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 derivative.
  
  Arguments:
      x (Tensor or optree): Input value
  
  Returns:
      Tensor or optree: Derivative
docstr_tok: ["Returns","the","derivative",".","Arguments",":","x","(","Tensor","or","optree",")",":","Input","value","Returns",":","Tensor","or","optree",":","Derivative"]
code: def bprop(self, x):
      """
      Returns the derivative.
  
      Arguments:
          x (Tensor or optree): Input value
  
      Returns:
          Tensor or optree: Derivative
      """
      return (
          self.be.greater(x, 0)
          + self.be.minimum(x, 0)
          + self.alpha * self.be.less(x, 0)
      )
  
imports: from __future__ import division
  from neon.transforms.transform import Transform
label_desc: Use image node_id 3 for calling the Explin obj's underlying member method code with example usage: obj.bprop(x) and returns: unknown