original_docstring: Computes the accuracy over the k top predictions for the specified values of k
docstring: Computes the accuracy over the k top predictions for the specified values of k
docstring_tokens: Computes the accuracy over the k top predictions for the specified values of k
code: def accuracy(output, target, topk=(1,)):
      """Computes the accuracy over the k top predictions for the specified values of k"""
      with torch.no_grad():
          maxk = max(topk)
          batch_size = target.size(0)
  
          _, pred = output.topk(maxk, 1, True, True)
          pred = pred.t()
          correct = pred.eq(target.view(1, -1).expand_as(pred))
  
          res = []
          for k in topk:
              correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
              res.append(correct_k.mul_(100.0 / batch_size))
          return res
code_tokens: def accuracy ( output , target , topk = ( 1 , ) ) : with torch . no_grad ( ) : maxk = max ( topk ) batch_size = target . size ( 0 ) _ , pred = output . topk ( maxk , 1 , True , True ) pred = pred . t ( ) correct = pred . eq ( target . view ( 1 , - 1 ) . expand_as ( pred ) ) res = [ ] for k in topk : correct_k = correct [ : k ] . view ( - 1 ) . float ( ) . sum ( 0 , keepdim = True ) res . append ( correct_k . mul_ ( 100.0 / batch_size ) ) return res
short_docstring: Computes the accuracy over the k top predictions for the specified values of k
short_docstring_tokens: Computes the accuracy over the k top predictions for the specified values of k
comment: """Computes the accuracy over the k top predictions for the specified values of k"""