class_docstr: Adagrad optimization algorithm.
  
  Adagrad is an algorithm that adapts the learning rate individually for each parameter
  by dividing by the :math:`L_2`-norm of all previous gradients. Given the parameters
  :math:`\theta`, gradient :math:`\nabla J`, accumulating norm :math:`G`, and smoothing
  factor :math:`\epsilon`, we use the update equations:
  
  .. math::
  
      G' = G + (\nabla J)^2
  
  .. math::
  
      \theta' = \theta - \frac{\alpha}{\sqrt{G' + \epsilon}} \nabla J
  
  where the smoothing factor :math:`\epsilon` prevents from dividing by zero.
  By adjusting the learning rate individually for each parameter, Adagrad adapts
  to the geometry of the error surface. Differently scaled weights have appropriately scaled
  update steps.
  
  Example usage:
  
  .. code-block:: python
  
      from neon.optimizers import Adagrad
  
      # use Adagrad with a learning rate of 0.01
      optimizer = Adagrad(learning_rate=0.01, epsilon=1e-6)
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docstr: Class constructor.
  
  Arguments:
      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 multiplication coefficent of updates
      epsilon (float): smoothing epsilon to avoid divide by zeros
      gradient_clip_norm (float, optional): Target gradient norm.
                                            Defaults to None.
      gradient_clip_value (float, optional): Value to element-wise clip
                                             gradients.
                                             Defaults to None.
      param_clip_value (float, optional): Value to element-wise clip
                                          parameters.
                                          Defaults to None.
  Notes:
      Only constant learning rate is supported currently.
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code: def __init__(
      self,
      stochastic_round=False,
      learning_rate=0.01,
      epsilon=1e-6,
      gradient_clip_norm=None,
      gradient_clip_value=None,
      param_clip_value=None,
      name=None,
  ):
      """
      Class constructor.
  
      Arguments:
          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 multiplication coefficent of updates
          epsilon (float): smoothing epsilon to avoid divide by zeros
          gradient_clip_norm (float, optional): Target gradient norm.
                                                Defaults to None.
          gradient_clip_value (float, optional): Value to element-wise clip
                                                 gradients.
                                                 Defaults to None.
          param_clip_value (float, optional): Value to element-wise clip
                                              parameters.
                                              Defaults to None.
      Notes:
          Only constant learning rate is supported currently.
      """
      super(Adagrad, self).__init__(name=name)
      self.state_list = None
      self.epsilon = epsilon
      self.learning_rate = learning_rate
      self.gradient_clip_norm = gradient_clip_norm
      self.gradient_clip_value = gradient_clip_value
      self.param_clip_value = param_clip_value
      self.stochastic_round = stochastic_round
  
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 Adagrad object from inherited base classes: Optimizer with example: obj = Adagrad(stochastic_round, learning_rate, epsilon, gradient_clip_norm, gradient_clip_value, param_clip_value, name)