public void Learn(NeuralLayer fromLayer)
{
for (int i = 0; i < Neurons.Count; i++)
{
var valueDerivatives = Activate.SoftMaxDer(Neurons[i].OutputDendrites.Select(d => d.Signal.Value).ToArray());
for (int d = 0; d < Neurons[i].OutputDendrites.Count; d++)
{
var currentDendrite = Neurons[i].OutputDendrites[d];
foreach (var fromNeuron in fromLayer.Neurons)
{
foreach (var fromDendrite in fromNeuron.InputDendrites)
{
currentDendrite.Gamma += fromDendrite.Gamma * currentDendrite.Weight;
}
}
currentDendrite.Gamma *= ActivationType == ActivationType.SoftMax
? valueDerivatives[d]
: Activate.GetActivationDer(ActivationType, currentDendrite.Signal.Value);
currentDendrite.UpdateWeightAndBias();
}
//foreach (var fromNeuron in fromLayer.Neurons)
//{
// Neurons[i].Gamma += fromNeuron.Gamma * Neurons[i].Weight;
//}
//var averageDendriteOutput = Neurons[i].OutputDendrites.Average(d => d.Signal.Value);
//Neurons[i].Gamma *= Activate.GetActivationDer(ActivationType, averageDendriteOutput);
//Neurons[i].UpdateWeightAndBias();
}
}
// Feedforward
public void Fire()
{
if (ActivationType == ActivationType.SoftMax)
{
var dendriteValues = new double[InputDendrites.Count];
for (int i = 0; i < InputDendrites.Count; i++)
{
dendriteValues[i] = (InputDendrites[i].Signal.Value * InputDendrites[i].Weight) + InputDendrites[i].Bias;
}
var output = Activate.SoftMax(dendriteValues);
for (int i = 0; i < OutputDendrites.Count; i++)
{
OutputDendrites[i].Signal.Value = output[i];
}
}
else
{
var coalescedValue = GetCoalescedSignal();
foreach (var output in OutputDendrites)
{
output.Signal.Value = Activate.GetActivation(ActivationType, coalescedValue);
}
}
}
public void Learn(double[] expectedOutput)
{
for (int i = 0; i < Neurons.Count; i++)
{
if (ActivationType == ActivationType.SoftMax)
{
var valueDerivatives = Activate.SoftMaxDer(Neurons[i].OutputDendrites.Select(d => d.Signal.Value).ToArray());
for (int d = 0; d < Neurons[i].OutputDendrites.Count; d++)
{
Neurons[i].OutputDendrites[d].Gamma = (Neurons[i].OutputDendrites[d].Signal.Value - expectedOutput[d]) * valueDerivatives[d];
Neurons[i].OutputDendrites[d].UpdateWeightAndBias();
}
}
else
{
//foreach (var dendrite in Neurons[i].OutputDendrites)
//{
// Neurons[i].Gamma += (dendrite.Signal.Value - expectedOutput[i]) * Activate.GetActivationDer(ActivationType, dendrite.Signal.Value);
//}
for (int d = 0; d < Neurons[i].OutputDendrites.Count; d++)
{
var signal = Neurons[i].OutputDendrites[d].Signal;
var value = (signal.Value - expectedOutput[d]) * Activate.GetActivationDer(ActivationType, signal.Value);
Neurons[i].OutputDendrites[d].Gamma = value;
Neurons[i].OutputDendrites[d].UpdateWeightAndBias();
}
}
//var averageDendriteOutput = Neurons[i].OutputDendrites.Average(d => d.Signal.Value);
//Neurons[i].Gamma =
// (averageDendriteOutput - expectedOutput[i]) *
// Activate.GetActivationDer(ActivationType, averageDendriteOutput);
//Neurons[i].UpdateWeightAndBias();
}
}