internal static void ConvolutionalLayer2D(Index2 currentInput, ArrayView3D <float> weights, ArrayView2D <float> error, ArrayView2D <float> activatedPreviousLayer, ArrayView2D <float> bias, ArrayView <float> variables)
{
int radius = (int)variables[new Index1(2)];
int diameter = radius * 2 + 1;
var fac = error[currentInput] * variables[new Index1(0)];
float xBounds = error.Extent.X;
float yBounds = error.Extent.Y;
bias[currentInput] -= fac;
var baseIndex = new Index2(currentInput.X - radius, currentInput.Y - radius);
for (int i = 0; i < diameter; i++)
{
for (int j = 0; j < diameter; j++)
{
var asosInput = baseIndex.Add(new Index2(i, j));
if (asosInput.X < xBounds & asosInput.X >= 0 & asosInput.Y >= 0 & asosInput.Y < yBounds)
{
var adjustment = fac * activatedPreviousLayer[asosInput];
var d = activatedPreviousLayer[asosInput];
if (float.IsNaN(adjustment) | float.IsInfinity(adjustment))
{
;
}
weights[new Index3(currentInput, i * diameter + j)] -= adjustment;
}
}
}
}
internal static void ConvolutionalLayer2D(Index2 currentInput, ArrayView3D <float> weight, ArrayView2D <float> outputPreviousLayerActivated, ArrayView2D <float> sumInput, ArrayView2D <float> bias, ArrayView <float> variables)
{
int radius = (int)variables[new Index1(1)];
int diameter = radius * 2 + 1;
var baseIndex = new Index2(currentInput.X - radius, currentInput.Y - radius);
var xBounds = outputPreviousLayerActivated.Extent.X;
var yBounds = outputPreviousLayerActivated.Extent.Y;
float sum = bias[currentInput];
for (int i = 0; i < diameter; i++)
{
for (int j = 0; j < diameter; j++)
{
var asosInput = baseIndex.Add(new Index2(i, j));
if (asosInput.X < xBounds & asosInput.X >= 0 & asosInput.Y >= 0 & asosInput.Y < yBounds)
{
sum += weight[new Index3(currentInput, i * diameter + j)] * outputPreviousLayerActivated[asosInput];
var w = weight[new Index3(currentInput, i * diameter + j)]; var oA = outputPreviousLayerActivated[asosInput];
if (float.IsNaN(w * oA) | float.IsInfinity(w * oA))
{
;
}
}
}
}
sumInput[currentInput] = sum;
if (float.IsNaN(sum) | float.IsInfinity(sum))
{
;
}
}
private static void UpdateBKernel(Index2 index,
ArrayView2D <float> candidates,
ArrayView2D <float> psf2,
ArrayView <Pixel> pixel)
{
var indexCandidate = index.Add(new Index2(pixel[0].X, pixel[0].Y)).Subtract(psf2.Extent / 2);
if (index.InBounds(psf2.Extent) & indexCandidate.InBounds(candidates.Extent) & pixel[0].AbsDiff > 0)
{
candidates[indexCandidate] -= (psf2[index] * pixel[0].Sign * pixel[0].AbsDiff);
}
}
internal static void ConvolutionalLayer2D(Index2 currentInput, ArrayView2D <float> error, ArrayView2D <float> errorNextLayer, ArrayView3D <float> weightNextLayer, ArrayView2D <float> derived, ArrayView2D <float> should, ArrayView <float> variable)
{
int radius = (int)variable[new Index1(2)];
int diameter = radius * 2 + 1;
int xBounds = (int)errorNextLayer.Extent.X;
int yBounds = (int)errorNextLayer.Extent.Y;
var baseIndex = new Index2(currentInput.X - radius, currentInput.Y - radius);
float sum = 0.0f;
for (int i = 0; i < diameter; i++)
{
for (int j = 0; j < diameter; j++)
{
var asosInput = baseIndex.Add(new Index2(i, j));
if (asosInput.X < xBounds & asosInput.X >= 0 & asosInput.Y >= 0 & asosInput.Y < yBounds)
{
sum += errorNextLayer[asosInput] * weightNextLayer[new Index3(currentInput, i * diameter + j)];
}
}
}
error[currentInput] = sum * derived[currentInput];
var d = derived[currentInput]; var e = error[currentInput];
int c = 0;
while (error[currentInput] > 1 | error[currentInput] < -1)
{
error[currentInput] /= 10;
c++;
if (c > 20)
{
;
}
}
if (float.IsNaN(error[currentInput]) | float.IsInfinity(e) | sum > 1000 | sum < -1000)
{
;
}
}
