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# SELU

class torch.nn.SELU(inplace=False)

  Applied element-wise, as:

  SELU(x)=scale∗(max⁡(0,x)+min⁡(0,α∗(exp⁡(x)−1)))\text{SELU}(x) = \text{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1))) SELU(x)=scale∗(max(0,x)+min(0,α∗(exp(x)−1)))

  with α=1.6732632423543772848170429916717\alpha = 1.6732632423543772848170429916717 α=1.6732632423543772848170429916717 and scale=1.0507009873554804934193349852946\text{scale} = 1.0507009873554804934193349852946 scale=1.0507009873554804934193349852946.

  Warning

  When using `kaiming_normal` or `kaiming_normal_` for initialisation, `nonlinearity='linear'` should be used instead of `nonlinearity='selu'` in order to get Self-Normalizing Neural Networks. See `torch.nn.init.calculate_gain()` for more information.

  More details can be found in the paper Self-Normalizing Neural Networks .

  Parameters:

    inplace (bool, optional) – 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.SELU()
      >>> input = torch.randn(2)
      >>> output = m(input)

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