public IActivationFunction GetActivationFunction()
{
IActivationFunction activation;
switch (ActivationFunction)
{
case ActivationFunctionType.Linear:
activation = new ActivationLinear();
break;
case ActivationFunctionType.Sigmoid:
activation = new ActivationSigmoid();
break;
case ActivationFunctionType.TanH:
activation = new ActivationTANH();
break;
case ActivationFunctionType.SoftMax:
activation = new ActivationSoftMax();
break;
case ActivationFunctionType.ReLU:
activation = new ActivationReLU();
break;
default:
throw new ArgumentOutOfRangeException();
}
return(activation);
}
void AddLayers(List <LayerConfig> gen)
{
foreach (var g in gen)
{
IActivationFunction act;
if (g.ActivationType == 0)
{
act = new ActivationBiPolar();
}
switch (g.ActivationType)
{
case 0:
act = new ActivationBiPolar();
break;
case 1:
act = new ActivationBipolarSteepenedSigmoid();
break;
case 2:
act = new ActivationClippedLinear();
break;
case 3:
act = new ActivationCompetitive();
break;
case 4:
act = new ActivationElliott();
break;
case 5:
act = new ActivationElliottSymmetric();
break;
case 6:
act = new ActivationGaussian();
break;
case 7:
act = new ActivationLinear();
break;
case 8:
act = new ActivationLOG();
break;
case 9:
act = new ActivationRamp();
break;
case 10:
act = new ActivationRamp();
break;
case 11:
act = new ActivationSigmoid();
break;
case 12:
act = new ActivationSIN();
break;
case 13:
act = new ActivationSoftMax();
break;
case 14:
act = new ActivationSteepenedSigmoid();
break;
case 15:
act = new ActivationStep();
break;
case 16:
act = new ActivationTANH();
break;
default:
act = new ActivationSoftMax();
break;
}
network.AddLayer(new BasicLayer(act, g.hasBias, g.neurons));
}
}
