public override int GetHashCode()
{
int hash = 1;
if (Engine.Length != 0)
{
hash ^= Engine.GetHashCode();
}
if (Normalization.Length != 0)
{
hash ^= Normalization.GetHashCode();
}
if (NInput != 0)
{
hash ^= NInput.GetHashCode();
}
if (NOutput != 0)
{
hash ^= NOutput.GetHashCode();
}
if (LearningRate != 0D)
{
hash ^= LearningRate.GetHashCode();
}
if (BatchSize != 0)
{
hash ^= BatchSize.GetHashCode();
}
if (EpochSize != 0)
{
hash ^= EpochSize.GetHashCode();
}
if (Optimizer.Length != 0)
{
hash ^= Optimizer.GetHashCode();
}
if (LossFunc.Length != 0)
{
hash ^= LossFunc.GetHashCode();
}
hash ^= maxV_.GetHashCode();
hash ^= minV_.GetHashCode();
hash ^= meanV_.GetHashCode();
hash ^= stdV_.GetHashCode();
hash ^= layers_.GetHashCode();
return(hash);
}
internal static float GetTotalLoss(float[][] outputSignals, float[][] expectedSignals, LossFunc lossFunc)
{
float _totalLoss = 0;
switch (lossFunc)
{
case LossFunc.MSE:
for (int _sample = 0; _sample < outputSignals.Length; _sample++)
{
for (int _neuron = 0; _neuron < outputSignals[0].Length; _neuron++)
{
_totalLoss += (float)Math.Pow(expectedSignals[_sample][_neuron] - outputSignals[_sample][_neuron], 2);
}
}
_totalLoss = _totalLoss / outputSignals.Length;
break;
case LossFunc.RootMSE:
_totalLoss = (float)Math.Sqrt(GetTotalLoss(outputSignals, expectedSignals, LossFunc.MSE));
break;
case LossFunc.Arctan:
for (int _sample = 0; _sample < outputSignals.Length; _sample++)
{
for (int _neuron = 0; _neuron < outputSignals[0].Length; _neuron++)
{
_totalLoss += (float)Math.Pow(Math.Atan(expectedSignals[_sample][_neuron] - outputSignals[_sample][_neuron]), 2);
}
}
_totalLoss = _totalLoss / outputSignals.Length;
break;
}
return(_totalLoss);
}
public Builder SetLossFunc(LossFunc lossFunc)
{
LossFunc = lossFunc;
return(this);
}
