public void Forward(NeuralLayer preLayer)
{
foreach (var neuron in Neurons)
{
neuron.Fire(preLayer);
}
}
public void Fire(NeuralLayer preLayer)
{
Output = Sum(preLayer);
Output = ActivationFunction.Sigmoid(Output);
// Console.WriteLine($"Activation: {Output}");
// Console.WriteLine();
}
public void AddLayer(NeuralLayer layer)
{
int dendriteCount = 1;
if (Layers.Count > 0)
{
dendriteCount = Layers.Last().Neurons.Count;
}
foreach (var element in layer.Neurons)
{
for (int i = 0; i < dendriteCount; i++)
{
element.InputDendrites.Add(new Dendrite());
}
}
}
private double Sum(NeuralLayer preLayer)
{
double computeValue = 0.0f;
for (int i = 0; i < preLayer.Neurons.Count; i++)
{
var neuron = preLayer.Neurons[i];
var dendrite = InputDendrites[i];
computeValue += neuron.Output * dendrite.Weight;
// Console.WriteLine($"{neuron.Output} * {neuron.InputDendrites[0].Weight} = {neuron.Output * neuron.InputDendrites[0].Weight}");
}
// Console.WriteLine($"Sum = {computeValue}");
return(computeValue);
}
private void CreateNetwork(NeuralLayer connectingFrom, NeuralLayer connectingTo)
{
// Initial a rand weight for input layer
var rand = new Random();
foreach (var to in connectingTo.Neurons)
{
int i = 0;
to.InputDendrites = new List <Dendrite>();
foreach (var from in connectingFrom.Neurons)
{
i++;
to.InputDendrites.Add(new Dendrite()
{
Pulse = from.Output,
Weight = rand.NextDouble() // i / 10.0 + i / 100.0
});
}
}
}
/// <summary>
/// Perform forward-pass through network and update node outputs
/// </summary>
private void ForwardPropagation(NDArray <double> data)
{
//Set the input data into the first layer
Layers[0].Neurons.Select((x, i) => x.Output = data[i]).ToList();
NeuralLayer preLayer = null;
foreach (var layer in Layers)
{
//Skip first layer as it is input
if (preLayer == null)
{
preLayer = layer;
continue;
}
layer.Forward(preLayer);
preLayer = layer;
}
}
