount; i++)
{
result += i.ToString() + " ==> " + lastLayer.Neurons[i].Output.ToString("N17") + "\r\n";
}
result += "\r\n";
double patternMSE = GetPatternMSE(pattern.Label);
result += "Pattern MSE: " + patternMSE.ToString("N17") + "\r\n";
result += "Highest prob: " + bestIndex.ToString() + "\r\n";
if (bestIndex == pattern.Label)
result += "Correctly recognized";
else
result += "Not correctly recognized";
result += "\r\nTime: " + timer.ElapsedMilliseconds.ToString() + " ms.";
calculatePageViewModel.Result = result;
calculatePageViewModel.TabItems = new ObservableCollection<TabItem>();
foreach (Layers layer in pageViewModel.NeuralNetwork.Layers)
{
if (layer.UseMapInfo)
{
TabItem tb = new TabItem();
tb.BeginInit();
tb.UseLayoutRounding = true;
tb.SnapsToDevicePixels = true;
tb.Header = layer.LayerIndex.ToString();
tb.Content = layer.GetMappedLayerOutputs();
tb.EndInit();
calculatePageViewModel.TabItems.Add(tb);
}
}
Mouse.OverrideCursor = null;
MainView.Status.Text = "Ready";
calculatePageViewModel.IsValid = true;
(MainView.DataContext as PageViewModel).CommandToolBar[0].IsEnabled = true;
}
public void GenerateTrainingData(bool flipGrayscale, int width, int height)
{
FlipGrayscale = flipGrayscale;
PatternWidth = width;
PatternHeight = height;
PatternSize = width * height;
patternSize = PatternSize;
if (FlipGrayscale)
{
Background = 1;
Foreground = -1;
}
else
{
Background = -1;
Foreground = 1;
}
int xOffset = (PatternWidth - MNistWidth) / 2;
int yOffset = (PatternHeight - MNistHeight) / 2;
Divisor = 255D / (Math.Abs(Background) + Math.Abs(Foreground));
TrainingPatterns = new DoubleImageData[TrainingPatternsCount];
RandomTrainingPattern = new int[TrainingPatternsCount];
Parallel.For(0, TrainingPatternsCount, parallelOption, i =>
{
DoubleImageData pattern = new DoubleImageData(i, new double[PatternSize]);
for (int j = 0; j < PatternSize; j++)
{
pattern.Image[j] = Background;
}
TrainingPatterns[i] = pattern;
RandomTrainingPattern[i] = i;
});
Parallel.For(0, TrainingPatternsCount, parallelOption, i =>
{
byte temp;
DoubleImageData trainingPattern = TrainingPatterns[i];
byte[] image = MNISTTraining[i].Image;
trainingPattern.Label = MNISTTraining[i].Label;
for (int y = 0; y < MNistHeight; y++)
{
for (int x = 0; x < MNistWidth; x++)
{
temp = image[x + (y * MNistWidth)];
{
trainingPattern.Image[(x + xOffset) + ((y + yOffset) * PatternWidth)] = ((((double)temp) / Divisor) - 1D);
}
}
}
TrainingPatterns[i] = trainingPattern;
});
MNISTTraining = null;
TestingPatterns = new DoubleImageData[TestingPatternsCount];
Parallel.For(0, TestingPatternsCount, parallelOption, i =>
{
DoubleImageData pattern = new DoubleImageData(i, new double[PatternSize]);
for (int j = 0; j < PatternSize; j++)
{
pattern.Image[j] = Background;
}
TestingPatterns[i] = pattern;
});
Parallel.For(0, TestingPatternsCount, parallelOption, i =>
{
byte temp;
DoubleImageData testingPattern = TestingPatterns[i];
byte[] image = MNISTTesting[i].Image;
testingPattern.Label = MNISTTesting[i].Label;
for (int y = 0; y < MNistHeight; y++)
{
for (int x = 0; x < MNistWidth; x++)
{
temp = image[x + (y * MNistWidth)];
{
testingPattern.Image[(x + xOffset) + ((y + yOffset) * PatternWidth)] = ((((double)temp / Divisor)) - 1D);
}
}
}
TestingPatterns[i] = testingPattern;
});
MNISTTesting = null;
}
private int SetTestingPattern(int index)
{
if (pageViewModel.CurrentContext == ContextTypes.Training)
{
// Training PageView
Layers inputLayer = pageViewModel.NeuralNetwork.Layers.First();
for (int i = 0; i < pageViewModel.DataProvider.PatternSize; i++)
{
inputLayer.Neurons[i].Output = pageViewModel.DataProvider.TestingPatterns[index].Image[i];
}
return (pageViewModel.DataProvider.TestingPatterns[index].Label);
}
else
{
// Testing PageView
DoubleImageData pattern;
if (!testingPageViewModel.UseTrainingSet)
{
if (testingPageViewModel.UseDistortions)
pattern = new DoubleImageData(pageViewModel.DataProvider.TestingPatterns[index].Label, pageViewModel.DataProvider.GetDistortedPattern(index, false));
else
pattern = pageViewModel.DataProvider.TestingPatterns[index];
}
else
{
if (testingPageViewModel.UseDistortions)
pattern = new DoubleImageData(pageViewModel.DataProvider.TrainingPatterns[index].Label, pageViewModel.DataProvider.GetDistortedPattern(index, true));
else
pattern = pageViewModel.DataProvider.TrainingPatterns[index];
}
Layers inputLayer = pageViewModel.NeuralNetwork.Layers.First();
for (int i = 0; i < pageViewModel.DataProvider.PatternSize; i++)
{
inputLayer.Neurons[i].Output = pattern.Image[i];
}
return (pattern.Label);
}
}