class_docstr: Steps the learning rate over training time.
  
  To set a step schedule, pass as arguments ``step_config`` and ``change``. The schedule
  will set the learning rate at ``step[i]`` to ``change[i]``. For example, the call:
  
  .. code-block:: python
  
      schedule = Schedule(step_config=[2, 6], change=[0.6, 0.4])
  
  will set the learning rate to 0.6 at step 2, and to 0.4 at step 6.
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docstr: Class constructor.
  
  Arguments:
      step_config (list): Configure the step times (list of epoch indices)
      change (list): List of learning rates. Must be same length as step_config
docstr_tok: ["Class","constructor",".","Arguments",":","step_config","(","list",")",":","Configure","the","step","times","(","list","of","epoch","indices",")","change","(","list",")",":","List","of","learning","rates",".","Must","be","same","length","as","step_config"]
code: def __init__(self, step_config, change):
      """
      Class constructor.
  
      Arguments:
          step_config (list): Configure the step times (list of epoch indices)
          change (list): List of learning rates. Must be same length as step_config
      """
  
      assert isinstance(step_config, list) and isinstance(
          change, list
      ), "The arguments change and step_config must be lists."
  
      assert len(step_config) == len(
          change
      ), "The arguments change and step_config must have the same length."
  
      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 StepSchedule object from inherited base classes: Schedule with example: obj = StepSchedule(step_config, change)