class_docstr: Polynomial learning rate schedule.
  
  This schedule takes as input the total number of epochs :math:`T` and a power :math:`\beta`,
  and produces the learning schedule:
  
  .. math::
      \alpha(t) = \alpha_\circ \times\left(1-\frac{t}{T}\right)^\beta
  
  where :math:`\alpha_\circ` is the initial learning rate.
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docstr: Class constructor.
  
  Arguments:
      total_epochs (int): Total number of epochs over which to calculate interpolated decay
      power (float): Total decay parameter
docstr_tok: ["Class","constructor",".","Arguments",":","total_epochs","(","int",")",":","Total","number","of","epochs","over","which","to","calculate","interpolated","decay","power","(","float",")",":","Total","decay","parameter"]
code: def __init__(self, total_epochs, power):
      """
      Class constructor.
  
      Arguments:
          total_epochs (int): Total number of epochs over which to calculate interpolated decay
          power (float): Total decay parameter
      """
      self.total_epochs = np.float32(total_epochs)
      self.power = power
  
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 PolySchedule object from inherited base classes: Schedule with example: obj = PolySchedule(total_epochs, power)