diff --git a/config/REPST/PEMS-BAY.yaml b/config/REPST/PEMS-BAY.yaml
index 3209bd0..0eacf3e 100755
--- a/config/REPST/PEMS-BAY.yaml
+++ b/config/REPST/PEMS-BAY.yaml
@@ -1,7 +1,7 @@
 basic:
   dataset: "PEMS-BAY"
   mode : "train"
-  device : "cuda:1"
+  device : "cuda:0"
   model: "REPST"
   seed: 2023
 
diff --git a/run.py b/run.py
index 175367f..741f9d7 100755
--- a/run.py
+++ b/run.py
@@ -46,7 +46,7 @@ def main():
             model.load_state_dict(
                 torch.load(
                     f"./pre-trained/{args['basic']['model']}/{args['basic']['dataset']}.pth",
-                    map_location=args["device"],
+                    map_location=args["basic"]["device"],
                     weights_only=True,
                 )
             )
diff --git a/trainer/Trainer.py b/trainer/Trainer.py
index 3c0e2b1..c7abff8 100755
--- a/trainer/Trainer.py
+++ b/trainer/Trainer.py
@@ -180,8 +180,8 @@ class Trainer:
                 loss = self.loss(output, label)
 
                 # 反归一化
-                self.scaler.inverse_transform(output)
-                self.scaler.inverse_transform(label)
+                d_output = self.scaler.inverse_transform(output)
+                d_label = self.scaler.inverse_transform(label)
 
                 # 反向传播和优化（仅在训练模式）
                 if optimizer_step and self.optimizer is not None:
@@ -194,6 +194,9 @@ class Trainer:
                             self.model.parameters(), self.args["max_grad_norm"]
                         )
                     self.optimizer.step()
+                
+                # 反归一化的loss
+                d_loss = self.loss(d_output, d_label)
 
                 # 记录步骤时间和内存使用
                 step_time = time.time() - start_time
@@ -205,7 +208,7 @@ class Trainer:
                 y_true.append(label.detach().cpu())
 
                 # 更新进度条
-                progress_bar.set_postfix(loss=loss.item())
+                progress_bar.set_postfix(loss=d_loss.item())
 
         # 合并所有批次的预测结果
         y_pred = torch.cat(y_pred, dim=0)