class_docstr: The average accuracy over a single training epoch.
  This plugin metric only works at training time.
  
  The accuracy will be logged after each training epoch by computing
  the number of correctly predicted patterns during the epoch divided by
  the overall number of patterns encountered in that epoch.
class_docstr_tok: ["The","average","accuracy","over","a","single","training","epoch",".","This","plugin","metric","only","works","at","training","time",".","The","accuracy","will","be","logged","after","each","training","epoch","by","computing","the","number","of","correctly","predicted","patterns","during","the","epoch","divided","by","the","overall","number","of","patterns","encountered","in","that","epoch","."]
docstr: Creates an instance of the EpochAccuracy metric.
docstr_tok: ["Creates","an","instance","of","the","EpochAccuracy","metric","."]
code: def __init__(self):
      """
      Creates an instance of the EpochAccuracy metric.
      """
  
      super(EpochAccuracy, self).__init__(
          reset_at="epoch", emit_at="epoch", mode="train"
      )
  
imports: from typing import List, Optional, Union, Dict
  import torch
  from torch import Tensor
  from avalanche.evaluation import Metric, GenericPluginMetric
  from avalanche.evaluation.metrics.mean import Mean
  from collections import defaultdict
label_desc: Use image node_id 1 to create a new EpochAccuracy object from inherited base classes: AccuracyPluginMetric with example: obj = EpochAccuracy()