class_docstr: Binary accuracy metric. Uses torch.eq to compare predictions to targets. Decorated with a mean and running_mean.
  Default for key: 'binary_acc'.
  
  Args:
      pred_key (StateKey): The key in state which holds the predicted values
      target_key (StateKey): The key in state which holds the target values
      threshold (float): value between 0 and 1 to use as a threshold when binarizing predictions and targets
class_docstr_tok: ["Binary","accuracy","metric",".","Uses","torch.eq","to","compare","predictions","to","targets",".","Decorated","with","a","mean","and","running_mean",".","Default","for","key",":","'binary_acc","'",".","Args",":","pred_key","(","StateKey",")",":","The","key","in","state","which","holds","the","predicted","values","target_key","(","StateKey",")",":","The","key","in","state","which","holds","the","target","values","threshold","(","float",")",":","value","between","0","and","1","to","use","as","a","threshold","when","binarizing","predictions","and","targets"]
code: def __init__(
      self,
      pred_key=torchbearer.Y_PRED,
      target_key=torchbearer.Y_TRUE,
      threshold=0.5,
  ):
      super(BinaryAccuracy, self).__init__("binary_acc")
      self.pred_key = pred_key
      self.target_key = target_key
  
      self.threshold = threshold
  
imports: import torchbearer
  from torchbearer import Metric
  from .decorators import default_for_key, running_mean, mean, std, to_dict
  import torch
label_desc: Use image node_id 1 to create a new BinaryAccuracy object from inherited base classes: Metric with example: obj = BinaryAccuracy(pred_key, target_key, threshold)