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SELU

class torch.nn.SELU(inplace=False)[source]

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)))

with α=1.6732632423543772848170429916717\alpha = 1.6732632423543772848170429916717 and scale=1.0507009873554804934193349852946\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: (N,)(N, *) where * means, any number of additional dimensions

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

../_images/SELU.png

Examples:

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

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