Softmin — PyTorch 2.7 documentation (original) (raw)

class torch.nn.Softmin(dim=None)[source][source]¶

Applies the Softmin function to an n-dimensional input Tensor.

Rescales 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:

Softmin(xi)=exp⁡(−xi)∑jexp⁡(−xj)\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

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

Returns

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

Return type

None

Examples:

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