diff --git a/exp2/modified/train.py b/exp2/modified/train.py
index 58d931b..b8ca5df 100644
--- a/exp2/modified/train.py
+++ b/exp2/modified/train.py
@@ -15,86 +15,6 @@ sys.path.append(str(Path(__file__).resolve().parent.parent.parent))
 import gpu_utils
 
 
-# class Trainer:
-
-#     device: torch.device
-#     data_source: MnistDataLoaders
-#     model: Cnn
-
-#     def __init__(self):
-#         self.device = gpu_utils.get_gpu_device()
-#         self.data_source = MnistDataLoaders(Trainer.N_BATCH_SIZE)
-#         self.model = Cnn().to(self.device)
-#         # 展示模型结构。批次为指定批次数量，通道只有一个灰度通道，大小28x28。
-#         torchinfo.summary(self.model, (Trainer.N_BATCH_SIZE, 1, 28, 28))
-
-#     def train(self):
-#         optimizer = torch.optim.Adam(self.model.parameters(), eps=1e-7)
-#         # optimizer = torch.optim.AdamW(
-#         #     self.model.parameters(),
-#         #     lr=0.001,          # 两者默认学习率都是 0.001
-#         #     betas=(0.9, 0.999), # 两者默认值相同
-#         #     eps=1e-07,         # 【关键】匹配 TensorFlow 的默认 epsilon
-#         #     weight_decay=0.0,   # 两者默认都是 0
-#         #     amsgrad=False       # 两者默认都是 False
-#         # )
-#         loss_func = torch.nn.CrossEntropyLoss()
-
-#         for epoch in range(Trainer.N_EPOCH):
-#             self.model.train()
-
-#             batch_images: torch.Tensor
-#             batch_labels: torch.Tensor
-#             for batch_index, (batch_images, batch_labels) in enumerate(self.data_source.train_loader):
-#                 gpu_images = batch_images.to(self.device)
-#                 gpu_labels = batch_labels.to(self.device)
-
-#                 prediction: torch.Tensor = self.model(gpu_images)
-#                 loss: torch.Tensor = loss_func(prediction, gpu_labels)
-#                 optimizer.zero_grad()
-#                 loss.backward()
-#                 optimizer.step()
-
-#                 if batch_index % 100 == 0:
-#                     literal_loss = loss.item()
-#                     print(f'Epoch: {epoch+1}, Batch: {batch_index}, Loss: {literal_loss:.4f}')
-
-#     def save(self):
-#         file_dir_path = Path(__file__).resolve().parent.parent / 'models'
-#         file_dir_path.mkdir(parents=True, exist_ok=True)
-#         file_path = file_dir_path / 'cnn.pth'
-#         torch.save(self.model.state_dict(), file_path)
-#         print(f'模型已保存至：{file_path}')
-
-#     def test(self):
-#         self.model.eval()
-#         correct_sum = 0
-#         total_sum = 0
-
-#         with torch.no_grad():
-#             batch_images: torch.Tensor
-#             batch_labels: torch.Tensor
-#             for batch_images, batch_labels in self.data_source.test_loader:
-#                 gpu_images = batch_images.to(self.device)
-#                 gpu_labels = batch_labels.to(self.device)
-
-#                 possibilities: torch.Tensor = self.model(gpu_images)
-#                 # 输出出来是10个数字各自的可能性，所以要选取最高可能性的那个对比
-#                 # 在dim=1上找最大的那个，就选那个。dim=0是批次所以不管他。
-#                 _, prediction = possibilities.max(1)
-#                 # 返回标签的个数作为这一批的总个数
-#                 total_sum += gpu_labels.size(0)
-#                 correct_sum += prediction.eq(gpu_labels).sum()
-
-#         test_acc = 100. * correct_sum / total_sum
-#         print(f"准确率: {test_acc:.4f}%，共测试了{total_sum}张图片")
-
-# def main():
-#     trainer = Trainer()
-#     trainer.train()
-#     trainer.save()
-#     trainer.test()
-
 class Trainer:
     """核心训练器"""
 
@@ -122,7 +42,7 @@ class Trainer:
             self.model, optimizer, criterion, self.device)
         # 将训练器关联到进度条
         self.pbar = ProgressBar(persist=True)
-        self.pbar.attach(self.trainer, output_transform=lambda o: {"loss": o})
+        self.pbar.attach(self.trainer, output_transform=lambda loss: {"loss": loss})
 
         # 创建测试的评估器的评估量
         evaluator_metrics = {