HiddenNeuron[] denseHiddenLayer(int numberOfNeurons, int numberOfInputOfEachNeuron, double learningRate = 1, string layerName = "hiddenNeuron")
{
HiddenNeuron[] on = new HiddenNeuron[numberOfNeurons];
for (int c = 0; c < numberOfNeurons; c++)
{
on [c] = new HiddenNeuron(numberOfInputOfEachNeuron, layerName + c.ToString(), learningRate);
}
return(on);
}
public HiddenLayer(int size, Network n) : base(size, n)
{
HiddenNeuron[] InputNeurons = new HiddenNeuron[size];
for (int i = 0; i < size; i++)
{
InputNeurons[i] = new HiddenNeuron(this);
InputNeurons[i].name = "HiddenNeuron " + i;
}
this.neurons = InputNeurons;
}
ParameterList <double> getInputList(HiddenNeuron[] hiddenLayer, HiddenNeuron nextNeuron)
{
ParameterList <double> list = new ParameterList <double> (hiddenLayer.Length);
for (int c = 0; c < list.Length; c++)
{
list [c] = hiddenLayer [c].Output(c, nextNeuron);
}
return(list);
}
private void CreateNeurons(ushort[] layerSizes, ITransferFunction[] transferFunctions)
{
if (layerSizes.Length != transferFunctions.Length)
{
throw new ArgumentException("Cannot make a network with these parameters.");
}
if (transferFunctions[0] != null)
{
throw new ArgumentException("Input layer transfer function must be null.");
}
Neurons = new Neuron[layerSizes.Length][];
for (byte l = 0; l < layerSizes.Length; l++)
{
// creates neurons in the input layer
if (l == 0)
{
Neurons[l] = new InputNeuron[layerSizes[l]];
for (ushort i = 0; i < layerSizes[l]; i++)
{
Neurons[l][i] = new InputNeuron(transferFunctions[l]);
}
}
// creates neurons in the hidden layer(s)
else if (l < layerSizes.Length - 1)
{
Neurons[l] = new HiddenNeuron[layerSizes[l]];
for (ushort i = 0; i < layerSizes[l]; i++)
{
Neurons[l][i] = new HiddenNeuron(transferFunctions[l]);
}
}
// creates neurons in the output layer
else
{
Neurons[l] = new OutputNeuron[layerSizes[l]];
for (ushort i = 0; i < layerSizes[l]; i++)
{
Neurons[l][i] = new OutputNeuron(transferFunctions[l]);
}
}
}
}
private void CreateHiddenLayers()
{
for (int j = 0; j < Topology.HiddenLayersCount; j++)
{
var hiddenNeuron = new Neuron[Topology.HiddenLayers[j]];
var preLastLayer = Layers[j];
for (int i = 0; i < Topology.HiddenLayers[j]; i++)
{
var neuron = new HiddenNeuron(preLastLayer.NeuronsCountInLayer);
hiddenNeuron[i] = neuron;
Thread.Sleep(10);
}
var hiddenLayer = new Layer(hiddenNeuron);
Layers[j + 1] = hiddenLayer;
}
}
internal virtual IList <ICollection <IHiddenNeuron> > BuildHiddenLayers(IEnumerable <INeuron> previousLayer, HiddenLayerSettings settings, ushort recurrentInputs = 0)
{
var tempLayer = new List <ICollection <IHiddenNeuron> >(settings.LayersCount);
IEnumerable <INeuron> prevLayer = previousLayer;
for (int i = 0; i < settings.LayersCount; i++)
{
var tempHidden = new List <IHiddenNeuron>(settings.NeuronsCount);
for (int j = 0; j < settings.NeuronsCount; j++)
{
var neuron = new HiddenNeuron(settings.FunctionType, recurrentInputs);
tempHidden.Add(neuron);
foreach (var inNeuron in prevLayer)
{
ConnectAxon(inNeuron, neuron, settings.NeuronsCount);
}
}
if (settings.HasBiasNeuron)
{
var biasNeuron = new BiasNeuron();
tempHidden.Add(biasNeuron);
foreach (var inNeuron in previousLayer)
{
ConnectAxon(inNeuron, biasNeuron, settings.NeuronsCount);
}
}
prevLayer = tempHidden;
tempLayer.Add(tempHidden);
}
return(tempLayer);
}
