public void DBiases(double[] dvalues, Layer l, double divisor = 1)
{
for (int j = 0; j < DeltaB.Count(); j++)
{
DeltaB[j] += (dvalues[j] * 1 / divisor);
}
if (l.L1Regularization > 0)
{
Parallel.For(0, l.Biases.GetLength(0), i =>
{
DeltaB[i] += l.Biases[i] >= 0 ? l.L1Regularization : (-1 * l.L1Regularization);
DeltaB[i] += 2 * l.L2Regularizattion * l.Biases[i];
});
}
}
public void Update(Layer layer, double adjustment)
{
Parallel.For(0, DeltaB.Count(), i =>
{
layer.BMomentum[i] = double.IsNaN(DeltaB[i]) ?
layer.BMomentum[i] : layer.BMomentum[i] - (adjustment * DeltaB[i]);
//DeltaB[i] >= 1 ?
//layer.BMomentum[i] - adjustment : DeltaB[i] <= -1 ?
//layer.BMomentum[i] + adjustment : layer.BMomentum [i] - (adjustment * DeltaB[i]);
});
Parallel.For(0, layer.Weights.GetLength(0), i =>
{
Parallel.For(0, layer.Weights.GetLength(1), j =>
{
layer.WMomentum[i, j] = double.IsNaN(DeltaW[i, j]) ?
layer.WMomentum[i, j] : layer.WMomentum[i, j] - (adjustment * DeltaW[i, j]);
//DeltaW[i, j] >= 1 ?
//layer.WMomentum[i, j] - adjustment : DeltaW[i, j] <= -1?
//layer.WMomentum[i, j] + adjustment : layer.WMomentum[i, j] - (adjustment * DeltaW[i, j]);
});
});
layer.Update();
Reset();
}
public void Reset()
{
DeltaB = new double[DeltaB.Count()];
DeltaW = new double[DeltaW.GetLength(0), DeltaW.GetLength(1)];
}
