public double Gradient(double?target = null)
{
if (target == null)
{
return(gradient = Axons.Sum(a => a.targetNeuron.gradient * a.Weight) * Activate.sigmoidDerivative(value));
}
return(gradient = (target.Value - value) * Activate.sigmoidDerivative(value));
}
public double activate(double x, ActivatorFunc?_tF)
{
ActivatorFunc __tF = _tF ?? tF;
switch (__tF)
{
case ActivatorFunc.Sigmoid:
return(Activate.Sigmoid(x));
case ActivatorFunc.TanHyperbolic:
return(Activate.Tanh(x));
case ActivatorFunc.Identity:
return(Activate.Identity(x));
case ActivatorFunc.ReLU:
return(Activate.ReLU(x));
case ActivatorFunc.SoftStep:
return(Activate.SoftStep(x));
}
return(-1);
}
public double Value()
{
return(value = Activate.Sigmoid(Dendrites.Sum(a => a.Weight * a.sourceNeuron.value) + bias));
}
