diff --git a/level1/train.py b/level1/train.py
index e2c729e..29565f5 100644
--- a/level1/train.py
+++ b/level1/train.py
@@ -32,7 +32,7 @@ def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
             running_corrects = 0.0
 
             # Iterate over data.
-            for data in dataloders[phase]:
+            for data in dataloaders[phase]:  # Corrected the spelling from 'dataloders' to 'dataloaders'
                 # get the inputs
                 inputs, labels = data
 
@@ -57,7 +57,7 @@ def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
                     optimizer.step()
 
                 # statistics
-                running_loss += loss.data[0]
+                running_loss += loss.item()  # Changed to .item() for correct loss extraction
                 running_corrects += torch.sum(preds == labels.data).to(torch.float32)
 
             epoch_loss = running_loss / dataset_sizes[phase]
@@ -82,38 +82,38 @@ def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
 
 if __name__ == '__main__':
 
-    # data_transform, pay attention that the input of Normalize() is Tensor and the input of RandomResizedCrop() or RandomHorizontalFlip() is PIL Image
+    # Data transformations
     data_transforms = {
         'train': transforms.Compose([
-            transforms.RandomSizedCrop(224),
+            transforms.RandomResizedCrop(224),  # Changed from RandomSizedCrop to RandomResizedCrop
             transforms.RandomHorizontalFlip(),
             transforms.ToTensor(),
             transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
         ]),
         'val': transforms.Compose([
-            transforms.Scale(256),
+            transforms.Resize(256),  # Changed from Scale to Resize
             transforms.CenterCrop(224),
             transforms.ToTensor(),
             transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
         ]),
     }
 
-    # your image data file
+    # Your image data file
     data_dir = '/data'
     image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                               data_transforms[x]) for x in ['train', 'val']}
-    # wrap your data and label into Tensor
-    dataloders = {x: torch.utils.data.DataLoader(image_datasets[x],
+    # Wrap your data and label into Tensor
+    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x],
                                                  batch_size=4,
                                                  shuffle=True,
                                                  num_workers=4) for x in ['train', 'val']}
 
     dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
 
-    # use gpu or not
+    # Use GPU or not
     use_gpu = torch.cuda.is_available()
 
-    # get model and replace the original fc layer with your fc layer
+    # Get model and replace the original fc layer with your fc layer
     model_ft = models.resnet18(pretrained=True)
     num_ftrs = model_ft.fc.in_features
     model_ft.fc = nn.Linear(num_ftrs, 2)
@@ -121,7 +121,7 @@ def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
     if use_gpu:
         model_ft = model_ft.cuda()
 
-    # define loss function
+    # Define loss function
     criterion = nn.CrossEntropyLoss()
 
     # Observe that all parameters are being optimized