class_docstr: Shift based AdaMax. http://arxiv.org/pdf/1602.02830v3.pdf
class_docstr_tok: ["Shift","based","AdaMax",".","http",":","\/\/arxiv.org\/pdf\/1602.02830v3.pdf"]
docstr: Args:
      stochastic_round (bool): Set this to True for stochastic rounding.
                               If False rounding will be to nearest.
                               If True will perform stochastic rounding using default width.
                               Only affects the gpu backend.
      learning_rate (float): the multiplicative coefficient of updates
      beta_1 (float): Adam parameter beta1
      beta_2 (float): Adam parameter beta2
      epsilon (float): numerical stability parameter
      schedule (neon.optimizers.optimizer.Schedule, optional): Learning rate schedule.
                                                               Defaults to a constant.
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code: def __init__(
      self,
      stochastic_round=False,
      learning_rate=0.002,
      beta_1=0.9,
      beta_2=0.999,
      epsilon=1e-8,
      schedule=Schedule(),
      name="ShiftAdaMax",
  ):
      """
      Args:
          stochastic_round (bool): Set this to True for stochastic rounding.
                                   If False rounding will be to nearest.
                                   If True will perform stochastic rounding using default width.
                                   Only affects the gpu backend.
          learning_rate (float): the multiplicative coefficient of updates
          beta_1 (float): Adam parameter beta1
          beta_2 (float): Adam parameter beta2
          epsilon (float): numerical stability parameter
          schedule (neon.optimizers.optimizer.Schedule, optional): Learning rate schedule.
                                                                   Defaults to a constant.
      """
      super(ShiftAdaMax, self).__init__(name=name)
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.epsilon = epsilon
      self.learning_rate = learning_rate
      self.stochastic_round = stochastic_round
      self.schedule = schedule
  
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 ShiftAdaMax object from inherited base classes: Optimizer with example: obj = ShiftAdaMax(stochastic_round, learning_rate, beta_1, beta_2, epsilon, schedule, name)