public Network(int inputSize, int layerSize, int layerCount, int outputSize)
{
InputSize = inputSize;
OutputSize = outputSize;
LayerCount = layerCount;
if (layerCount > 0)
{
HiddernLayers.Add(new NeuronLayer(layerSize, InputSize));
for (int x = 1; x < LayerCount; x++)
{
HiddernLayers.Add(new NeuronLayer(layerSize, layerSize));
}
LastLayer = new NeuronLayer(OutputSize, layerSize);
}
else if (layerCount == 0)
{
LastLayer = new NeuronLayer(OutputSize, InputSize);
}
else
{
throw new ArgumentOutOfRangeException("Input size cannot be negative");
}
}
public Vector Propergate(Vector input)
{
// Validate Input
if (input.Count != InputSize)
{
throw new ArgumentOutOfRangeException();
}
if (LayerCount == 0) // easy case
{
LastLayer.Input = input;
}
else // move data through layers
{
HiddernLayers[0].Input = input;
for (int currentLayer = 1; currentLayer < LayerCount; currentLayer++)
{
HiddernLayers[currentLayer].Input = HiddernLayers[currentLayer - 1].Output;
}
LastLayer.Input = HiddernLayers.Last().Output;
}
return(LastLayer.Output);
}
public double TrainToData(Vector input, Vector requiredOutput, double learningSpeed)
{
// Validate Input
if (input.Count != InputSize)
{
throw new ArgumentOutOfRangeException();
}
Vector result = Propergate(input);
LastLayer.Correction = requiredOutput - result;
// back propergate corrections
if (LayerCount == 0)
{
// easy
}
else
{
HiddernLayers.Last().Correction = LastLayer.BackPropergation;
for (int currectLayer = LayerCount - 2; currectLayer >= 0; currectLayer--)
{
HiddernLayers[currectLayer].Correction = HiddernLayers[currectLayer + 1].BackPropergation;
}
}
LastLayer.CorrectForError(learningSpeed);
HiddernLayers.ForEach(layer => layer.CorrectForError(learningSpeed));
return((requiredOutput - result).Average(correction => System.Math.Abs(correction)));
}