examples/train_cifar10.py
def accuracy(output, target, topk=(1, )):
    """Computes the precision@k 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
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
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
Computes the precision@k for the specified values of k
Computes the precision