public Network(int InputLayerSize, int[] HiddenLayerSizes, int OutputLayerSize, Function[] activationFuncs)
{
Random r = new Random();
//will create a network with InputLayerSize inputneurons, HiddenLayerSizes.count Hiddenlayers (Each with the size of the array at that position), and OutputLayerSize outputneurons
/*****************************
* InputNeurons
****************************/
InputLayer = new InputLayer(InputLayerSize, this);
/*****************************
* HiddenNeurons
****************************/
hiddenLayer = new HiddenLayer[HiddenLayerSizes.Length];
for (int i = 0; i < hiddenLayer.Length; i++) //for each layer
{
hiddenLayer[i] = new HiddenLayer(HiddenLayerSizes[i], this);
if (i == 0)
{
hiddenLayer[i].connectToInputLayer(InputLayer);//connect the hiddenlayer to the inputlayer
}
else
{
hiddenLayer[i].connectToInputLayer(hiddenLayer[i - 1]);//connect the hiddenlayer to the hiddenlayer before
}
hiddenLayer[i].ActivationFunction = activationFuncs[i];
}
/*****************************
* OutputNeurons
****************************/
outputLayer = new OutputLayer(OutputLayerSize, this);
outputLayer.connectToInputLayer(hiddenLayer[HiddenLayerSizes.Length - 1]);
outputLayer.ActivationFunction = activationFuncs[activationFuncs.Length - 1];
this.RandomizeAllWeights();
this.DatabaseID = db.getNetworkID();
}
public void Mutate()
{
if (RandomGenerator.NextDouble() < Constants.Con.mutate_weights_chanse)
{
//Debug.Log("mutate");
OutputLayer.Mutate();
foreach (var neuron in HiddenLayers)
{
neuron.Value.Mutate();
}
}
if (RandomGenerator.NextDouble() < Constants.Con.mutate_new_neuron_chanse)
{
//Debug.Log("mutate");
if (AllSynapses.Count == 0)
{
return;
}
AddNeuron();
}
if (RandomGenerator.NextDouble() < Constants.Con.mutate_del_neuron_chanse)
{
//Debug.Log("mutate");
if (AllSynapses.Count == 0)
{
return;
}
DelNeuron();
}
if (RandomGenerator.NextDouble() < Constants.Con.mutate_new_synapse_chanse)
{
//Debug.Log("mutate");
AddSynapse();
}
if (RandomGenerator.NextDouble() < Constants.Con.mutate_del_synapse_chanse)
{
//Debug.Log("mutate");
DelSynapse();
}
}
public void FeedForward()
{
HiddenLayer.CalculateNeuronValues();
OutputLayer.CalculateNeuronValues();
}
public OutputLayer CreateOutputLayer()
{
OutputLayer = new OutputLayer();
return(OutputLayer);
}
public NeuralNetwork()
{
InputLayer = null;
HiddenLayers = new List <HiddenLayer>();
OutputLayer = null;
}
