OutputNeuron[] denseOutputLayer(int numberOfNeurons, int numberOfInputOfEachNeuron, double learningRate = 1, string layerName = "outputNeuron")
{
OutputNeuron[] on = new OutputNeuron[numberOfNeurons];
for (int c = 0; c < numberOfNeurons; c++)
{
on [c] = new OutputNeuron(numberOfInputOfEachNeuron, layerName + c.ToString(), learningRate);
}
return(on);
}
public OutputLayer(int size, Network n) : base(size, n)
{
OutputNeuron[] InputNeurons = new OutputNeuron[size];
for (int i = 0; i < size; i++)
{
InputNeurons[i] = new OutputNeuron(this);
InputNeurons[i].name = "HiddenNeuron " + i;
}
this.neurons = InputNeurons;
}
ParameterList <double> getInputList(HiddenNeuron[] hiddenLayer, OutputNeuron 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 CreateOutputLayer()
{
var outputNeurons = new Neuron[Topology.OutputCount];
var preLastLayer = Layers[Topology.TotalLayersCount - 2];
for (int i = 0; i < Topology.OutputCount; i++)
{
var neuron = new OutputNeuron(preLastLayer.NeuronsCountInLayer, NeuronType.Output);
outputNeurons[i] = neuron;
}
var outputLayer = new Layer(outputNeurons, NeuronType.Output);
Layers[Topology.TotalLayersCount - 1] = outputLayer;
}
internal IEnumerable <IOutputNeuron> BuildOutputNeurons(IEnumerable <INeuron> prevLayer, ushort count, ActivationTypes type)
{
var tempOutputLayer = new List <IOutputNeuron>(count);
for (int i = 0; i < count; i++)
{
var neuron = new OutputNeuron(type);
tempOutputLayer.Add(neuron);
foreach (var inNeuron in prevLayer)
{
ConnectAxon(inNeuron, neuron, count);
}
}
((List <IOutputNeuron>) this.OutputNeurons).AddRange(tempOutputLayer);
return(tempOutputLayer);
}
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]);
}
}
}
}
public String GetName()
{
if (OutputNeuron != null)
{
if (InputNeuron != null)
{
return(String.Format("Source: {0}, Target: {1}", InputNeuron.GetName(), OutputNeuron.GetName()));
}
else
{
return(String.Format("Input layer: {0}, Target: {1}", InputIndex, OutputNeuron.GetName())); //input
}
}
else
{
return("");
}
}
