public Neuron(BinaryReader aReader, SerializationContext <Neuron> aContext)
{
byte marker = aReader.ReadByte();
if (marker != 0x3)
{
throw new System.Exception("Binarystream: expected neuron (0x3) got " + marker);
}
uint id = aReader.ReadUInt32();
type = (ENeuronType)aReader.ReadByte();
activationType = (EActivationType)aReader.ReadByte();
SigmoidBase = aReader.ReadSingle();
int count = aReader.ReadInt32();
if (count >= 0)
{
inputs = new NeuronLink[count];
}
for (int i = 0; i < count; i++)
{
int index = i;
aContext.GetObject(aReader.ReadUInt32(), (n) => inputs[index].neuron = n);
inputs[i].weight = aReader.ReadSingle();
}
aContext.RegisterObject(id, this);
}
public Layer AddLayer(int neuronCount, EActivationType method)
{
_lastLayer.IsMonitored = false;
_lastLayer = new Layer(_node.BuildChild(), neuronCount, _lastLayer);
_lastLayer.BasicConfiguration.ActivationType.Value = method;
_lastLayer.IsMonitored = true;
return(_lastLayer);
}
/// <summary>
/// Mutates the neuron values based on the passed mutation rate
/// </summary>
public void Mutate(int aMutationRate)
{
if (aMutationRate == -1 || Random.Range(0, aMutationRate) == 1)
{
activationType = (EActivationType)Random.Range(0, (int)EActivationType.COUNT);
}
for (int i = 0; i < inputs.Length; i++)
{
if (aMutationRate == -1 || Random.Range(0, aMutationRate) == 1)
{
inputs[i].weight = Random.Range(-1f, 1f);
}
}
if (aMutationRate == -1 || Random.Range(0, aMutationRate) == 1)
{
SigmoidBase = Random.Range(0f, 10.0f);
}
}
public Neuron(XmlNode aNode, SerializationContext <Neuron> aContext)
{
if (aNode.Name != "Neuron")
{
throw new System.Exception("Expected element 'Neuron' but got '" + aNode.Name + "'");
}
var id = uint.Parse(aNode.Attributes["id"].Value);
type = (ENeuronType)System.Enum.Parse(typeof(ENeuronType), aNode.Attributes["type"].Value);
activationType = (EActivationType)System.Enum.Parse(typeof(EActivationType), aNode.Attributes["activationType"].Value);
SigmoidBase = float.Parse(aNode.Attributes["SigmoidBase"].Value);
inputs = new NeuronLink[aNode.ChildNodes.Count];
for (int i = 0; i < inputs.Length; i++)
{
int index = i;
var link = aNode.ChildNodes.Item(i);
inputs[i].weight = float.Parse(link.Attributes["weight"].Value);
var neuronId = uint.Parse(link.Attributes["neuron"].Value);
aContext.GetObject(neuronId, (n) => inputs[index].neuron = n);
}
aContext.RegisterObject(id, this);
}
public double Activate(double activity)
{
EActivationType at = ActivationType.Value;
switch (at)
{
case EActivationType.Symmetric:
return(Math.Tanh(activity));
case EActivationType.Asymmetric:
return(1.0 / (1.0 + Math.Exp(-activity / ActivationTemperature.Value)));
case EActivationType.Threshold:
return(activity >= ActivationThreshold.Value ? HighOutput.Value : LowOutput.Value);
case EActivationType.Userdefined:
return(ActivationFunction.Value(activity));
default: //case EActivationType.Linear:
return(activity);
}
}
public double ActivateDerivative(double activity, double output)
{
double fe = FlatspotEliminationEnable.Value ? FlatspotElimination.Value : 0;
EActivationType at = ActivationType.Value;
switch (at)
{
case EActivationType.Symmetric:
return(fe + 1 - output * output);
case EActivationType.Asymmetric:
return(fe + output * (1 - output));
case EActivationType.Threshold:
return(activity == ActivationThreshold.Value ? double.PositiveInfinity : fe); //TODO: hardly useful ...
case EActivationType.Userdefined:
return(fe + ActivationFunctionDerivative.Value(activity, output));
default: //case EActivationType.Linear:
return(1);
}
}
