private void BindClassicNet()
{
AlltoAllBinding(InputLayer, HiddenLayers[0]);
if (HiddenLayers.Count > 1)
{
for (int i = 0; i < HiddenLayers.Count - 1; i++)
{
AlltoAllBinding(HiddenLayers[i], HiddenLayers[i + 1]);
}
}
AlltoAllBinding(HiddenLayers.Last(), OutputLayer);
}
private void BindReccNet()
{
AlltoAllBinding(InputLayer, HiddenLayers[0]);
if (HiddenLayers.Count > 1)
{
for (int i = 0; i < HiddenLayers.Count - 1; i++)
{
for (int j = i + 1; j < HiddenLayers.Count; j++)
{
AlltoAllBinding(HiddenLayers[i], HiddenLayers[j]);
}
}
}
AlltoAllBinding(HiddenLayers.Last(), OutputLayer);
}
public Network(int inputSize, int[] hiddenSizes, int outputSize, double learnRate = 0.4, double momentum = 0.9)
{
this.LearnRate = learnRate;
this.Momentum = momentum;
this.InputLayer = new List <Neuron>();
this.HiddenLayers = new List <List <Neuron> >();
this.OutputLayer = new List <Neuron>();
for (var i = 0; i < inputSize; i++)
{
InputLayer.Add(new Neuron());
}
var firstHiddenLayer = new List <Neuron>();
for (var i = 0; i < hiddenSizes[0]; i++)
{
firstHiddenLayer.Add(new Neuron(InputLayer));
}
HiddenLayers.Add(firstHiddenLayer);
for (var i = 1; i < hiddenSizes.Length; i++)
{
var hiddenLayer = new List <Neuron>();
for (var j = 0; j < hiddenSizes[i]; j++)
{
hiddenLayer.Add(new Neuron(HiddenLayers[i - 1]));
}
HiddenLayers.Add(hiddenLayer);
}
for (var i = 0; i < outputSize; i++)
{
OutputLayer.Add(new Neuron(HiddenLayers.Last()));
}
}
