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ELU#

classtorch.nn.ELU(alpha=1.0,inplace=False)[source]#

Applies the Exponential Linear Unit (ELU) function, element-wise.

Method described in the paper:Fast and Accurate Deep Network Learning by Exponential LinearUnits (ELUs).

ELU is defined as:

ELU(x)={x, if x>0α∗(exp⁡(x)−1), if x≤0\text{ELU}(x) = \begin{cases}x, & \text{ if } x > 0\\\alpha * (\exp(x) - 1), & \text{ if } x \leq 0\end{cases}
Parameters:

  • alpha (float) – theα\alpha value for the ELU formulation. Default: 1.0

  • inplace (bool) – can optionally do the operation in-place. Default:False

Shape:

  • Input:(∗)(*), where∗* means any number of dimensions.

  • Output:(∗)(*), same shape as the input.

../_images/ELU.png

Examples:

>>>m=nn.ELU()>>>input=torch.randn(2)>>>output=m(input)
extra_repr()[source]#

Return the extra representation of the module.

Return type:

str

forward(input)[source]#

Runs the forward pass.

Return type:

Tensor