机器学习中，评价指标Top

Love The Way You Lie 2022-12-25 05:00 252阅读 0赞

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**Top-1 错误率 = 1.0 - Top-1正确率**

**Top-5 错误率 = 1.0 - Top-5正确率 【Top-1，Top-5中的Top指的是一个图片中的概率前1和前5，不是所有图片中预测最好的1个或5个图片】**

**实现代码：**

def accuracy(output, target, topk=(1,)):
        maxk = max(topk)
        batch_size = target.size(0)
        # torch.topk(input, k, dim=None, largest=True, sorted=True, *, out=None) -> (Tensor, LongTensor)
        _, pred = output.topk(maxk, 1, True, True)  # Returns the k largest elements of the given input tensor along a given dimension.
        # _ 是dim=1维度下的max k个values；pred是dim=1维度下values的下标。参见：https://blog.csdn.net/u014264373/article/details/86525621
        pred = pred.t()
        # >> > b = torch.tensor([[2, 2], [3, 3], [5, 5]])
        # >> > print(b.size())
        # torch.Size([3, 2])
        # >> > a.expand_as(b)
        # tensor([[2, 2],
        #         [3, 3],
        #         [4, 4]])
        # >> > a
        # tensor([[2],
        #         [3],
        #         [4]])
        correct = pred.eq(target.view(1, -1).expand_as(pred))  # expand_as 把一个tensor变成和函数括号内一样形状的tensor，用法与expand（）类似
        # outputs = torch.FloatTensor([[1], [2], [3]])
        # targets = torch.FloatTensor([[0], [2], [3]])
        # print(targets.eq(outputs.data)) = [0,1,1]
    
        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0)
            res.append(correct_k.mul_(100.0/batch_size))   # 如果batch_size = 10,表示10张图片一起技计算。那么correct_k是10张的正确度相加。
        return res

def evaluteTop1(model, loader):
        model.eval()
        
        correct = 0
        total = len(loader.dataset)
    
        for x,y in loader:
            x,y = x.to(device), y.to(device)
            with torch.no_grad():
                logits = model(x)
                pred = logits.argmax(dim=1)
                correct += torch.eq(pred, y).sum().float().item()
            #correct += torch.eq(pred, y).sum().item()
        return correct / total
    
    def evaluteTop5(model, loader):
        model.eval()
        correct = 0
        total = len(loader.dataset)
        for x, y in loader:
            x,y = x.to(device),y.to(device)
            with torch.no_grad():
                logits = model(x)
                maxk = max((1,5))　　　　　　　 
                y_resize = y.view(-1,1)
                _, pred = logits.topk(maxk, 1, True, True)
                correct += torch.eq(pred, y_resize).sum().float().item()
        return correct / total

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