public List <Synapse> BuildSynapses()
{
Synapses = new List <Synapse>();
var seq = 0;
Hiddens.ForEach(p => p.Seq = ++seq);
var hiddens = Hiddens.OrderBy(p => p.Seq);
var orig = Inputs.ToList();
var id = 0;
foreach (var h in hiddens)
{
var dest = h;
Build(orig, dest, ref id);
orig = dest;
}
Build(orig, Outputs, ref id);
return(Synapses);
}
public void FeedForward()
{
CalculateNeurons(Inputs);
foreach (var hiddens in Hiddens.OrderBy(p => p.Seq))
{
CalculateNeurons(hiddens);
}
}
private void CalculateHiddenErrors()
{
foreach (var hiddens in Hiddens.OrderByDescending(p => p.Seq))
{
foreach (var neuron in hiddens)
{
neuron.Error = Synapses.Where(p => p.Orig == neuron).Sum(p => p.Dest.Error * dActivation(p.Dest.Value) * p.Weight);
}
}
}
public void BackPropagation()
{
CalculateErrors();
CalculateSynapse(Outputs);
foreach (var hiddens in Hiddens.OrderByDescending(p => p.Seq))
{
CalculateSynapse(hiddens);
}
}
public void FeedForward(float[] inputs)
{
ActivateFunction activateFunction = ActivateFunctions.Sigmoid;
Hiddens[0] = ApplyActivationFunction(Task.Run(() => Feed(WeightsIH, Matrix.ArrayToMatrix(inputs), Bias[0, 0])).Result, activateFunction);
for (int i = 0; i < Hiddens.Count() - 1; i++)
{
Hiddens[i + 1] = ApplyActivationFunction(Task.Run(() => Feed(WeightsH[i], Matrix.ArrayToMatrix(Hiddens[i].Items), Bias[i + 1, 0])).Result, activateFunction);
}
var lastBias = Bias.GetLength(0) - 1;
Outputs = ApplyActivationFunction(Task.Run(() => Feed(WeightsHO, Matrix.ArrayToMatrix(Hiddens.LastOrDefault().Items), Bias[lastBias, 0])).Result, activateFunction);
}
public IEnumerable <CstToken> TokenWithHiddens()
{
return(Hiddens.Concat(Value));
}
