softmin

class brainpy.math.softmin(x, axis=-1)

Applies the Softmin function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0, 1] and sum to 1.

Softmin is defined as:

\[\text{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}\]

Shape:

• Input: \((*)\) where * means, any number of additional dimensions

• Output: \((*)\), same shape as the input

Parameters:

axis (int) – A dimension along which Softmin will be computed (so every slice along dim will sum to 1).