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# Softplus

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

  Applies the Softplus function Softplus(x)=1 β∗log⁡(1+exp⁡(β∗x))\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))Softplus(x)=β 1​∗log(1+exp(β∗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 i n p u t×β>t h r e s h o l d input \times \beta > threshold in p u t×β>t h res h o l d.

  Parameters:

    - beta (int) – the β\beta β 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.

  Examples:

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

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