class_docstr: Multiplies the learning rate by a factor at regular epoch intervals.
  
  This schedule will multiply the learning rate by
  the factor ``change`` every ``step_config`` epochs. For example,
  
  .. code-block:: python
  
      schedule = Schedule(step_config=2, change=0.5)
      optimizer = GradientDescentMomentum(0.1, 0.9, schedule=schedule)
  
  will yield a learning rate schedule of:
  
  .. csv-table::
      :header: "Epoch", "LR"
      :widths: 20, 10
  
      0, 0.1
      1, 0.1
      2, 0.05
      3, 0.05
      4, 0.025
      5, 0.025
      6, 0.0125
      7, 0.0125
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docstr: Class constructor.
  
  Arguments:
      step_config (int): Learning rate update interval (in epochs)
      change (int): Update factor
docstr_tok: ["Class","constructor",".","Arguments",":","step_config","(","int",")",":","Learning","rate","update","interval","(","in","epochs",")","change","(","int",")",":","Update","factor"]
code: def __init__(self, step_config, change):
      """
      Class constructor.
  
      Arguments:
          step_config (int): Learning rate update interval (in epochs)
          change (int): Update factor
      """
      assert isinstance(
          step_config, int
      ), "The argument step_config must be an integer."
  
      assert not isinstance(
          change, list
      ), "The argument change must be a float or integer."
  
      self.step_config = step_config
      self.change = change
      self.steps = 0
  
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 PowerSchedule object from inherited base classes: Schedule with example: obj = PowerSchedule(step_config, change)