Applies the Softmax 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.
Softmax is defined as:
nn_softmax(dim)
| dim | (int): A dimension along which Softmax 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]
$$ \mbox{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)} $$
When the input Tensor is a sparse tensor then the unspecifed
values are treated as -Inf.
This module doesn't work directly with NLLLoss,
which expects the Log to be computed between the Softmax and itself.
Use LogSoftmax instead (it's faster and has better numerical properties).
Input: \((*)\) where * means, any number of additional
dimensions
Output: \((*)\), same shape as the input
if (torch_is_installed()) { m <- nn_softmax(1) input <- torch_randn(2, 3) output <- m(input) }