{"id":75471,"date":"2026-02-12T12:17:42","date_gmt":"2026-02-12T04:17:42","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/75471.html"},"modified":"2026-02-12T12:17:42","modified_gmt":"2026-02-12T04:17:42","slug":"%e5%9f%ba%e4%ba%8e-pytorch-%e7%9a%84%e5%8d%8a%e7%9b%91%e7%9d%a3%e9%a3%9f%e5%93%81%e5%88%86%e7%b1%bb","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/75471.html","title":{"rendered":"\u57fa\u4e8e PyTorch \u7684\u534a\u76d1\u7763\u98df\u54c1\u5206\u7c7b"},"content":{"rendered":"

1. \u4ee3\u7801\u6574\u4f53\u529f\u80fd\u6982\u8ff0<\/h4>\n

\u8fd9\u6bb5\u4ee3\u7801\u5b9e\u73b0\u4e86\u4e00\u4e2a\u534a\u76d1\u7763\u5b66\u4e60\u7684\u98df\u54c1\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1，\u6838\u5fc3\u6d41\u7a0b\u5982\u4e0b：<\/p>\n

  • \u56fa\u5b9a\u968f\u673a\u79cd\u5b50，\u4fdd\u8bc1\u5b9e\u9a8c\u7ed3\u679c\u53ef\u590d\u73b0<\/li>\n
  • \u5b9a\u4e49\u56fe\u50cf\u9884\u5904\u7406\u7b56\u7565（\u8bad\u7ec3\u96c6\u6570\u636e\u589e\u5f3a\u3001\u9a8c\u8bc1\u96c6\u4ec5\u6807\u51c6\u5316）<\/li>\n
  • \u81ea\u5b9a\u4e49\u6570\u636e\u96c6\u7c7b：\u652f\u6301\u8bfb\u53d6\u6709\u6807\u7b7e\u6570\u636e（\u8bad\u7ec3 \/ \u9a8c\u8bc1）\u548c\u65e0\u6807\u7b7e\u6570\u636e（\u534a\u76d1\u7763）<\/li>\n
  • \u5b9e\u73b0\u534a\u76d1\u7763\u903b\u8f91：\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5bf9\u65e0\u6807\u7b7e\u6570\u636e\u6253\u4f2a\u6807\u7b7e，\u7b5b\u9009\u7f6e\u4fe1\u5ea6\u9ad8\u7684\u6837\u672c\u52a0\u5165\u8bad\u7ec3<\/li>\n
  • \u6784\u5efa\u5377\u79ef\u795e\u7ecf\u7f51\u7edc（\u6216\u8c03\u7528\u9884\u8bad\u7ec3 VGG），\u5b8c\u6210\u6709\u76d1\u7763 + \u534a\u76d1\u7763\u7684\u6df7\u5408\u8bad\u7ec3<\/li>\n
  • \u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u76d1\u63a7\u635f\u5931\u548c\u51c6\u786e\u7387，\u4fdd\u5b58\u6700\u4f18\u6a21\u578b，\u7ed8\u5236\u8bad\u7ec3\u66f2\u7ebf<\/li>\n

    2. \u6838\u5fc3\u6a21\u5757\u9010\u884c\u62c6\u89e3<\/h4>\n

    （1）\u968f\u673a\u79cd\u5b50\u56fa\u5b9a：\u4fdd\u8bc1\u5b9e\u9a8c\u53ef\u590d\u73b0<\/h5>\n

    def seed_everything(seed):
    \n torch.manual_seed(seed) # CPU\u968f\u673a\u79cd\u5b50
    \n torch.cuda.manual_seed(seed) # \u5355\u4e2aGPU\u968f\u673a\u79cd\u5b50
    \n torch.cuda.manual_seed_all(seed) # \u591aGPU\u968f\u673a\u79cd\u5b50
    \n torch.backends.cudnn.benchmark = False # \u5173\u95ed\u81ea\u52a8\u4f18\u5316，\u907f\u514d\u968f\u673a\u6027
    \n torch.backends.cudnn.deterministic = True # \u5f3a\u5236\u786e\u5b9a\u6027\u7b97\u6cd5
    \n random.seed(seed) # Python\u539f\u751f\u968f\u673a\u79cd\u5b50
    \n np.random.seed(seed) # Numpy\u968f\u673a\u79cd\u5b50
    \n os.environ['PYTHONHASHSEED'] = str(seed) # \u54c8\u5e0c\u79cd\u5b50<\/p>\n

    seed_everything(0) # \u56fa\u5b9a\u79cd\u5b50\u4e3a0，\u6bcf\u6b21\u8fd0\u884c\u7ed3\u679c\u4e00\u81f4<\/p>\n

    \u5173\u952e\u77e5\u8bc6\u70b9：<\/p>\n

      \n
    • \u6df1\u5ea6\u5b66\u4e60\u4e2d GPU \u8fd0\u7b97\u3001\u6570\u636e\u6d17\u724c\u7b49\u64cd\u4f5c\u81ea\u5e26\u968f\u673a\u6027，\u56fa\u5b9a\u79cd\u5b50\u662f\u5b9e\u9a8c\u53ef\u590d\u73b0\u7684\u6838\u5fc3<\/li>\n
    • cudnn.deterministic = True \u4f1a\u727a\u7272\u4e00\u70b9\u901f\u5ea6，\u4f46\u4fdd\u8bc1\u6bcf\u6b21\u8ba1\u7b97\u7ed3\u679c\u5b8c\u5168\u4e00\u81f4<\/li>\n<\/ul>\n

      （2）\u56fe\u50cf\u9884\u5904\u7406：\u6570\u636e\u589e\u5f3a（\u8bad\u7ec3）vs \u7eaf\u8f6c\u6362（\u9a8c\u8bc1）<\/p>\n

      HW = 224 # \u56fe\u50cf\u7edf\u4e00\u7f29\u653e\u5230224×224（\u9002\u914dVGG\u7b49\u9884\u8bad\u7ec3\u6a21\u578b）<\/p>\n

      # \u8bad\u7ec3\u96c6：\u6570\u636e\u589e\u5f3a（\u63d0\u5347\u6cdb\u5316\u80fd\u529b）
      \ntrain_transform = transforms.Compose([
      \n transforms.ToPILImage(), # \u628anumpy\u6570\u7ec4\u8f6c\u6210PIL\u56fe\u50cf（\u56e0\u4e3a\u540e\u7eed\u64cd\u4f5c\u9700\u8981PIL\u683c\u5f0f）
      \n transforms.RandomResizedCrop(224), # \u968f\u673a\u88c1\u526a+\u7f29\u653e（\u6a21\u62df\u4e0d\u540c\u89c6\u89d2）
      \n transforms.RandomRotation(50), # \u968f\u673a\u65cb\u8f6c\u00b150\u5ea6
      \n transforms.ToTensor() # \u8f6cTensor：(H,W,C)\u2192(C,H,W)，\u503c\u5f52\u4e00\u5316\u5230[0,1]
      \n])<\/p>\n

      # \u9a8c\u8bc1\u96c6：\u65e0\u589e\u5f3a（\u907f\u514d\u5f15\u5165\u566a\u58f0，\u4fdd\u8bc1\u8bc4\u4f30\u51c6\u786e）
      \nval_transform = transforms.Compose([
      \n transforms.ToPILImage(),
      \n transforms.ToTensor()
      \n])<\/p>\n

      \u6838\u5fc3\u903b\u8f91：<\/p>\n

        \n
      • \u8bad\u7ec3\u96c6\u589e\u5f3a：\u901a\u8fc7\u968f\u673a\u88c1\u526a\u3001\u65cb\u8f6c\u589e\u52a0\u6570\u636e\u591a\u6837\u6027，\u9632\u6b62\u8fc7\u62df\u5408<\/li>\n
      • \u9a8c\u8bc1\u96c6\u4e0d\u589e\u5f3a：\u7528 \u201c\u5e72\u51c0\u201d \u7684\u6570\u636e\u8bc4\u4f30\u6a21\u578b\u771f\u5b9e\u6027\u80fd<\/li>\n
      • ToTensor() \u662f\u5173\u952e：PyTorch \u6a21\u578b\u8981\u6c42\u8f93\u5165\u662f (\u901a\u9053\u6570, \u9ad8\u5ea6, \u5bbd\u5ea6) \u7684 Tensor，\u800c PIL\/OpenCV \u8bfb\u53d6\u7684\u662f (H,W,C) \u7684\u6570\u7ec4，H\u662f\u9ad8\u5ea6\u3001W\u662f\u5bbd\u5ea6\u3001C\u662f\u901a\u9053\u6570<\/li>\n<\/ul>\n
        （3）\u81ea\u5b9a\u4e49\u6570\u636e\u96c6\u7c7b food_Dataset：\u5904\u7406\u6709\u6807\u7b7e \/ \u65e0\u6807\u7b7e\u6570\u636e<\/h5>\n

        \u8fd9\u662f\u4ee3\u7801\u7684\u6838\u5fc3\u4e4b\u4e00，\u652f\u6301 3 \u79cd\u6a21\u5f0f（train\/val\/semi），\u9002\u914d\u4e0d\u540c\u6570\u636e\u7c7b\u578b：<\/p>\n

        class food_Dataset(Dataset):
        \n def __init__(self, path, mode="train"):
        \n self.mode = mode
        \n # \u534a\u76d1\u7763\u6a21\u5f0f：\u53ea\u8bfb\u53d6\u65e0\u6807\u7b7e\u6570\u636e（\u65e0Y）
        \n if mode == "semi":
        \n self.X = self.read_file(path)
        \n # \u8bad\u7ec3\/\u9a8c\u8bc1\u6a21\u5f0f：\u8bfb\u53d6\u6709\u6807\u7b7e\u6570\u636e（X+Y）
        \n else:
        \n self.X, self.Y = self.read_file(path)
        \n self.Y = torch.LongTensor(self.Y) # \u6807\u7b7e\u8f6cLongTensor（\u5206\u7c7b\u4efb\u52a1\u8981\u6c42）<\/p>\n

        # \u7ed1\u5b9a\u9884\u5904\u7406\u7b56\u7565
        \n if mode == "train":
        \n self.transform = train_transform
        \n else:
        \n self.transform = val_transform<\/p>\n

        # \u6838\u5fc3：\u8bfb\u53d6\u6587\u4ef6\u7684\u51fd\u6570（\u533a\u5206\u6709\u6807\u7b7e\/\u65e0\u6807\u7b7e）
        \n def read_file(self, path):
        \n # \u534a\u76d1\u7763\u6a21\u5f0f：\u8bfb\u53d6\u65e0\u6807\u7b7e\u6587\u4ef6\u5939\u4e0b\u7684\u6240\u6709\u56fe\u7247
        \n if self.mode == "semi":
        \n file_list = os.listdir(path) # \u5217\u51fa\u6587\u4ef6\u5939\u4e0b\u6240\u6709\u56fe\u7247\u540d
        \n # \u521d\u59cb\u5316\u6570\u7ec4：(\u6837\u672c\u6570, 224, 224, 3)，uint8\u8282\u7701\u5185\u5b58
        \n xi = np.zeros((len(file_list), HW, HW, 3), dtype=np.uint8)
        \n for j, img_name in enumerate(file_list):
        \n img_path = os.path.join(path, img_name)
        \n img = Image.open(img_path).resize((HW, HW)) # \u7f29\u653e\u81f3224×224
        \n xi[j, …] = img # \u5b58\u5165\u6570\u7ec4
        \n print("\u8bfb\u5230\u4e86%d\u4e2a\u6570\u636e" % len(xi))
        \n return xi
        \n # \u6709\u6807\u7b7e\u6a21\u5f0f：\u6309\u7c7b\u522b\u6587\u4ef6\u5939\u8bfb\u53d6（00\/01\/…\/10\u517111\u7c7b）
        \n else:
        \n for i in tqdm(range(11)): # tqdm\u663e\u793a\u8fdb\u5ea6\u6761
        \n file_dir = path + "\/%02d" % i # \u62fc\u63a5\u7c7b\u522b\u6587\u4ef6\u5939\u8def\u5f84（00~10）
        \n file_list = os.listdir(file_dir)
        \n # \u521d\u59cb\u5316\u5f53\u524d\u7c7b\u522b\u7684\u6570\u636e\/\u6807\u7b7e
        \n xi = np.zeros((len(file_list), HW, HW, 3), dtype=np.uint8)
        \n yi = np.zeros(len(file_list), dtype=np.uint8)<\/p>\n

        for j, img_name in enumerate(file_list):
        \n img_path = os.path.join(file_dir, img_name)
        \n img = Image.open(img_path).resize((HW, HW))
        \n xi[j, …] = img
        \n yi[j] = i # \u6807\u7b7e=\u6587\u4ef6\u5939\u5bf9\u5e94\u7684\u7c7b\u522b（0~10）<\/p>\n

        # \u62fc\u63a5\u6240\u6709\u7c7b\u522b\u7684\u6570\u636e
        \n if i == 0:
        \n X = xi
        \n Y = yi
        \n else:
        \n X = np.concatenate((X, xi), axis=0)
        \n Y = np.concatenate((Y, yi), axis=0)
        \n print("\u8bfb\u5230\u4e86%d\u4e2a\u6570\u636e" % len(Y))
        \n return X, Y<\/p>\n

        # \u83b7\u53d6\u5355\u4e2a\u6837\u672c（Dataset\u5fc5\u987b\u5b9e\u73b0）
        \n def __getitem__(self, item):
        \n if self.mode == "semi":
        \n # \u534a\u76d1\u7763：\u8fd4\u56de\u9884\u5904\u7406\u540e\u7684\u56fe\u7247 + \u539f\u59cb\u56fe\u7247（\u540e\u7eed\u6253\u4f2a\u6807\u7b7e\u7528）
        \n return self.transform(self.X[item]), self.X[item]
        \n else:
        \n # \u6709\u6807\u7b7e：\u8fd4\u56de\u9884\u5904\u7406\u540e\u7684\u56fe\u7247 + \u6807\u7b7e
        \n return self.transform(self.X[item]), self.Y[item]<\/p>\n

        # \u8fd4\u56de\u6570\u636e\u96c6\u957f\u5ea6（Dataset\u5fc5\u987b\u5b9e\u73b0）
        \n def __len__(self):
        \n return len(self.X)<\/p>\n

        \u5bf9\u4e8e\u56fe\u50cf\u6570\u636e\u96c6\u7684\u8bfb\u53d6\u51fd\u6570\u00a0\u6709\u4e24\u79cd\u6a21\u5f0f<\/p>\n

        1.\u5728\u534a\u76d1\u7763\u6a21\u5f0f\u4e0b：<\/p>\n

        if self.mode == "semi":
        \n # \u5217\u51fa\u6587\u4ef6\u5939\u4e0b\u6240\u6709\u6587\u4ef6
        \n file_list = os.listdir(path)<\/p>\n

        # \u521d\u59cb\u5316\u6570\u7ec4: [\u6587\u4ef6\u6570, \u56fe\u7247\u9ad8\u5ea6, \u56fe\u7247\u5bbd\u5ea6, 3\u901a\u9053]
        \n xi = np.zeros((len(file_list), HW, HW, 3), dtype=np.uint8)<\/p>\n

        # \u904d\u5386\u6bcf\u4e2a\u56fe\u7247\u6587\u4ef6
        \n for j, img_name in enumerate(file_list):
        \n # \u5b8c\u6574\u7684\u56fe\u7247\u8def\u5f84
        \n img_path = os.path.join(path, img_name)<\/p>\n

        # \u6253\u5f00\u56fe\u7247
        \n img = Image.open(img_path)<\/p>\n

        # \u8c03\u6574\u5927\u5c0f\u5230\u7edf\u4e00\u5c3a\u5bf8 (HW \u00d7 HW)
        \n img = img.resize((HW, HW))<\/p>\n

        # \u5b58\u50a8\u56fe\u7247\u6570\u636e
        \n xi[j, …] = img # … \u8868\u793a\u6240\u6709\u7ef4\u5ea6<\/p>\n

        print("\u8bfb\u5230\u4e86%d\u4e2a\u6570\u636e" % len(xi))
        \n return xi # \u53ea\u8fd4\u56de\u56fe\u50cf\u6570\u636e，\u4e0d\u8fd4\u56de\u6807\u7b7e<\/p>\n

        \u534a\u76d1\u7763\u6a21\u5f0f\u7279\u70b9：<\/p>\n

          \n
        • \n

          \u6240\u6709\u56fe\u7247\u5728\u540c\u4e00\u4e2a\u6587\u4ef6\u5939<\/p>\n<\/li>\n

        • \n

          \u6ca1\u6709\u6807\u7b7e（\u53ea\u8fd4\u56deX，\u4e0d\u8fd4\u56deY）<\/p>\n<\/li>\n

        • \n

          \u7528\u4e8e\u8bad\u7ec3\u65f6\u4e0d\u9700\u8981\u6807\u7b7e\u7684\u60c5\u51b5<\/p>\n<\/li>\n<\/ul>\n

          2. \u5b8c\u6574\u76d1\u7763\u6a21\u5f0f:<\/p>\n

          else:
          \n # \u4f7f\u7528tqdm\u663e\u793a\u8fdb\u5ea6\u6761
          \n for i in tqdm(range(11)): # \u904d\u53860-10\u517111\u4e2a\u7c7b\u522b
          \n # \u7c7b\u522b\u6587\u4ef6\u5939\u8def\u5f84: path\/00, path\/01, …
          \n file_dir = path + "\/%02d" % i # %02d \u8868\u793a\u4e24\u4f4d\u6570\u5b57，\u598200,01<\/p>\n

          # \u5217\u51fa\u8be5\u7c7b\u522b\u4e0b\u6240\u6709\u56fe\u7247
          \n file_list = os.listdir(file_dir)<\/p>\n

          # \u521d\u59cb\u5316\u5f53\u524d\u7c7b\u522b\u7684\u6570\u636e
          \n xi = np.zeros((len(file_list), HW, HW, 3), dtype=np.uint8) # \u56fe\u50cf
          \n yi = np.zeros(len(file_list), dtype=np.uint8) # \u6807\u7b7e<\/p>\n

          # \u904d\u5386\u8be5\u7c7b\u522b\u7684\u6240\u6709\u56fe\u7247
          \n for j, img_name in enumerate(file_list):
          \n img_path = os.path.join(file_dir, img_name)
          \n img = Image.open(img_path)
          \n img = img.resize((HW, HW))
          \n xi[j, …] = img
          \n yi[j] = i # \u6587\u4ef6\u5939\u540d\u5c31\u662f\u6807\u7b7e（0,1,2…）<\/p>\n

          # \u62fc\u63a5\u6240\u6709\u7c7b\u522b\u7684\u6570\u636e
          \n if i == 0:
          \n X = xi # \u7b2c\u4e00\u4e2a\u7c7b\u522b
          \n Y = yi
          \n else:
          \n X = np.concatenate((X, xi), axis=0) # \u6cbf\u7b2c0\u7ef4（\u6837\u672c\u7ef4\u5ea6）\u62fc\u63a5
          \n Y = np.concatenate((Y, yi), axis=0)<\/p>\n

          print("\u8bfb\u5230\u4e86%d\u4e2a\u6570\u636e" % len(Y))
          \n return X, Y # \u8fd4\u56de\u56fe\u50cf\u548c\u6807\u7b7e<\/p>\n

          （4）\u534a\u76d1\u7763\u6838\u5fc3：semiDataset \u7c7b（\u7ed9\u65e0\u6807\u7b7e\u6570\u636e\u6253\u4f2a\u6807\u7b7e）<\/h5>\n

          \u8fd9\u662f\u534a\u76d1\u7763\u5b66\u4e60\u7684\u5173\u952e，\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5bf9\u65e0\u6807\u7b7e\u6570\u636e\u9884\u6d4b，\u7b5b\u9009\u7f6e\u4fe1\u5ea6\u9ad8\u7684\u6837\u672c：<\/p>\n

          class semiDataset(Dataset):
          \n def __init__(self, no_label_loder, model, device, thres=0.99):
          \n # \u6838\u5fc3：\u7528\u6a21\u578b\u7ed9\u65e0\u6807\u7b7e\u6570\u636e\u6253\u4f2a\u6807\u7b7e
          \n x, y = self.get_label(no_label_loder, model, device, thres)
          \n # \u65e0\u9ad8\u7f6e\u4fe1\u5ea6\u6837\u672c：\u6807\u8bb0\u4e3a\u65e0\u6548
          \n if x == []:
          \n self.flag = False
          \n # \u6709\u9ad8\u7f6e\u4fe1\u5ea6\u6837\u672c：\u6784\u5efa\u6570\u636e\u96c6
          \n else:
          \n self.flag = True
          \n self.X = np.array(x)
          \n self.Y = torch.LongTensor(y)
          \n self.transform = train_transform # \u7528\u8bad\u7ec3\u96c6\u589e\u5f3a\u7b56\u7565<\/p>\n

          def get_label(self, no_label_loder, model, device, thres):
          \n model = model.to(device)
          \n pred_prob = [] # \u5b58\u50a8\u9884\u6d4b\u7f6e\u4fe1\u5ea6
          \n labels = [] # \u5b58\u50a8\u9884\u6d4b\u6807\u7b7e
          \n x = [] # \u5b58\u50a8\u9ad8\u7f6e\u4fe1\u5ea6\u7684\u539f\u59cb\u56fe\u7247
          \n y = [] # \u5b58\u50a8\u9ad8\u7f6e\u4fe1\u5ea6\u7684\u4f2a\u6807\u7b7e
          \n soft = nn.Softmax() # \u628a\u6a21\u578b\u8f93\u51fa\u8f6c\u6210\u6982\u7387<\/p>\n

          with torch.no_grad(): # \u7981\u7528\u68af\u5ea6，\u63d0\u901f+\u7701\u5185\u5b58
          \n for bat_x, _ in no_label_loder: # \u904d\u5386\u65e0\u6807\u7b7e\u6570\u636e
          \n bat_x = bat_x.to(device)
          \n pred = model(bat_x) # \u6a21\u578b\u9884\u6d4b（logits）
          \n pred_soft = soft(pred) # \u8f6c\u6982\u7387（0~1）
          \n # \u53d6\u6bcf\u4e2a\u6837\u672c\u7684\u6700\u5927\u6982\u7387\u548c\u5bf9\u5e94\u6807\u7b7e
          \n pred_max, pred_value = pred_soft.max(1)
          \n pred_prob.extend(pred_max.cpu().numpy().tolist())
          \n labels.extend(pred_value.cpu().numpy().tolist())<\/p>\n

          # \u7b5b\u9009\u7f6e\u4fe1\u5ea6>\u9608\u503c\u7684\u6837\u672c
          \n for index, prob in enumerate(pred_prob):
          \n if prob > thres: # \u7f6e\u4fe1\u5ea6>0.99\u624d\u4fdd\u7559
          \n x.append(no_label_loder.dataset[index][1]) # \u539f\u59cb\u56fe\u7247
          \n y.append(labels[index]) # \u4f2a\u6807\u7b7e
          \n return x, y<\/p>\n

          def __getitem__(self, item):
          \n return self.transform(self.X[item]), self.Y[item]<\/p>\n

          def __len__(self):
          \n return len(self.X)<\/p>\n

          # \u8f85\u52a9\u51fd\u6570：\u521b\u5efa\u534a\u76d1\u7763\u6570\u636e\u52a0\u8f7d\u5668
          \ndef get_semi_loader(no_label_loder, model, device, thres):
          \n semiset = semiDataset(no_label_loder, model, device, thres)
          \n if semiset.flag == False:
          \n return None
          \n else:
          \n semi_loader = DataLoader(semiset, batch_size=16, shuffle=False)
          \n return semi_loader<\/p>\n

          \u00a0 \u00a0 \u5bf9\u4e8e\u4ee3\u7801\u4e2d\u7684\u6838\u5fc3\u51fd\u6570get_label\u8fd9\u91cc\u7ed9\u51fa\u8be6\u7ec6\u6ce8\u91ca：<\/p>\n

          def get_label(self, no_label_loder, model, device, thres):
          \n # \u51c6\u5907\u5de5\u4f5c
          \n model = model.to(device) # \u628a\u6a21\u578b\u642c\u5230GPU（\u5982\u679c\u6709）
          \n pred_prob = [] # \u5b58\u50a8\u6240\u6709\u56fe\u7247\u7684\u9884\u6d4b\u7f6e\u4fe1\u5ea6
          \n labels = [] # \u5b58\u50a8\u6240\u6709\u56fe\u7247\u7684\u9884\u6d4b\u6807\u7b7e
          \n x = [] # \u5b58\u50a8\u9ad8\u7f6e\u4fe1\u5ea6\u56fe\u7247（\u8981\u8fd4\u56de\u7684）
          \n y = [] # \u5b58\u50a8\u9ad8\u7f6e\u4fe1\u5ea6\u4f2a\u6807\u7b7e（\u8981\u8fd4\u56de\u7684）
          \n soft = nn.Softmax() # Softmax\u5c42：\u628a\u6a21\u578b\u8f93\u51fa\u8f6c\u6210\u6982\u7387（0~1\u4e4b\u95f4）<\/p>\n

          with torch.no_grad(): # 🚩\u975e\u5e38\u91cd\u8981！\u7981\u7528\u68af\u5ea6\u8ba1\u7b97，\u52a0\u901f\u63a8\u7406，\u8282\u7701\u663e\u5b58
          \n for bat_x, _ in no_label_loder: # \u904d\u5386\u65e0\u6807\u7b7e\u6570\u636e，_\u5360\u4f4d\u7b26\u8868\u793a\u201c\u5ffd\u7565\u6807\u7b7e\u201d
          \n bat_x = bat_x.to(device) # \u628a\u56fe\u7247\u642c\u5230GPU
          \n pred = model(bat_x) # \u6a21\u578b\u524d\u5411\u4f20\u64ad，\u8f93\u51falogits（\u672a\u5f52\u4e00\u5316\u7684\u5206\u6570）
          \n pred_soft = soft(pred) # Softmax\u5f52\u4e00\u5316，\u53d8\u6210\u6982\u7387<\/p>\n

          # max(1)\u7684\u542b\u4e49：
          \n # – \u53c2\u65701\u8868\u793a\u5728\u7ef4\u5ea61（\u7c7b\u522b\u7ef4\u5ea6）\u4e0a\u53d6\u6700\u5927\u503c
          \n # – \u8fd4\u56de\u503c1: \u6700\u5927\u6982\u7387\u503c（\u59820.99）
          \n # – \u8fd4\u56de\u503c2: \u6700\u5927\u6982\u7387\u5bf9\u5e94\u7684\u7d22\u5f15（\u59823，\u4ee3\u8868\u7b2c3\u7c7b）
          \n pred_max, pred_value = pred_soft.max(1)<\/p>\n

          # \u6536\u96c6\u8fd9\u4e00\u6279\u6b21\u7684\u7ed3\u679c
          \n # .cpu()：\u4eceGPU\u642c\u5230CPU
          \n # .numpy()：\u8f6c\u6210numpy\u6570\u7ec4
          \n # .tolist()：\u8f6c\u6210Python\u5217\u8868
          \n pred_prob.extend(pred_max.cpu().numpy().tolist())
          \n labels.extend(pred_value.cpu().numpy().tolist())<\/p>\n

          # 🚩\u7b5b\u9009\u7f6e\u4fe1\u5ea6>\u9608\u503c\u7684\u6837\u672c
          \n for index, prob in enumerate(pred_prob):
          \n if prob > thres: # \u53ea\u6709\u9ad8\u7f6e\u4fe1\u5ea6\u7684\u624d\u4fdd\u7559
          \n # \u6ce8\u610f\u8fd9\u91cc：no_label_loder.dataset[index][1]
          \n # – no_label_loder.dataset: \u539f\u59cb\u6570\u636e\u96c6
          \n # – [index]: \u7b2cindex\u4e2a\u6837\u672c
          \n # – [1]: \u53d6\u8fd9\u4e2a\u6837\u672c\u7684\u7b2c2\u4e2a\u5143\u7d20（\u901a\u5e38\u662f\u56fe\u7247）
          \n x.append(no_label_loder.dataset[index][1]) # \u539f\u59cb\u56fe\u7247
          \n y.append(labels[index]) # \u5bf9\u5e94\u7684\u4f2a\u6807\u7b7e<\/p>\n

          return x, y<\/p>\n

          \u6838\u5fc3\u903b\u8f91（\u534a\u76d1\u7763\u5173\u952e）：<\/p>\n

        • \u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5bf9\u65e0\u6807\u7b7e\u6570\u636e\u9884\u6d4b，\u901a\u8fc7Softmax\u8f6c\u6982\u7387<\/li>\n
        • \u7b5b\u9009\u7f6e\u4fe1\u5ea6 > 0.99 \u7684\u6837\u672c（\u8ba4\u4e3a\u6a21\u578b\u5bf9\u8fd9\u4e9b\u6837\u672c\u7684\u9884\u6d4b\u8db3\u591f\u53ef\u9760）<\/li>\n
        • \u628a\u8fd9\u4e9b\u9ad8\u7f6e\u4fe1\u5ea6\u6837\u672c\u7684 \u201c\u4f2a\u6807\u7b7e\u201d \u5f53\u4f5c\u771f\u5b9e\u6807\u7b7e，\u52a0\u5165\u8bad\u7ec3\u96c6<\/li>\n
        • \u9608\u503cthres=0.99 \u662f\u8d85\u53c2\u6570：\u503c\u8d8a\u9ad8，\u4f2a\u6807\u7b7e\u8d8a\u53ef\u9760，\u4f46\u6837\u672c\u8d8a\u5c11；\u503c\u8d8a\u4f4e，\u6837\u672c\u8d8a\u591a，\u4f46\u566a\u58f0\u8d8a\u5927<\/li>\n
          （5）\u81ea\u5b9a\u4e49 CNN \u6a21\u578b myModel<\/h5>\n

          \u4ee3\u7801\u4e2d\u5b9e\u9645\u7528\u4e86\u9884\u8bad\u7ec3 VGG，\u4f46\u4fdd\u7559\u4e86\u81ea\u5b9a\u4e49 CNN \u4f5c\u4e3a\u5907\u9009，\u7ed3\u6784\u5982\u4e0b：<\/p>\n

          class myModel(nn.Module):
          \n def __init__(self, num_class):
          \n super(myModel, self).__init__()
          \n # \u5377\u79ef\u5c42：\u63d0\u53d6\u56fe\u50cf\u7279\u5f81（\u7c7b\u4f3cVGG\u7684\u7b80\u5316\u7248）
          \n self.conv1 = nn.Conv2d(3, 64, 3, 1, 1) # 3\u219264\u901a\u9053，3×3\u5377\u79ef，padding=1（\u4fdd\u6301\u5c3a\u5bf8）
          \n self.bn1 = nn.BatchNorm2d(64) # \u6279\u91cf\u5f52\u4e00\u5316：\u52a0\u901f\u8bad\u7ec3，\u9632\u6b62\u68af\u5ea6\u6d88\u5931
          \n self.relu = nn.ReLU()
          \n self.pool1 = nn.MaxPool2d(2) # \u6c60\u5316：\u7f29\u5c0f\u5c3a\u5bf8，\u4fdd\u7559\u5173\u952e\u7279\u5f81<\/p>\n

          # \u5806\u53e0\u5377\u79ef\u5c42（\u6bcf\u5c42\u901a\u9053\u6570\u7ffb\u500d，\u5c3a\u5bf8\u51cf\u534a）
          \n self.layer1 = nn.Sequential(nn.Conv2d(64, 128, 3, 1, 1), nn.BatchNorm2d(128), nn.ReLU(), nn.MaxPool2d(2))
          \n self.layer2 = nn.Sequential(nn.Conv2d(128, 256, 3, 1, 1), nn.BatchNorm2d(256), nn.ReLU(), nn.MaxPool2d(2))
          \n self.layer3 = nn.Sequential(nn.Conv2d(256, 512, 3, 1, 1), nn.BatchNorm2d(512), nn.ReLU(), nn.MaxPool2d(2))<\/p>\n

          self.pool2 = nn.MaxPool2d(2) # \u6700\u7ec8\u7279\u5f81\u56fe：512x7x7
          \n # \u5168\u8fde\u63a5\u5c42：\u5206\u7c7b
          \n self.fc1 = nn.Linear(25088, 1000) # 512*7*7=25088 \u2192 1000\u7ef4\u9690\u85cf\u5c42
          \n self.relu2 = nn.ReLU()
          \n self.fc2 = nn.Linear(1000, num_class) # 1000\u219211\u7c7b（\u6700\u7ec8\u8f93\u51fa）<\/p>\n

          def forward(self, x):
          \n # \u5377\u79ef\u5c42\u63d0\u53d6\u7279\u5f81
          \n x = self.conv1(x)
          \n x = self.bn1(x)
          \n x = self.relu(x)
          \n x = self.pool1(x)
          \n x = self.layer1(x)
          \n x = self.layer2(x)
          \n x = self.layer3(x)
          \n x = self.pool2(x)
          \n # \u5c55\u5e73：(batch, 512,7,7) \u2192 (batch, 25088)
          \n x = x.view(x.size()[0], -1)
          \n # \u5168\u8fde\u63a5\u5c42\u5206\u7c7b
          \n x = self.fc1(x)
          \n x = self.relu2(x)
          \n x = self.fc2(x)
          \n return x<\/p>\n

          Conv2d\u53c2\u6570\u542b\u4e49\u901f\u67e5\u8868：<\/h3>\n\n\u53c2\u6570\u4f4d\u7f6e\u4ee3\u7801\u4e2d\u7684\u503c\u53c2\u6570\u540d\u542b\u4e49\u7c7b\u6bd4\u7406\u89e3<\/tr>\n\n\n\n\n\n\n
          \u7b2c1\u4e2a<\/td>\n3<\/td>\nin_channels<\/td>\n\u8f93\u5165\u901a\u9053\u6570<\/td>\n\u5f69\u8272\u56fe\u7247\u6709RGB 3\u4e2a\u901a\u9053<\/td>\n<\/tr>\n
          \u7b2c2\u4e2a<\/td>\n64<\/td>\nout_channels<\/td>\n\u8f93\u51fa\u901a\u9053\u6570<\/td>\n\u752864\u4e2a\u4e0d\u540c\u7684\u5377\u79ef\u6838\u63d0\u53d6\u7279\u5f81<\/td>\n<\/tr>\n
          \u7b2c3\u4e2a<\/td>\n3<\/td>\nkernel_size<\/td>\n\u5377\u79ef\u6838\u5927\u5c0f<\/td>\n3×3\u7684\u8fc7\u6ee4\u5668<\/td>\n<\/tr>\n
          \u7b2c4\u4e2a<\/td>\n1<\/td>\nstride<\/td>\n\u6b65\u957f<\/td>\n\u6bcf\u6b21\u79fb\u52a81\u4e2a\u50cf\u7d20<\/td>\n<\/tr>\n
          \u7b2c5\u4e2a<\/td>\n1<\/td>\npadding<\/td>\n\u586b\u5145<\/td>\n\u5468\u56f4\u88651\u57080<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u8ba1\u7b97\u516c\u5f0f：\u8f93\u51fa\u9ad8\u5ea6 H_out = (H_in + 2*padding – kernel_size) \/\/ stride + 1
          \n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u8f93\u51fa\u5bbd\u5ea6 W_out = (W_in + 2*padding – kernel_size) \/\/ stride + 1<\/p>\n

          \u8f93\u5165: 224×224
          \nconv = nn.Conv2d(3, 64, 3, 1, 1)<\/p>\n

          H_out = (224 + 2*1 – 3) \/\/ 1 + 1
          \n = (224 + 2 – 3) \/\/ 1 + 1
          \n = 223 \/\/ 1 + 1
          \n = 223 + 1
          \n = 224 \u2705 \u5c3a\u5bf8\u4fdd\u6301\u4e0d\u53d8！<\/p>\n

          \u5173\u952e\u77e5\u8bc6\u70b9：<\/p>\n

            \n
          • \u5377\u79ef\u5c42\u89c4\u5f8b：\u901a\u9053\u6570\u4ece 3\u219264\u2192128\u2192256\u2192512（\u9010\u6b65\u63d0\u53d6\u9ad8\u5c42\u7279\u5f81）<\/li>\n
          • \u6c60\u5316\u5c42\u4f5c\u7528：\u6bcf\u6b21\u5c3a\u5bf8\u51cf\u534a，\u6700\u7ec8 224×224\u21927×7<\/li>\n
          • x.view(x.size()[0], -1)：\u5c55\u5e73\u7279\u5f81\u56fe，\u4e3a\u5168\u8fde\u63a5\u5c42\u505a\u51c6\u5907（PyTorch \u4e2d-1\u8868\u793a\u81ea\u52a8\u8ba1\u7b97\u7ef4\u5ea6）<\/li>\n<\/ul>\n
            （6）\u6838\u5fc3\u8bad\u7ec3\u51fd\u6570 train_val：\u6709\u76d1\u7763 + \u534a\u76d1\u7763\u6df7\u5408\u8bad\u7ec3<\/h5>\n

            \u8fd9\u662f\u4ee3\u7801\u7684\u6267\u884c\u6838\u5fc3，\u6574\u5408\u4e86\u6709\u76d1\u7763\u8bad\u7ec3\u3001\u534a\u76d1\u7763\u6837\u672c\u7b5b\u9009\u3001\u9a8c\u8bc1\u8bc4\u4f30：<\/p>\n

            def train_val(model, train_loader, val_loader, no_label_loader, device, epochs, optimizer, loss, thres, save_path):
            \n model = model.to(device)
            \n semi_loader = None # \u534a\u76d1\u7763\u6570\u636e\u52a0\u8f7d\u5668（\u521d\u59cb\u4e3a\u7a7a）
            \n # \u8bb0\u5f55\u8bad\u7ec3\/\u9a8c\u8bc1\u7684\u635f\u5931\u548c\u51c6\u786e\u7387
            \n plt_train_loss = []
            \n plt_val_loss = []
            \n plt_train_acc = []
            \n plt_val_acc = []
            \n max_acc = 0.0 # \u8bb0\u5f55\u6700\u9ad8\u9a8c\u8bc1\u51c6\u786e\u7387<\/p>\n

            for epoch in range(epochs):
            \n train_loss = 0.0
            \n val_loss = 0.0
            \n train_acc = 0.0
            \n val_acc = 0.0
            \n semi_loss = 0.0
            \n semi_acc = 0.0
            \n start_time = time.time()<\/p>\n

            # ========== 1. \u6709\u76d1\u7763\u8bad\u7ec3（\u8bad\u7ec3\u96c6） ==========
            \n model.train() # \u8bad\u7ec3\u6a21\u5f0f（\u542f\u7528BN\/Dropout）
            \n for batch_x, batch_y in train_loader:
            \n x, target = batch_x.to(device), batch_y.to(device)
            \n pred = model(x) # \u6a21\u578b\u9884\u6d4b
            \n train_bat_loss = loss(pred, target) # \u8ba1\u7b97\u635f\u5931
            \n train_bat_loss.backward() # \u53cd\u5411\u4f20\u64ad
            \n optimizer.step() # \u66f4\u65b0\u53c2\u6570
            \n optimizer.zero_grad() # \u68af\u5ea6\u6e05\u96f6（\u5fc5\u987b！）
            \n train_loss += train_bat_loss.cpu().item()# \u7d2f\u52a0\u635f\u5931
            \n # \u8ba1\u7b97\u51c6\u786e\u7387：\u9884\u6d4b\u6807\u7b7e=argmax(pred)，\u548c\u771f\u5b9e\u6807\u7b7e\u6bd4\u8f83
            \n train_acc += np.sum(np.argmax(pred.detach().cpu().numpy(), axis=1) == target.cpu().numpy())
            \n # \u8bb0\u5f55\u5e73\u5747\u635f\u5931\u548c\u51c6\u786e\u7387
            \n plt_train_loss.append(train_loss \/ len(train_loader))
            \n plt_train_acc.append(train_acc \/ len(train_loader.dataset))<\/p>\n

            # ========== 2. \u534a\u76d1\u7763\u8bad\u7ec3（\u53ef\u9009） ==========
            \n if semi_loader != None: # \u6709\u534a\u76d1\u7763\u6570\u636e\u624d\u8bad\u7ec3
            \n for batch_x, batch_y in semi_loader:
            \n x, target = batch_x.to(device), batch_y.to(device)
            \n pred = model(x)
            \n semi_bat_loss = loss(pred, target)
            \n semi_bat_loss.backward()
            \n optimizer.step()
            \n optimizer.zero_grad()
            \n semi_loss += train_bat_loss.cpu().item() # \u6ce8\u610f：\u8fd9\u91cc\u4ee3\u7801\u6709\u7b14\u8bef，\u5e94\u8be5\u662fsemi_bat_loss
            \n semi_acc += np.sum(np.argmax(pred.detach().cpu().numpy(), axis=1) == target.cpu().numpy())
            \n print("\u534a\u76d1\u7763\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u51c6\u786e\u7387\u4e3a", semi_acc \/ len(semi_loader.dataset)) # \u4fee\u6b63：\u7528semi_loader\u7684\u957f\u5ea6<\/p>\n

            # ========== 3. \u9a8c\u8bc1\u96c6\u8bc4\u4f30 ==========
            \n model.eval() # \u8bc4\u4f30\u6a21\u5f0f（\u5173\u95edBN\/Dropout）
            \n with torch.no_grad(): # \u7981\u7528\u68af\u5ea6，\u63d0\u901f
            \n for batch_x, batch_y in val_loader:
            \n x, target = batch_x.to(device), batch_y.to(device)
            \n pred = model(x)
            \n val_bat_loss = loss(pred, target)
            \n val_loss += val_bat_loss.cpu().item()
            \n val_acc += np.sum(np.argmax(pred.detach().cpu().numpy(), axis=1) == target.cpu().numpy())
            \n # \u8bb0\u5f55\u9a8c\u8bc1\u635f\u5931\/\u51c6\u786e\u7387（\u6ce8\u610f：\u4ee3\u7801\u539f\u7b14\u8bef\u662fval_loader.dataset.__len__()，\u7edf\u4e00\u7528len(val_loader.dataset)）
            \n plt_val_loss.append(val_loss \/ len(val_loader.dataset))
            \n plt_val_acc.append(val_acc \/ len(val_loader.dataset))<\/p>\n

            # ========== 4. \u6bcf3\u8f6e\u7b5b\u9009\u4e00\u6b21\u534a\u76d1\u7763\u6570\u636e ==========
            \n if epoch % 3 == 0 and plt_val_acc[-1] > 0.6: # \u9a8c\u8bc1\u51c6\u786e\u7387>0.6\u624d\u5f00\u59cb\u534a\u76d1\u7763（\u6a21\u578b\u8db3\u591f\u597d）
            \n semi_loader = get_semi_loader(no_label_loader, model, device, thres)<\/p>\n

            # ========== 5. \u4fdd\u5b58\u6700\u4f18\u6a21\u578b ==========
            \n if val_acc > max_acc: # \u9a8c\u8bc1\u51c6\u786e\u7387\u66f4\u9ad8\u65f6\u4fdd\u5b58
            \n torch.save(model, save_path)
            \n max_acc = val_acc # \u4fee\u6b63：\u539f\u4ee3\u7801\u662fmax_acc = val_loss，\u660e\u663e\u9519\u8bef！<\/p>\n

            # ========== 6. \u6253\u5370\u8bad\u7ec3\u65e5\u5fd7 ==========
            \n print('[%03d\/%03d] %2.2f sec(s) TrainLoss : %.6f | valLoss: %.6f Trainacc : %.6f | valacc: %.6f' % \\\\
            \n (epoch, epochs, time.time() – start_time, plt_train_loss[-1], plt_val_loss[-1], plt_train_acc[-1], plt_val_acc[-1]))<\/p>\n

            # ========== 7. \u7ed8\u5236\u8bad\u7ec3\u66f2\u7ebf ==========
            \n plt.plot(plt_train_loss)
            \n plt.plot(plt_val_loss)
            \n plt.title("loss")
            \n plt.legend(["train", "val"])
            \n plt.show()<\/p>\n

            plt.plot(plt_train_acc)
            \n plt.plot(plt_val_acc)
            \n plt.title("acc")
            \n plt.legend(["train", "val"])
            \n plt.show()<\/p>\n

            （7）\u4e3b\u7a0b\u5e8f：\u914d\u7f6e\u53c2\u6570 + \u6267\u884c\u8bad\u7ec3<\/p>\n

            # \u6570\u636e\u8def\u5f84（\u9002\u914d\u6837\u672c\u96c6）
            \ntrain_path = r"E:\\\\fenlei\\\\food_classification\\\\food-11_sample\\\\training\\\\labeled"
            \nval_path = r"E:\\\\fenlei\\\\food_classification\\\\food-11_sample\\\\validation"
            \nno_label_path = r"E:\\\\fenlei\\\\food_classification\\\\food-11_sample\\\\training\\\\unlabeled\\\\00"<\/p>\n

            # \u6784\u5efa\u6570\u636e\u96c6
            \ntrain_set = food_Dataset(train_path, "train")
            \nval_set = food_Dataset(val_path, "val")
            \nno_label_set = food_Dataset(no_label_path, "semi")<\/p>\n

            # \u6784\u5efa\u6570\u636e\u52a0\u8f7d\u5668（batch_size=16，\u8bad\u7ec3\u96c6shuffle=True）
            \ntrain_loader = DataLoader(train_set, batch_size=16, shuffle=True)
            \nval_loader = DataLoader(val_set, batch_size=16, shuffle=True)
            \nno_label_loader = DataLoader(no_label_set, batch_size=16, shuffle=False)<\/p>\n

            # \u6a21\u578b\u9009\u62e9：\u7528\u9884\u8bad\u7ec3VGG（\u800c\u975e\u81ea\u5b9a\u4e49CNN）
            \nmodel, _ = initialize_model("vgg", 11, use_pretrained=True)<\/p>\n

            # \u8bad\u7ec3\u914d\u7f6e
            \nlr = 0.001
            \nloss = nn.CrossEntropyLoss() # \u5206\u7c7b\u4efb\u52a1\u6807\u914d\u635f\u5931
            \noptimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4) # AdamW\u5e26\u6743\u91cd\u8870\u51cf（L2\u6b63\u5219）
            \ndevice = "cuda" if torch.cuda.is_available() else "cpu" # GPU\u4f18\u5148
            \nsave_path = "model_save\/best_model.pth"
            \nepochs = 15
            \nthres = 0.99<\/p>\n

            # \u542f\u52a8\u8bad\u7ec3
            \ntrain_val(model, train_loader, val_loader, no_label_loader, device, epochs, optimizer, loss, thres, save_path)<\/p>\n

              \n
            • \n

              \u5173\u952e\u77e5\u8bc6\u70b9：<\/p>\n<\/li>\n

            • \u9884\u8bad\u7ec3\u6a21\u578b：initialize_model("vgg", 11) \u52a0\u8f7d VGG16\/19 \u9884\u8bad\u7ec3\u6743\u91cd，\u51bb\u7ed3\u5e95\u5c42\u7279\u5f81\u63d0\u53d6\u5c42（\u4ee3\u7801\u4e2d\u672a\u663e\u5f0f\u51bb\u7ed3，\u4f46use_pretrained=True\u4f1a\u52a0\u8f7d\u6743\u91cd）<\/li>\n
            • \u4f18\u5316\u5668：AdamW \u662f Adam + \u6743\u91cd\u8870\u51cf，\u6bd4 SGD \u6536\u655b\u66f4\u5feb，\u6bd4\u539f\u59cb Adam \u66f4\u7a33\u5b9a<\/li>\n
            • weight_decay=1e-4：L2 \u6b63\u5219，\u9632\u6b62\u8fc7\u62df\u5408<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

              1. \u4ee3\u7801\u6574\u4f53\u529f\u80fd\u6982\u8ff0\u8fd9\u6bb5\u4ee3\u7801\u5b9e\u73b0\u4e86\u4e00\u4e2a\u534a\u76d1\u7763\u5b66\u4e60\u7684\u98df\u54c1\u56fe\u50cf\u5206\u7c7b\u4efb\u52a1，\u6838\u5fc3\u6d41\u7a0b\u5982\u4e0b：\u56fa\u5b9a\u968f\u673a\u79cd\u5b50，\u4fdd\u8bc1\u5b9e\u9a8c\u7ed3\u679c\u53ef\u590d\u73b0\u5b9a\u4e49\u56fe\u50cf\u9884\u5904\u7406\u7b56\u7565（\u8bad\u7ec3\u96c6\u6570\u636e\u589e\u5f3a\u3001\u9a8c\u8bc1\u96c6\u4ec5\u6807\u51c6\u5316）\u81ea\u5b9a\u4e49\u6570\u636e\u96c6\u7c7b：\u652f\u6301\u8bfb\u53d6\u6709\u6807\u7b7e\u6570\u636e（\u8bad\u7ec3 \/ \u9a8c\u8bc1）\u548c\u65e0\u6807\u7b7e\u6570\u636e（\u534a\u76d1\u7763）\u5b9e\u73b0\u534a\u76d1\u7763\u903b\u8f91：\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5bf9\u65e0\u6807\u7b7e\u6570\u636e\u6253\u4f2a\u6807\u7b7e，\u7b5b\u9009\u7f6e\u4fe1\u5ea6\u9ad8\u7684\u6837\u672c<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[152,2395,86],"topic":[],"class_list":["post-75471","post","type-post","status-publish","format-standard","hentry","category-server","tag-pytorch","tag-2395","tag-86"],"yoast_head":"\n\u57fa\u4e8e PyTorch \u7684\u534a\u76d1\u7763\u98df\u54c1\u5206\u7c7b - 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