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: Returns the current learning rate given the epoch and initial learning rate.
  
  Arguments:
      learning_rate (float): Initial learning rate
      epoch (int): Current epoch, used to calculate the adjusted learning rate.
  
  Returns:
      (float): The adjusted learning rate.
docstr_tok: ["Returns","the","current","learning","rate","given","the","epoch","and","initial","learning","rate",".","Arguments",":","learning_rate","(","float",")",":","Initial","learning","rate","epoch","(","int",")",":","Current","epoch",",","used","to","calculate","the","adjusted","learning","rate",".","Returns",":","(","float",")",":","The","adjusted","learning","rate","."]
code: def get_learning_rate(self, learning_rate, epoch):
      """
      Returns the current learning rate given the epoch and initial learning rate.
  
      Arguments:
          learning_rate (float): Initial learning rate
          epoch (int): Current epoch, used to calculate the adjusted learning rate.
  
      Returns:
          (float): The adjusted learning rate.
      """
      return float(learning_rate / (1.0 + self.decay * epoch))
  
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 2 for calling the ExpSchedule obj's underlying member method code with example usage: obj.get_learning_rate(learning_rate, epoch) and returns: float