class_docstr: Scaled Exponential Linear Unit
  :math:`\varphi(x)=\lambda \left[(x>0) ? x : \alpha(e^x-1)\right]`
  
  The Scaled Exponential Linear Unit (SELU) was introduced in [1]_
  as an activation function that allows the construction of
  self-normalizing neural networks.
  
  Parameters
  ----------
  scale : float32
      The scale parameter :math:`\lambda` for scaling all output.
  
  scale_neg  : float32
      The scale parameter :math:`\alpha`
      for scaling output for nonpositive argument values.
  
  Methods
  -------
  __call__(x)
      Apply the SELU function to the activation `x`.
  
  Examples
  --------
  In contrast to other activation functions in this module, this is
  a class that needs to be instantiated to obtain a callable:
  
  >>> from lasagne.layers import InputLayer, DenseLayer
  >>> l_in = InputLayer((None, 100))
  >>> from lasagne.nonlinearities import SELU
  >>> selu = SELU(2, 3)
  >>> l1 = DenseLayer(l_in, num_units=200, nonlinearity=selu)
  
  See Also
  --------
  selu: Instance with :math:`\alpha\approx1.6733,\lambda\approx1.0507`
        as used in [1]_.
  
  References
  ----------
  .. [1] Günter Klambauer et al. (2017):
     Self-Normalizing Neural Networks,
     https://arxiv.org/abs/1706.02515
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code: def __init__(self, scale=1, scale_neg=1):
      self.scale = scale
      self.scale_neg = scale_neg
  
file_path: Lasagne/lasagne/nonlinearities.py
imports: import theano.tensor
label_desc: Use image node_id 1 to create a new SELU object from inherited base classes: object with example: obj = SELU(scale, scale_neg)