Softmax

class torch.nn.Softmax(dim: Optional[int] = None)

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:

$\text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}$

When the input Tensor is a sparse tensor then the unspecifed values are treated as -inf.

Shape:

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

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

Returns

a Tensor of the same dimension and shape as the input with values in the range [0, 1]

Parameters

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

Note

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).

Examples:

>>> m = nn.Softmax(dim=1)
>>> input = torch.randn(2, 3)
>>> output = m(input)