public void GetSoftMaxLogOutput()
{
Matrix <double> matrix = Matrix <double> .Build.DenseOfArray(
new double[, ] {
{ 1, 0 }, { 0, 2 }
}
);
Matrix <double> softMax = SoftMax.GetSoftMaxLogOutput(matrix);
double lognorm1 = Math.Log(Math.Exp(1) + Math.Exp(0));
double lognorm2 = Math.Log(Math.Exp(0) + Math.Exp(2));
Matrix <double> softmax_check = Matrix <double> .Build.DenseOfArray(
new double[, ] {
{ 1 - lognorm1, 0 - lognorm2 },
{ 0 - lognorm1, 2 - lognorm2 }
}
);
for (int i = 0; i < softMax.RowCount; i++)
{
for (int j = 0; j < softMax.ColumnCount; j++)
{
Assert.AreEqual(softmax_check[i, j], softMax[i, j], 0.00000001);
}
}
}
public static ushort EnCodeIConduction(IConduction conduction)
{
return(conduction switch
{
Sigmoind _ => 1,
ReLU _ => 2,
SoftReLU _ => 3,
Straight _ => 4,
SoftMax _ => 5,
_ => throw new ArgumentException(
nameof(conduction), $"this type of IConduction is not registered. {conduction}"),
});
private void calculateActivatedSumsForLayer <T>(ref IList <T> layer, ActivationAlgorithm activationAlgorithm)
{
//OutputNode
if (typeof(T) == typeof(OutputNode))
{
foreach (OutputNode oNode in (List <OutputNode>)layer)
{
foreach (Connector connector in oNode.InboundConnectors)
{
oNode.Val += connector.Weight * connector.FromNode.Val;
}
if (activationAlgorithm == ActivationAlgorithm.Sigmoid)
{
var sigmoid = new Sigmoid();
IActivationFormulaDelegate activationFormulaDelegate = sigmoid;
oNode.Val = activationFormulaDelegate.applyActivation(oNode.Val);
}
else if (activationAlgorithm == ActivationAlgorithm.HyperTan)
{
var hTan = new HyperTan();
IActivationFormulaDelegate activationFormulaDelegate = hTan;
oNode.Val = activationFormulaDelegate.applyActivation(oNode.Val);
}
}
if (activationAlgorithm == ActivationAlgorithm.SoftMax)
{
int index = 0;
double[] temp = new double[layer.Count];
foreach (OutputNode oNode in (List <OutputNode>)layer)
{
temp[index] = oNode.Val;
index++;
}
index = 0;
var softMax = new SoftMax();
IActivationFormulaDelegate activationFormulaDelegate = softMax;
temp = activationFormulaDelegate.applyActivation(temp);
foreach (OutputNode oNode in (List <OutputNode>)layer)
{
oNode.Val = temp[index];
index++;
}
}
}
else if (typeof(T) == typeof(HiddenNode))
{
//HiddenNode
foreach (HiddenNode hNode in (List <HiddenNode>)layer)
{
foreach (Connector connector in hNode.InboundConnectors)
{
hNode.Val += connector.Weight * connector.FromNode.Val;
}
if (activationAlgorithm == ActivationAlgorithm.Sigmoid)
{
var sigmoid = new Sigmoid();
IActivationFormulaDelegate activationFormulaDelegate = sigmoid;
hNode.Val = activationFormulaDelegate.applyActivation(hNode.Val);
}
else if (activationAlgorithm == ActivationAlgorithm.HyperTan)
{
var hTan = new HyperTan();
IActivationFormulaDelegate activationFormulaDelegate = hTan;
hNode.Val = activationFormulaDelegate.applyActivation(hNode.Val);
}
}
if (activationAlgorithm == ActivationAlgorithm.SoftMax)
{
int index = 0;
double[] temp = new double[layer.Count];
foreach (HiddenNode oNode in (List <HiddenNode>)layer)
{
temp[index] = oNode.Val;
index++;
}
index = 0;
var softMax = new SoftMax();
IActivationFormulaDelegate activationFormulaDelegate = softMax;
temp = activationFormulaDelegate.applyActivation(temp);
foreach (HiddenNode oNode in (List <HiddenNode>)layer)
{
oNode.Val = temp[index];
index++;
}
}
}
}
