public void RecognizeImage(NormalizedImage image)
{
Debug.Assert(image != null);
image.PadCanvas(Network.InputImageWidth, Network.InputImageHeight);
var output = _network.Calculate(image.RawData);
RefreshNetworkImage();
var builder = new StringBuilder();
var maxValueIndex = 0;
for (var i = 0; i < output.Length; i++)
{
builder.AppendFormat("{0}: {1:0.00}", i, output[i]).AppendLine();
if (output[i] > output[maxValueIndex]) maxValueIndex = i;
}
builder.AppendFormat("Ответ сети: {0}", maxValueIndex).AppendLine();
History = builder + History;
}
public void TestNetwork(MNistImage[] images, CancellationToken cancellation)
{
Debug.AssertNotNull(images);
Debug.AssertNotNull(cancellation);
if (_statRefresher != null && _statRefresher.Started) return;
_cancelTrainToken = cancellation;
var normalImages = new NormalizedImage[images.Length];
for (var i = 0; i < images.Length; i++)
{
normalImages[i] = new NormalizedImage(images[i]);
normalImages[i].PadCanvas(Network.InputImageWidth, Network.InputImageHeight);
}
_testInfoBuffer.Clear();
_imageProcessed = 0;
_errors.Clear();
_totalMSE = 0.0;
_statRefresher = new ActionRepeater(PrintTestingInfo, 1000.0);
_statRefresher.Start(false);
TestProcess(normalImages);
_statRefresher.Stop(true);
History = txtStatistics.Text;
}
private void TrainProcess(NormalizedImage[] images)
{
while (!_cancelTrainToken.IsCancellationRequested)
{
ArrayHelper<NormalizedImage>.Shuffle(images);
for (var i = 0; i < images.Length && !_cancelTrainToken.IsCancellationRequested; i++)
{
var image = images[i]; //TODO foreach
image.PadCanvas(Network.InputImageWidth, Network.InputImageHeight);
var desiredOutput = ArrayHelper<double>.CreateScalar(10, -1.0, images[i].Label.Value, 1.0);
_network.Train(image.RawData, desiredOutput);
}
}
}
private void TestProcess(NormalizedImage[] images)
{
for (_imageProcessed = 0; _imageProcessed < images.Length; _imageProcessed++)
{
var desiredOutput = ArrayHelper<double>.CreateScalar(10, -1.0, images[_imageProcessed].Label.Value, 1.0);
var output = _network.Calculate(images[_imageProcessed].RawData);
var answer = ArrayMath.MaxValueIndex(output);
var mse = ArrayMath.CalculateMSE(output, desiredOutput);
if (answer != images[_imageProcessed].Label) _errors.Push(_imageProcessed);
_totalMSE += mse;
if (_cancelTrainToken.IsCancellationRequested) break;
}
}
public void TrainNetwork(MNistImage[] images, CancellationToken cancellation)
{
Debug.AssertNotNull(images);
Debug.Assert(cancellation != null);
if (_statRefresher != null && _statRefresher.Started) return;
_cancelTrainToken = cancellation;
var normalImages = new NormalizedImage[images.Length];
for (var i = 0; i < images.Length; i++)
normalImages[i] = new NormalizedImage(images[i]);
_statRefresher = new ActionRepeater(PrintTrainingInfo, 1000.0);
_statRefresher.Start(false);
TrainProcess(normalImages);
_statRefresher.Stop(true);
History = txtStatistics.Text;
}
private void UdMNistImageIndexValueChanged(object sender, EventArgs e)
{
var index = (int) udMNistImageIndex.Value;
if (nistImages != null)
{
if (0 <= index && index < nistImages.Length)
{
imgMNistImage.Image = nistImages[index].ToBitmap().Resize(2, InterpolationMode.NearestNeighbor);
lblMNistImage.Text = "Подпись базы: " + nistImages[index].Label;
currentInputImage = new NormalizedImage(nistImages[index]);
}
}
else
{
MessageBox.Show("База MNist не загружена", "Изображение не получено",
MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
private void BtnOpenImageClick(object sender, EventArgs e)
{
var dialog = new OpenFileDialog {Title = "Выберите файл изображения", Filter = "Файл изображения|*.jpg"};
if (dialog.ShowDialog() != DialogResult.OK) return;
try
{
var bitmap = (new Bitmap(dialog.FileName)).Resize(new Size(28, 28), InterpolationMode.High);
currentInputImage = new NormalizedImage(bitmap);
imgMNistImage.Image = currentInputImage.ToBitmap().Resize(2, InterpolationMode.NearestNeighbor);
lblImageInfo.Text = string.Format("Загружено: {0}", dialog.SafeFileName);
lblImageInfo.ForeColor = Color.DarkGreen;
DisablePanels(PanelsState.AllEnabled);
}
catch
{
}
}
