PyTorch代码：计算TN（真负例）

def eval_net(net, dataset, slicetotal, batch_size=12, gpu=True):
    'Evaluation without the densecrf with the dice coefficient'

    net.eval()
    start = time.time()
    dice_ = torch.zeros(14).cuda()
    jac_ = torch.zeros(14).cuda()
    NE = torch.zeros(14).cuda()
    JNE = torch.zeros(14).cuda()

    accuracy_ = torch.zeros(14).cuda()
    precision_ = torch.zeros(14).cuda()
    recall_ = torch.zeros(14).cuda()
    specificity_ = torch.zeros(14).cuda()

    print(1)
    with torch.no_grad():
        for i, b in enumerate(batch(dataset, batch_size)):

            imgs = np.array([k[0] for k in b]).astype(np.float32)
            true_masks = np.array([k[1] for k in b])

            imgs = torch.from_numpy(imgs)
            imgs = imgs.unsqueeze(1)
            true_masks = torch.from_numpy(true_masks)

            pre_masks_eval = torch.zeros(true_masks.shape[0],14,256,256)
            true_masks_eval = torch.zeros(true_masks.shape[0],14,256,256)
            batchshape = true_masks.shape[0]

            batch_dice = torch.zeros(14).cuda()
            if gpu:
                imgs = imgs.cuda()
                true_masks = true_masks.cuda()
                net.cuda()

            output_img = net(imgs)
            input = output_img.cuda()
            pre_masks = input.max(1)[1].float() #索引代表像素所属类别的数字
            for ak in range(14):
                if ak == 0:
                    continue
                pre_masks_eval[:,ak] = (pre_masks==ak)
                true_masks_eval[:,ak] = (true_masks==ak)
                premasks = pre_masks_eval[:,ak].view(true_masks.shape[0],-1)
                truemasks = true_masks_eval[:,ak].view(true_masks.shape[0],-1)

                intersection = premasks * truemasks
                TP = intersection.sum(1)
                FP = premasks.sum(1) - TP
                FN = truemasks.sum(1) - TP

                TN = (1 - premasks - truemasks).sum(1)

                for bk in range(true_masks.shape[0]):
                    if TP[bk] == 0 and FP[bk] == 0 and FN[bk] == 0:
                        NE[ak] += 1
                        JNE[ak] += 1
                    else:
                        batch_dice[ak] = batch_dice[ak] + 2*TP[bk] / (2*TP[bk] + FP[bk] + FN[bk])
                        jac_[ak] = jac_[ak] + TP[bk] / (TP[bk] + FP[bk] + FN[bk])

            dice_ = dice_ + batch_dice

        for knum in range(14):
            dice_[knum] = dice_[knum] / (slicetotal - NE[knum])
            jac_[knum] = jac_[knum] / (slicetotal - JNE[knum])
    end = time.time()
    print('time used:',end - start)

    return dice_,jac_```

该代码在 `for ak in range(14):` 循环内计算了 TN 值。代码中 `TN = (1 - premasks - truemasks).sum(1)` 使用了布尔逻辑运算，其中 `1 - premasks` 表示预测为负的像素，`1 - truemasks` 表示实际为负的像素。所以 `1 - premasks - truemasks` 表示同时预测为负且实际为负的像素，即 TN。

**注意:**

* 这段代码假设你已经定义了 `batch` 函数，用于将数据集分成批次。
* 代码中的 `14` 代表类别数量，你可以根据实际情况修改。
* 如果你需要计算其他评价指标，例如准确率、精确率、召回率等，可以将相应的计算代码添加到 `for ak in range(14):` 循环内。

希望这段代码和解释能够帮助你理解如何计算 TN。