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: Class constructor.
  
  Arguments:
      alpha (float): weight of exponential factor for negative values (default: 1.0).
      name (string, optional): Name (default: None)
docstr_tok: ["Class","constructor",".","Arguments",":","alpha","(","float",")",":","weight","of","exponential","factor","for","negative","values","(","default",":","1.0",")",".","name","(","string",",","optional",")",":","Name","(","default",":","None",")"]
code: def __init__(self, alpha=1.0, name=None):
      """
      Class constructor.
  
      Arguments:
          alpha (float): weight of exponential factor for negative values (default: 1.0).
          name (string, optional): Name (default: None)
      """
      super(Explin, self).__init__(name)
      self.alpha = alpha
  
imports: from __future__ import division
  from neon.transforms.transform import Transform
label_desc: Use image node_id 1 to create a new Explin object from inherited base classes: Transform with example: obj = Explin(alpha, name)