SELU
class torch.nn.SELU(inplace=False)
Applied element-wise, as:
  \text{SELU}(x) = \text{scale} * (\max(0,x) + \min(0, \alpha *
  (\exp(x) - 1)))

with \alpha = 1.6732632423543772848170429916717 and \text{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.

[image]
Examples:
  >>> m = nn.SELU()
  >>> input = torch.randn(2)
  >>> output = m(input)
