public static LogisticFunction GetLogisticFunction(LogisticFunctions logistic)
{
switch (logistic)
{
case LogisticFunctions.Sigmoid:
return(new Sigmoid());
case LogisticFunctions.SoftPlus:
return(new SoftPlus());
case LogisticFunctions.HypTan:
return(new HyperbolicTan());
case LogisticFunctions.ArcTan:
return(new ArcTan());
case LogisticFunctions.IdentityFunction:
return(new IdentityFunction());
case LogisticFunctions.ReLF:
return(new RectifiedLinearUnit());
case LogisticFunctions.SoftMax:
return(new SoftMax());
default:
return(null);
}
}
public static bool IsCanonicalLink(LogisticFunctions logistic, LossFunctions loss)
{
switch (logistic)
{
case LogisticFunctions.Sigmoid:
return(loss == LossFunctions.MLE);
case LogisticFunctions.IdentityFunction:
return(loss == LossFunctions.MSE);
case LogisticFunctions.SoftMax:
return(loss == LossFunctions.CE);
default:
return(false);
}
}
/// <summary>
/// 3Layer Backpropagation Class
/// </summary>
/// <param name="inputWeight">Input Weight.</param>
/// <param name="outputWeight">Output Weight.</param>
/// <param name="hiddenLayer">Hidden layer.</param>
/// <param name="outputLayer">Output layer.</param>
/// <param name="hiddenLogisticFunc">Hidden Layer Logistic Function. Default = SigmoidFunc</param>
/// <param name="outputLogisticFunc">Output Layer Logistic Function. Default = SigmoidFunc</param>
/// <param name="lossFunc">Output Layer Loss Function. Default = MSE</param>
/// <param name="learnRate">Learn rate. Default = 0.01</param>
public BackPropagation(Matrix inputWeight, Matrix outputWeight, Matrix hiddenLayer, Matrix outputLayer,
LogisticFunctions hiddenLogisticFunc = LogisticFunctions.Sigmoid,
LogisticFunctions outputLogisticFunc = LogisticFunctions.Sigmoid,
LossFunctions lossFunc = LossFunctions.MSE, double learnRate = 0.01)
{
_inputWeight = inputWeight;
_outputWeight = outputWeight;
_hiddenLayer = hiddenLayer;
_outputLayer = outputLayer;
HiddenLogisticFunc = hiddenLogisticFunc;
OutputLogisticFunc = outputLogisticFunc;
LossFunc = lossFunc;
_hiddenLogisticFunc = GetLogisticFunction(hiddenLogisticFunc);
_outputLogisticFunc = GetLogisticFunction(outputLogisticFunc);
_lossFunc = GetLossFunction(lossFunc);
LearnRate = learnRate;
}