Softshrink

class torch.nn.modules.activation.Softshrink(lambd=0.5)

Applies the soft shrinkage function element-wise.

$$\text{SoftShrinkage}(x)=\{\begin{array}{ll}x-\lambda ,& \text{ if }x>\lambda \\ x+\lambda ,& \text{ if }x<-\lambda \\ 0,& \text{ otherwise }\end{array}$$

Parameters

lambd (float) – the $\lambda$ (must be no less than zero) value for the Softshrink formulation. Default: 0.5

Shape:

• Input: $(\ast )$, where $\ast$ means any number of dimensions.

• Output: $(\ast )$, same shape as the input.

Examples:

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

extra_repr()

Return the extra representation of the module.

Return type

str

forward(input)

Run forward pass.

Return type

Tensor