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Softplus

class torch.nn.Softplus(beta=1, threshold=20)[source]

Applies the Softplus function Softplus(x)=1βlog(1+exp(βx))\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) element-wise.

SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.

For numerical stability the implementation reverts to the linear function when input×β>thresholdinput \times \beta > threshold.

Parameters
  • beta (int) – the β\beta value for the Softplus formulation. Default: 1

  • threshold (int) – values above this revert to a linear function. Default: 20

Shape:
  • Input: ()(*), where * means any number of dimensions.

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

../_images/Softplus.png

Examples:

>>> m = nn.Softplus()
>>> input = torch.randn(2)
>>> output = m(input)

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