/// <summary>
/// Visualizes the full neural network for the most recently executed input.
/// </summary>
/// <param name="network">The neural network to draw.</param>
/// <param name="input">Input image that was fed into the network.</param>
/// <returns>An object that can be assigned directly to an ImageControl's Source property.</returns>
public static ImageSource DrawNetworkSingle(NeuralNetwork network, float[,] input)
{
int inputWidth = input.GetLength(0);
int inputHeight = input.GetLength(1);
const int inputScaleFact = 2;
int networkMaxNeurons = network.LayerSizes.Skip(1).Max();
int lastWeightsCount = network.LayerSizes[network.LayerCount - 2];
int lastWeightsWidth = (network.LayerCount == 2 ? inputWidth : lastWeightsCount);
int lastWeightsHeight = lastWeightsCount / lastWeightsWidth;
int width = /*inputWidth * inputScaleFact +
* sectionPadding +*/(network.LayerCount - 1) * neuronFullSize +
sectionPadding + lastWeightsWidth;
int height = Math.Max(Math.Max(inputHeight * inputScaleFact,
networkMaxNeurons * neuronFullSize),
network.LayerSizes.Last() * (lastWeightsHeight + neuronPadding));
var output = new WriteableBitmap(width, height, 96.0, 96.0, PixelFormats.Rgb24, null);
byte[] bytes = new byte[width * height * 3];
int baseX = 0;
// Draw input image, scaled by a factor 2
/*drawInput(input, bytes, inputScaleFact, baseX, 0, width);
* baseX += inputWidth * inputScaleFact + sectionPadding;*/
// Draw neuron values (except input)
for (int k = 1; k < network.LayerCount; k++)
{
for (int i = 0; i < network.LayerSizes[k]; i++)
{
int baseY = i * neuronFullSize;
float value = Truncate1(network.Layers[k][i]); // force in interval [0, 1]
byte r = (byte)Truncate255(128.0f + value * 128.0f);
byte g = (byte)Truncate255(value * 256.0f);
byte b = (byte)Truncate255(value * 64.0f);
for (int y = baseY; y < baseY + neuronSize; y++)
{
for (int x = baseX; x < baseX + neuronSize; x++)
{
bytes[(x + y * width) * 3] = r;
bytes[(x + y * width) * 3 + 1] = g;
bytes[(x + y * width) * 3 + 2] = b;
}
}
}
baseX += neuronFullSize;
}
baseX += sectionPadding;
// Draw weights of last two layers
var lastWeights = network.Weights.Last();
for (int j = 0; j < network.LayerSizes[network.LayerCount - 1]; j++)
{
int baseY = j * (lastWeightsHeight + neuronPadding);
for (int i = 0; i < network.LayerSizes[network.LayerCount - 2]; i++)
{
int y = baseY + (i / lastWeightsWidth);
int x = baseX + (i % lastWeightsWidth);
float value = Truncate1((lastWeights[i, j] + 1.0f) / 2.0f); // force in interval [0, 1]
byte r = (byte)Truncate255(128.0f + value * 128.0f);
byte g = (byte)Truncate255(value * 256.0f);
byte b = (byte)Truncate255(value * 64.0f);
bytes[(x + y * width) * 3] = r;
bytes[(x + y * width) * 3 + 1] = g;
bytes[(x + y * width) * 3 + 2] = b;
}
}
output.WritePixels(new Int32Rect(0, 0, width, height), bytes, width * 3, 0);
return(output);
}
public BackpropNetworkTrainer(NeuralNetwork network, float learningRate)
{
Network = network;
LearningRate = learningRate;
}
