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:
Input: where * means, any number of additional dimensions
Output: , same shape as the input
dim (int) – A dimension along which Softmin will be computed (so every slice along dim will sum to 1).
a Tensor of the same dimension and shape as the input, with values in the range [0, 1]
>>> m = nn.Softmin() >>> input = torch.randn(2, 3) >>> output = m(input)