static void Main(string[] args)
{
INeuralDataSet trainingSet = new BasicNeuralDataSet(AndInput, AndIdeal);
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(new ActivationRamp(), true, 25));
network.AddLayer(new BasicLayer(new ActivationRamp(), true, 75));
network.AddLayer(new BasicLayer(new ActivationRamp(), true, 50));
network.AddLayer(new BasicLayer(new ActivationRamp(), true, 20));
network.Structure.FinalizeStructure();
network.Reset();
ITrain train = new Backpropagation(network, trainingSet, 0.02, 0.3);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine($"{train.Error}");
epoch++;
} while ((epoch < MaxEpoch) && (train.Error > AcceptableError));
var input = new BasicMLData(new double[25] {
1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1
});
int best = network.Winner(input);
Console.WriteLine($"Rozpoznano: {_literki[best]}");
foreach (IMLDataPair pair in trainingSet)
{
IMLData output = network.Compute(pair.Input);
Console.WriteLine($"wynik : { output[0].ToString("0.000")} {output[1].ToString("0.000")} {output[2].ToString("0.000")} {output[3].ToString("0.000")} {output[4].ToString("0.000")} {output[5].ToString("0.000")} {output[6].ToString("0.000")} {output[7].ToString("0.000")} {output[8].ToString("0.000")} {output[9].ToString("0.000")} {output[10].ToString("0.000")} {output[11].ToString("0.000")} {output[12].ToString("0.000")} {output[13].ToString("0.000")} {output[14].ToString("0.000")} {output[15].ToString("0.000")} {output[16].ToString("0.000")} {output[17].ToString("0.000")} {output[18].ToString("0.000")} {output[19].ToString("0.000")}");
}
Console.ReadKey();
}
public void ProcessWhatIs()
{
String filename = GetArg("image");
try
{
var img = new Bitmap(filename);
var input = new ImageMLData(img);
input.Downsample(downsample, false, downsampleHeight,
downsampleWidth, 1, -1);
int winner = network.Winner(input);
app.WriteLine("What is: " + filename + ", it seems to be: "
+ neuron2identity[winner]);
}
catch (Exception e)
{
app.WriteLine("Error loading: " + filename + ", " + e.Message);
}
}
private void RecCanvas_MouseUp(object sender, MouseButtonEventArgs e)
{
recCanvas_drawing = false;
var size = new Size(RecCanvas.ActualWidth, RecCanvas.ActualHeight);
// Measure and arrange the surface
// VERY IMPORTANT
RecCanvas.Measure(size);
RecCanvas.Arrange(new Rect(size));
var renderBitmap = new RenderTargetBitmap((int)size.Width, (int)size.Height, 96d, 96d, PixelFormats.Pbgra32);
renderBitmap.Render(RecCanvas);
MemoryStream stream = new MemoryStream();
BitmapEncoder encoder = new BmpBitmapEncoder();
encoder.Frames.Add(BitmapFrame.Create(renderBitmap));
encoder.Save(stream);
var bitmap = new Drawing.Bitmap(stream);
var downsample = new Downsampler();
var result = downsample.DownSample(bitmap, DIGIT_HEIGHT, DIGIT_WIDTH);
var image = new Drawing.Bitmap(DIGIT_WIDTH, DIGIT_HEIGHT);
for (var i = 0; i < DIGIT_HEIGHT; ++i)
{
for (var j = 0; j < DIGIT_WIDTH; ++j)
{
image.SetPixel(j, i, Drawing.Color.FromArgb(
255,
(int)result[i * DIGIT_WIDTH + j],
(int)result[i * DIGIT_WIDTH + j],
(int)result[i * DIGIT_WIDTH + j]
));
}
}
RecComp.Source = Imaging.CreateBitmapSourceFromHBitmap(
image.GetHbitmap(),
IntPtr.Zero,
Int32Rect.Empty,
BitmapSizeOptions.FromEmptyOptions());
if (network == null)
{
return;
}
var input = new ImageMLData(bitmap);
input.Downsample(downsample, false, DIGIT_HEIGHT, DIGIT_WIDTH, 1, -1);
int winner = network.Winner(input);
RecDigit.Text = winner.ToString();
var data = network.Compute(input);
for (var i = 0; i < DIGITS_COUNT; ++i)
{
Results[i] = new ResultObject
{
Char = i.ToString(),
Result = (data[i] + 1) / 2
};
}
}
