Softmin

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Softmin#

class brainstate.nn.Softmin(dim=None)#

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

Rescales the input so that the elements of the n-dimensional output Tensor lie in the range [0, 1] and sum to 1.

Softmin is defined as:

\[\text{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}\]
Parameters:

  • dim (int | None) – A dimension along which Softmin will be computed (so every slice along dim will sum to 1).

  • Shape

  • -----

  • Input (-)

  • Output (-)

Returns:

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

Return type:

Tensor

Examples

>>> import brainstate.nn as nn
>>> import brainstate
>>> m = nn.Softmin(dim=1)
>>> x = brainstate.random.randn(2, 3)
>>> output = m(x)
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