public HiddenLayer CreateHiddenLayer()
{
HiddenLayer hiddenLayer;
hiddenLayer = new HiddenLayer();
HiddenLayers.Add(hiddenLayer);
return(hiddenLayer);
}
public NeuralNetwork(int inputNeurons, int hiddenNeurons, int outputNeurons, int hidLayers)
{
InputLayer = new InputLayer(inputNeurons);
for (int i = 0; i < hidLayers; i++)
{
HiddenLayers.Add(new HiddenLayer(hiddenNeurons));
}
OutputLayer = new OutputLayer(outputNeurons);
Generate();
CreateSubscribes();
}
public void InitializeHiddenLayers(int[] hiddenNeuronCount)
{
InputLayer.Clear();
HiddenLayers.Clear();
OutputLayer.Clear();
foreach (var neuronsCount in hiddenNeuronCount)
{
var newHiddenLayer = new Matrix(1, neuronsCount);
HiddenLayers.Add(newHiddenLayer);
}
}
void AddSynapse(Synapse template)
{
if (AllSynapses.ContainsKey(template.InnovationNo))
{
Debug.Log("wtf??");
Debug.Break();
}
Neuron neuron1 = null, neuron2 = null;
if (template.InputNeuron.InnovationNo <= InputLayer.Count)
{
neuron1 = InputLayer[template.InputNeuron.InnovationNo - 1];
}
else
{
if (HiddenLayers.ContainsKey(template.InputNeuron.InnovationNo))
{
neuron1 = HiddenLayers[template.InputNeuron.InnovationNo];
}
else
{
neuron1 = new Neuron(template.InputNeuron);
HiddenLayers.Add(neuron1.InnovationNo, neuron1);
}
}
if (template.OutputNeuron.InnovationNo <= InputLayer.Count + OutputLayer.Count)
{
neuron2 = OutputLayer[template.OutputNeuron.InnovationNo - InputLayer.Count - 1];
}
else
{
if (HiddenLayers.ContainsKey(template.OutputNeuron.InnovationNo))
{
neuron2 = HiddenLayers[template.OutputNeuron.InnovationNo];
}
else
{
neuron2 = new Neuron(template.OutputNeuron);
HiddenLayers.Add(neuron2.InnovationNo, neuron2);
}
}
Synapse newSyn = new Synapse(neuron1, neuron2, template.InnovationNo);
newSyn.Weight = template.Weight;
AllSynapses.Add(newSyn.InnovationNo, newSyn);
//Debug.Log("Koniec add synapse template");
}
public NeuralGeneNode AddHiddenNode(NeuralActivationFunction _neuralActivationFunctions)
{
if (HiddenLayers == null)
{
HiddenLayers = new List <NeuralGeneNode>();
}
if (nodes == null)
{
nodes = new List <NeuralGeneNode>();
}
NeuralGeneNode tmpNode = new NeuralGeneNode(_neuralActivationFunctions, nodes.Count + 1, NeuralNodeType.Hidden);
HiddenLayers.Add(tmpNode);
nodes.Add(tmpNode);
return(tmpNode);
}
public SantoriniNN()
{
OutputLayer = new Layer(_OutputNeurons);
Layer lastLayer = null;
for (int i = 0; i < _NumberOfHiddenLayers; i++)
{
if (lastLayer == null)
{
lastLayer = new Layer(_NumberOfHiddenNeurons, OutputLayer);
HiddenLayers.Add(lastLayer);
}
else
{
Layer l = new Layer(_NumberOfHiddenNeurons, lastLayer);
lastLayer = l;
HiddenLayers.Add(lastLayer);
}
}
InputLayer = new Layer(175, lastLayer);
}
private void InitHiddenLayers(int inputsAmount, int[] neuronsInHiddenLayersAmount)
{
for (var neuronsCount = 0; neuronsCount < neuronsInHiddenLayersAmount.Length; neuronsCount++)
{
var hiddenLayer = new Matrix(1, neuronsInHiddenLayersAmount[neuronsCount]);
HiddenLayers.Add(hiddenLayer);
Biases.Add(Random.Range(-1f, 1f));
if (neuronsCount == 0)
{
var weightsInputToHiddenLayer1 = new Matrix(inputsAmount, neuronsInHiddenLayersAmount[neuronsCount]);
Weights.Add(weightsInputToHiddenLayer1);
}
else
{
var weightsHliToNextHl = new Matrix(neuronsInHiddenLayersAmount[neuronsCount - 1],
neuronsInHiddenLayersAmount[neuronsCount]);
Weights.Add(weightsHliToNextHl);
}
}
}
public Network(int inputSize, int[] hiddenSizes, int outputSize, double learnRate = 0.4, double momentum = 0.9)
{
this.LearnRate = learnRate;
this.Momentum = momentum;
this.InputLayer = new List <Neuron>();
this.HiddenLayers = new List <List <Neuron> >();
this.OutputLayer = new List <Neuron>();
for (var i = 0; i < inputSize; i++)
{
InputLayer.Add(new Neuron());
}
var firstHiddenLayer = new List <Neuron>();
for (var i = 0; i < hiddenSizes[0]; i++)
{
firstHiddenLayer.Add(new Neuron(InputLayer));
}
HiddenLayers.Add(firstHiddenLayer);
for (var i = 1; i < hiddenSizes.Length; i++)
{
var hiddenLayer = new List <Neuron>();
for (var j = 0; j < hiddenSizes[i]; j++)
{
hiddenLayer.Add(new Neuron(HiddenLayers[i - 1]));
}
HiddenLayers.Add(hiddenLayer);
}
for (var i = 0; i < outputSize; i++)
{
OutputLayer.Add(new Neuron(HiddenLayers.Last()));
}
}