class_docstr: The optimizer class handles the gradient update stage of training a neural network.
  Given the current parameters :math:`w`, update parameters
  :math:`\Delta w`, and current state :math:`s`, the optimizer specifies an
  algorithm for performing the update.
  
  This base class contains to helper functions for scaling the gradients.
  specifices the abstract method optimize, which subclasses should implement. The optimize
  method is called at every minibatch to update the layer parameters.
class_docstr_tok: ["The","optimizer","class","handles","the","gradient","update","stage","of","training","a","neural","network",".","Given","the","current","parameters",":","math",":","`","w","`",",","update","parameters",":","math",":","`","\\Delta","w","`",",","and","current","state",":","math",":","`","s","`",",","the","optimizer","specifies","an","algorithm","for","performing","the","update",".","This","base","class","contains","to","helper","functions","for","scaling","the","gradients",".","specifices","the","abstract","method","optimize",",","which","subclasses","should","implement",".","The","optimize","method","is","called","at","every","minibatch","to","update","the","layer","parameters","."]
docstr: Class constructor.
docstr_tok: ["Class","constructor","."]
code: def __init__(self, name=None):
      """
      Class constructor.
      """
      super(Optimizer, self).__init__(name=name)
  
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 Optimizer object from inherited base classes: NervanaObject with example: obj = Optimizer(name)