private void InitializeNetwork()
{
IsLearningRightNow = false;
wpfGraphics.Clear();
X = new double[10000, 2];
Y = new double[10000, 3];
for (int x = 0; x < 100; x++)
{
for (int y = 0; y < 100; y++)
{
X[x * 100 + y, 0] = (double)(((double)(x - 50)) / 50);
X[x * 100 + y, 1] = (double)(((double)(y - 50)) / 50);
Y[x * 100 + y, 0] = getDouble((GetPixelColor(TestBitmap, x, y).R));
Y[x * 100 + y, 1] = getDouble((GetPixelColor(TestBitmap, x, y).G));
Y[x * 100 + y, 2] = getDouble((GetPixelColor(TestBitmap, x, y).B));
}
}
Layer[] layers = new Layer[2 + HiddenLayers.Count()];
layers[0] = new Layer(LayerType.Input, X, ActivationFunction.RELU);
for (int i = 1; i < HiddenLayers.Count() + 1; i++)
{
layers[i] = HiddenLayers[i - 1];
}
layers[layers.Count() - 1] = new Layer(LayerType.Output, Y, ActivationFunction.Tanh);
nn = new NEngine(layers, Y, LearningRate, 0, BatchSize);
nn.ForwardPropagation();
}
private void AddNewPoint(MouseButton mb)
{
IsLearningRightNow = false;
Point p = new Point(PanelX, PanelY);
if (mb == MouseButton.Left)
{
BluePoint.Add(p);
}
else
{
OrangePoint.Add(p);
}
wpfGraphics.Clear();
DrawPoints();
double[,] X = new double[BluePoint.Count() + OrangePoint.Count(), 2];
double[,] Y = new double[BluePoint.Count() + OrangePoint.Count(), 2];
for (int i = 0; i < BluePoint.Count(); i++)
{
X[i, 0] = BluePoint[i].X / 300;
X[i, 1] = BluePoint[i].Y / 300;
Y[i, 0] = 1;
Y[i, 1] = -1;
}
for (int i = BluePoint.Count(); i < BluePoint.Count() + OrangePoint.Count(); i++)
{
X[i, 0] = (OrangePoint[i - BluePoint.Count()].X / 300);
X[i, 1] = (OrangePoint[i - BluePoint.Count()].Y / 300);
Y[i, 0] = -1;
Y[i, 1] = 1;
}
MatrixExtensions.Unsort(ref X, ref Y);
X.SplitMatrix(out X2, out TestX, LearnToTest);
Y.SplitMatrix(out Y2, out TestY, LearnToTest);
Layer[] layers = new Layer[2 + HiddenLayers.Count()];
layers[0] = new Layer(LayerType.Input, X2, ActivationFunction.Tanh);
for (int i = 1; i < HiddenLayers.Count() + 1; i++)
{
layers[i] = HiddenLayers[i - 1];
}
layers[layers.Count() - 1] = new Layer(LayerType.Output, Y2, ActivationFunction.Tanh);
nn = new NEngine(layers, Y2, LearningRate, 0);
nn.ForwardPropagation().Print();
}