public void AddLayer(NeuralLayer layer)
{
// Don't give the first layer any dendrites yet. They will
// be determined on FeedForward by the number of inputs.
// Normally, you will want to have the same number of neurons as
// inputs, but this adds flexibility for trying out different approaches.
if (Layers.Count == 0)
{
Layers.Add(layer);
return;
}
var currentLastLayer = Layers.Last();
foreach (var newLayerNeuron in layer.Neurons)
{
foreach (var lastLayerNeuron in currentLastLayer.Neurons)
{
foreach (var lastLayerOutput in lastLayerNeuron.OutputDendrites)
{
var newLayerNeuronDendrite = new Dendrite {
Signal = lastLayerOutput.Signal
};
newLayerNeuron.InputDendrites.Add(newLayerNeuronDendrite);
}
// Each neuron in the new layer is assigned a dendrite
// corresponding to each neuron's output in the layer before.
//var newLayerNeuronDendrite = new Dendrite { Signal = lastLayerNeuron.Output };
//newLayerNeuron.InputDendrites.Add(newLayerNeuronDendrite);
}
newLayerNeuron.ConfigureOutputDendrites();
}
Layers.Add(layer);
}
private void UpdateFirstLayerInputs(double[] inputs)
{
// Add dendrites to the first layer according to the number of inputs,
// but only if dendrites do not yet exist. An interesting twist on this
// might be to allow the inputs to vary so, say, different image sizes
// could be passed in. Not sure how well that would work, and it would
// require some changes to the existing code (weights, biases handled at
// network model level rather than Neuron instantiation).
var firstLayer = Layers.First();
if (firstLayer.Neurons.Sum(n => n.InputDendrites.Count()) < 1)
{
var dendritesPerNeuron = inputs.Count() / firstLayer.Neurons.Count();
foreach (var neuron in Layers.First().Neurons)
{
for (int i = 0; i < dendritesPerNeuron; i++)
{
var dendrite = new Dendrite();
neuron.InputDendrites.Add(dendrite);
}
}
}
// Assign values to all first layer inputs
var dendrites = firstLayer.Neurons.SelectMany(n => n.InputDendrites).ToArray();
for (int i = 0; i < inputs.Count(); i++)
{
dendrites[i].Signal.Value = inputs[i];
}
}
