class_docstr: Exponential Linear activation function, :math:`f(x) = \max(x, 0) + \alpha (e^{\min(x, 0)}-1)`
  
  From: Clevert, Unterthiner and Hochreiter, ICLR 2016.
docstr: Returns the Exponential Linear activation
  
  Arguments:
      x (Tensor or optree): input value
  
  Returns:
      Tensor or optree: output activation
docstr_tok: ["Returns","the","Exponential","Linear","activation","Arguments",":","x","(","Tensor","or","optree",")",":","input","value","Returns",":","Tensor","or","optree",":","output","activation"]
code: def __call__(self, x):
      """
      Returns the Exponential Linear activation
  
      Arguments:
          x (Tensor or optree): input value
  
      Returns:
          Tensor or optree: output activation
      """
      return self.be.maximum(x, 0) + self.alpha * (
          self.be.exp(self.be.minimum(x, 0)) - 1
      )
  
imports: from __future__ import division
  from neon.transforms.transform import Transform
label_desc: Use image node_id 2 for calling the Explin obj's underlying member method code with example usage: obj.__call__(x) and returns: unknown