public void RunGAN()
{
ClearNetworks();
dLosses = 0; tie = 0; gLosses = 0;
System.Random random = new System.Random();
//the dimension in this case will have a depth of 4 since it is an ARGB image, thus having 3 color channels and one alpha channel
inputDimension = new Dimension(imageCount: 1, depth: 4, width: inputImages[0].width, height: inputImages[0].height);
inputVolume = Utils.Init4dArr(1, 4, inputDimension.width, inputDimension.height);
noiseVolume = Utils.Init4dArr(1, 4, inputDimension.width, inputDimension.height);
CreateDNet();
CreateGNet();
for (int i = 0; i < epochs; i++)
{
var pixels = inputImages[random.Next(0, inputImages.Length - 1)].GetPixels();
for (int y = 0; y < inputDimension.height; y++)
{
for (int x = 0; x < inputDimension.width; x++)
{
inputVolume[0][0][x][y] = pixels[x + y * inputDimension.width].a;
inputVolume[0][1][x][y] = pixels[x + y * inputDimension.width].r;
inputVolume[0][2][x][y] = pixels[x + y * inputDimension.width].g;
inputVolume[0][3][x][y] = pixels[x + y * inputDimension.width].b;
}
}
for (int y = 0; y < inputDimension.height; y++)
{
for (int x = 0; x < inputDimension.width; x++)
{
noiseVolume[0][0][x][y] = Mathf.PerlinNoise((float)x / (float)inputDimension.width * (float)random.NextDouble(), (float)y / (float)inputDimension.height);
noiseVolume[0][1][x][y] = Mathf.PerlinNoise((float)x / (float)inputDimension.width * (float)random.NextDouble(), (float)y / (float)inputDimension.height);
noiseVolume[0][2][x][y] = Mathf.PerlinNoise((float)x / (float)inputDimension.width * (float)random.NextDouble(), (float)y / (float)inputDimension.height);
noiseVolume[0][3][x][y] = Mathf.PerlinNoise((float)x / (float)inputDimension.width * (float)random.NextDouble(), (float)y / (float)inputDimension.height);
}
}
var detectedImage = dNet.ForwardPropagation(input: inputVolume);
trueImage = Utils.Init4dArr(detectedImage.Length, detectedImage[0].Length, detectedImage[0][0].Length, detectedImage[0][0][0].Length);
falseImage = trueImage;
trueImage[0][0][0][0] = 1;
guess1 = detectedImage[0][0][0][0];
var generatedImage = gNet.ForwardPropagation(noiseVolume);
var generatedImage2 = dNet.ForwardPropagation(generatedImage);
guess2 = generatedImage2[0][0][0][0];
if (guess1 > guess2)
{
gLosses++;
gNet.GBackwardPropagation(actual: generatedImage, expected: inputVolume);
}
else if (guess1 == guess2)
{
tie++;
Debug.Log("Tie!");
SetPixels(textureFromNoise, generatedImage);
break;
}
else
{
dLosses++;
dNet.DBackwardPropagation(actual: detectedImage, expected: trueImage);
dNet.DBackwardPropagation(actual: generatedImage2, expected: falseImage);
}
}
}