/// <summary>
/// http://aelag.com/translation-of-theano-softmax-function
/// </summary>
/// <param name="a"></param>
/// <param name="axis">The axis to compute the Softmax along, like in the Max function. Default value mimics Theano behavior</param>
/// <param name="result"></param>
/// <returns></returns>
public static Array <Real> Softmax(Array <Real> a, int axis = -1, Array <Real> result = null, Array <Real> buffer = null)
{
var maxes = NN.Max(a, axis: axis, keepDims: true, result: buffer);
var shifted = a.Sub(maxes, result: result);
result = NN.Exp(shifted, result: result);
var sum = NN.Sum(result, axis: axis, keepDims: true, result: maxes);
//result = result.Apply(sum, (x, s) => Math.Max(x / s, 0.000001f), result: result);
result = result.Div(sum, result: result);
return(result);
}
// "fast" softmax for 1D and 2D arrays
public static Array <Real> Softmax_(Array <Real> a, Array <Real> result = null)
{
if (a.Shape.Length > 2)
{
throw new RankException(string.Format("Must be 1-d or 2-d tensor, got {0}-d with shape ({1}).", a.Shape.Length, string.Join(", ", a.Shape)));
}
if (result == null)
{
result = Zeros <Real>(a.Shape);
}
else
{
result.AssertOfShape(a);
}
if (a.Shape.Length == 1)
{
var max = a.Max();
result = NN.Exp(a - max, result: result);
}
else
{
var maxes = NN.Zeros <Real>(a.Shape[0], 1);
var vMax = maxes.Values;
int off = a.Offset, offX;
int incX = a.Stride[0], incY = a.Stride[1];
int nX = a.Shape[0], nY = a.Shape[1];
Real max = Real.NegativeInfinity;
var v = a.Values;
for (int i = 0; i < nX; ++i)
{
offX = off;
max = Real.NegativeInfinity;
for (int j = 0; j < nY; ++j)
{
max = Math.Max(v[off], max);
off += incY;
}
off = offX + incX;
vMax[i] = max;
}
result = NN.Exp(a - maxes, result: result);
}
var sum = NN.Sum(result, axis: a.Shape.Length - 1, keepDims: true);
result = result.Div(sum, result: result);
//result = result.Apply(sum, (x, s) => Math.Max(x / s, 0.000001f), result: result);
return(result);
}