diff --git a/data/data_loader.py b/data/data_loader.py
index 3176855..6a1f352 100644
--- a/data/data_loader.py
+++ b/data/data_loader.py
@@ -231,7 +231,7 @@ class DataLoaderMultiAspect():
                 target_wh = min(self.aspects, key=lambda aspects:abs(aspects[0]/aspects[1] - image_aspect))
                 if not self.has_scanned:
                     if width * height < target_wh[0] * target_wh[1]:
-                        undersized_images.append(f" *** {pathname} with size: {width},{height} is smaller than target size: {target_wh}, consider using larger images")
+                        undersized_images.append(f" {pathname}, size: {width},{height}, target size: {target_wh}")
 
                 image_train_item = ImageTrainItem(image=None, caption=caption, target_wh=target_wh, pathname=pathname, flip_p=flip_p)
 
@@ -251,7 +251,7 @@ class DataLoaderMultiAspect():
                 with open(underized_log_path, "w") as undersized_images_file:
                     undersized_images_file.write(f" The following images are smaller than the target size, consider removing or sourcing a larger copy:")
                     for undersized_image in undersized_images:
-                        undersized_images_file.write(undersized_image)
+                        undersized_images_file.write(f"{undersized_image}\n")
         
         return decorated_image_train_items
 
diff --git a/train.py b/train.py
index 0883363..8e5f1f6 100644
--- a/train.py
+++ b/train.py
@@ -675,8 +675,6 @@ def main(args):
     logging.info(f" {Fore.GREEN}batch_size: {Style.RESET_ALL}{Fore.LIGHTGREEN_EX}{args.batch_size}{Style.RESET_ALL}")
     logging.info(f" {Fore.GREEN}epoch_len: {Fore.LIGHTGREEN_EX}{epoch_len}{Style.RESET_ALL}")
 
-
-    #scaler = torch.cuda.amp.GradScaler()
     scaler = GradScaler(
         enabled=args.amp,
         init_scale=2**17.5,
@@ -686,13 +684,8 @@ def main(args):
     )
     logging.info(f" Grad scaler enabled: {scaler.is_enabled()} (amp mode)")
 
-
     epoch_pbar = tqdm(range(args.max_epochs), position=0, leave=True)
     epoch_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Epochs{Style.RESET_ALL}")
-
-    # steps_pbar = tqdm(range(epoch_len), position=1, leave=True)
-    # steps_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Steps{Style.RESET_ALL}")
-
     epoch_times = []
 
     global global_step
@@ -781,11 +774,8 @@ def main(args):
                                     param.grad *= grad_scale
 
                 if ((global_step + 1) % args.grad_accum == 0) or (step == epoch_len - 1):
-                    # if args.amp:
                     scaler.step(optimizer)
                     scaler.update()
-                    # else:
-                    #     optimizer.step()
                     optimizer.zero_grad(set_to_none=True)
 
                 lr_scheduler.step()