class_docstr: The layer that calculates classification accuracy, that is,
  the proportion of objects classified correctly in the set.
  
  :param input_layers: The input layers to be connected. 
      The integer in each tuple specifies the number of the output.
      If not set, the first output will be used.
  :type input_layers: list of object, tuple(object, int)
  :param reset: Specifies if the statistics should be reset with each run.
      Set to False to accumulate statistics for subsequent runs.
  :type reset: bool, default=True
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a blob with the network response
      The dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** equal to the number of objects 
        that were classified
      - **Height**, **Width**, **Depth** equal to 1
      - **Channels** equal to 1 for binary classification and to the number of classes 
        if there are more than 2
  
  (2) a blob with the correct class labels
      The dimensions should be the same as for the first input
  
  .. rubric:: Layer outputs:
  
  (1) a blob with only one element, which contains the proportion of
      correctly classified objects among all objects
docstr: Specifies if the statistics should be reset after each run.
          
docstr_tok: ["Specifies","if","the","statistics","should","be","reset","after","each","run","."]
code: def reset(self, reset):
      """Specifies if the statistics should be reset after each run."""
      self._internal.set_reset(bool(reset))
  
file_path: neoml/NeoML/Python/neoml/Dnn/Accuracy.py
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
label_desc: Use image node_id 3 for calling the Accuracy obj's underlying member method code with example usage: obj.reset(reset) without return types