/// <inheritdoc />
public float Loss(Tensor y, Tensor expected, bool calculateGradient)
{
if (y == null)
{
throw new ArgumentNullException(nameof(y));
}
if (expected == null)
{
throw new ArgumentNullException(nameof(expected));
}
if (expected.Length != y.Length)
{
throw new ArgumentException(string.Format(
CultureInfo.CurrentCulture,
"The number of expected labels: {0} does not match the tensor length: {1}.",
expected.Length,
y.Length));
}
float[] yw = y.Weights;
float[] ew = expected.Weights;
if (calculateGradient)
{
Vectors.Copy(expected.Length, ew, 0, y.Gradient, 0);
}
if (y.Shape.Rank == 1)
{
return(Calculate(expected.Length, 0, 0));
}
else
{
int mb = y.Shape.Axes[0]; // number of items in a mini-batch
int mbsize = y.Shape.Strides[0]; // item size
float loss = 0.0f;
for (int i = 0, yi = 0, ei = 0; i < mb; i++, yi += mbsize, ei += mbsize)
{
loss += Calculate(mbsize, yi, ei);
}
return(loss / mb);
}
float Calculate(int length, int offy, int offe)
{
return(Vectors.EuclideanDistance(length, yw, offy, ew, offe) / length);
}
}
public double Distance(double[] x, double[] y)
{
return(Vectors.EuclideanDistance(x.Length, x, 0, y, 0));
}
public float Distance(float[] x, float[] y)
{
return(Vectors.EuclideanDistance(x.Length, x, 0, y, 0));
}
public float EuclideanDistance(float[] y, int offy) => Vectors.EuclideanDistance(this.length, this.x, this.offset, y, offy);
public float EuclideanDistance(float[] y, int offy) => Vectors.EuclideanDistance(this.x.Length, this.x, 0, y, offy);