class_docstr: Measures top1 accuracy for multi-class classification
  or accuracy for binary classification.
  
  # Arguments
  zero_based_label: Boolean. Whether target labels start from 0. Default is True.
                    If False, labels start from 1.
                    Note that this only takes effect for multi-class classification.
                    For binary classification, labels ought to be 0 or 1.
  
  >>> acc = Accuracy()
  creating: createZooKerasAccuracy
class_docstr_tok: ["Measures","top1","accuracy","for","multi-class","classification","or","accuracy","for","binary","classification",".","#","Arguments","zero_based_label",":","Boolean",".","Whether","target","labels","start","from","0",".","Default","is","True",".","If","False",",","labels","start","from","1",".","Note","that","this","only","takes","effect","for","multi-class","classification",".","For","binary","classification",",","labels","ought","to","be","0","or","1",".",">",">",">","acc","=","Accuracy","(",")","creating",":","createZooKerasAccuracy"]
code: def __init__(self, zero_based_label=True, bigdl_type="float"):
      super(Accuracy, self).__init__(None, bigdl_type, zero_based_label)
  
imports: from bigdl.util.common import *
  from zoo.pipeline.api.keras.base import ZooKerasCreator
label_desc: Use image node_id 1 to create a new Accuracy object from inherited base classes: ZooKerasCreator, JavaValue with example: obj = Accuracy(zero_based_label, bigdl_type)