Softplus# class torch.nn.modules.activation.Softplus(beta=1.0, threshold=20.0)[source]# Applies the Softplus function element-wise. Softplus(x)=1β∗log(1+exp(β∗x))\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) Softplus(x)=β1∗log(1+exp(β∗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×β>thresholdinput \times \beta > thresholdinput×β>threshold. Parameters beta (float) – the β\betaβ value for the Softplus formulation. Default: 1 threshold (float) – 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) extra_repr()[source]# Return the extra representation of the module. Return type str forward(input)[source]# Run forward pass. Return type Tensor
