Softmin
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:
nn_softmin(dim)
dim |
(int): A dimension along which Softmin will be computed (so every slice along dim will sum to 1). |
\mbox{Softmin}(x_{i}) = \frac{\exp(-x_i)}{∑_j \exp(-x_j)}
a Tensor of the same dimension and shape as the input, with
values in the range [0, 1].
Input: (*) where * means, any number of additional
dimensions
Output: (*), same shape as the input
if (torch_is_installed()) {
m <- nn_softmin(dim = 1)
input <- torch_randn(2, 2)
output <- m(input)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.