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# ELU

class torch.nn.ELU(alpha=1.0, inplace=False)

  Applies the Exponential Linear Unit (ELU) function, element-wise, as described in the paper: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

  ELU is defined as:

  ELU(x)={x, if x>0 α∗(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} ELU(x)={x,α∗(exp(x)−1),​ if x>0 if x≤0​
  Parameters:

    - alpha (float) – the α\alpha α 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.

  Examples:

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

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