Softplus
class torch.nn.Softplus(beta=1, threshold=20)
Applies the Softplus function \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 \times \beta > threshold.
Parameters:
      * beta (int) -- the \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.

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