class_docstr: Adadelta optimization algorithm.
  
  Similar to RMSprop, Adadelta tracks the running average of the
  gradients, :math:`\mu_J`, over a window size :math:`1/\lambda`, where
  :math:`\lambda` is the parameter ``decay``. Adadelta also tracks an average of the
  recent update steps, which we denote as :math:`\mu_\theta`, and sets the learning rate
  as the ratio of the two averages:
  
  .. math::
  
      \mu_J' &= \lambda\mu_J + (1-\lambda) (\nabla J)^2
  
  .. math::
  
      \Delta \theta &= \sqrt{\frac{\mu_\theta + \epsilon}{\mu_J' + \epsilon}} \nabla J
  
  .. math::
  
      \mu_\theta &= \lambda \mu_\theta + (1-\rho) (\Delta \theta)^2
  
  .. math::
  
      \theta &= \theta - \Delta \theta
  
  Note that the learning rate is a ratio of the average updates from the
  previous step, :math:`\mu_\theta`, divided by the average gradients including the current
  step, :math:`\mu'_J`.
  
  Example usage:
  
  .. code-block:: python
  
      from neon.optimizers import Adadelta
      # use Adagrad with a learning rate of 0.01
      optimizer = Adadelta(decay=0.95, epsilon=1e-6)
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docstr: Class constructor.
  
  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.
      decay: decay parameter in Adadelta
      epsilon: epsilon parameter in Adadelta
      param_clip_value (float, optional): Value to element-wise clip
                                          parameters.
                                          Defaults to None.
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code: def __init__(
      self,
      stochastic_round=False,
      decay=0.95,
      epsilon=1e-6,
      param_clip_value=None,
      name=None,
  ):
      """
      Class constructor.
  
      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.
          decay: decay parameter in Adadelta
          epsilon: epsilon parameter in Adadelta
          param_clip_value (float, optional): Value to element-wise clip
                                              parameters.
                                              Defaults to None.
      """
      super(Adadelta, self).__init__(name=name)
      self.decay = decay
      self.epsilon = epsilon
      self.stochastic_round = stochastic_round
      self.param_clip_value = param_clip_value
  
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 Adadelta object from inherited base classes: Optimizer with example: obj = Adadelta(stochastic_round, decay, epsilon, param_clip_value, name)