class_docstr: Exponential learning rate schedule. This schedule implements
  
  .. math::
      \alpha(t) = \frac{\alpha_\circ}{1 + \beta t}
  
  where :math:`\beta` is the decay rate, and :math:`\alpha_\circ` is the
  initial learning rate.
class_docstr_tok: ["Exponential","learning","rate","schedule",".","This","schedule","implements","..","math",":",":","\\alpha","(","t",")","=","\\frac","{","\\alpha_\\circ","}","{","1","+","\\beta","t","}","where",":","math",":","`","\\beta","`","is","the","decay","rate",",","and",":","math",":","`","\\alpha_\\circ","`","is","the","initial","learning","rate","."]
docstr: Class constructor.
  
  Arguments:
      decay (float): Decay rate.
docstr_tok: ["Class","constructor",".","Arguments",":","decay","(","float",")",":","Decay","rate","."]
code: def __init__(self, decay):
      """
      Class constructor.
  
      Arguments:
          decay (float): Decay rate.
      """
      self.decay = decay
  
imports: from __future__ import division
  from neon import NervanaObject
  from neon.util.persist import load_class
  import logging
  import numpy
label_desc: Use image node_id 1 to create a new ExpSchedule object from inherited base classes: Schedule with example: obj = ExpSchedule(decay)