Softplus

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

Applies the element-wise function:

$$\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))$$

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 – the $\beta$ value for the Softplus formulation. Default: 1
• threshold – values above this revert to a linear function. Default: 20

Shape:

• Input: $(N, *)$ where * means, any number of additional dimensions
• Output: $(N, *)$ , same shape as the input

Examples:

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