diff --git a/train_network.py b/train_network.py
index 6eefdb2b..128690fb 100644
--- a/train_network.py
+++ b/train_network.py
@@ -981,20 +981,19 @@ class NetworkTrainer:
                     logs = self.generate_step_logs(args, current_loss, avr_loss, lr_scheduler, keys_scaled, mean_norm, maximum_norm)
                     accelerator.log(logs, step=global_step)
                 
-                if args.validation_every_n_step is not None:        
-                    if global_step % (args.validation_every_n_step) == 0:                
-                        if len(val_dataloader) > 0:
+                if len(val_dataloader) > 0:
+                    if  (args.validation_every_n_step is not None and global_step % args.validation_every_n_step == 0) or step == len(train_dataloader) - 1 or global_step >= args.max_train_steps:
                             print(f"\nValidating バリデーション処理...")
                             total_loss = 0.0
                             with torch.no_grad():
-                                validation_steps = min(args.validation_batches, len(val_dataloader)) if args.validation_batches is not None else len(val_dataloader)
+                                validation_steps = min(args.max_validation_steps, len(val_dataloader)) if args.max_validation_steps is not None else len(val_dataloader)
                                 for val_step in tqdm(range(validation_steps), desc='Validation Steps'): 
                                     is_train = False
                                     batch = next(cyclic_val_dataloader)
                                     loss = self.process_val_batch(batch, is_train, tokenizers, text_encoders, unet, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args)
                                     total_loss += loss.detach().item()
-                                current_loss = total_loss / args.validation_batches
-                                val_loss_recorder.add(epoch=epoch, step=global_step, loss=current_loss)   
+                                current_loss = total_loss / validation_steps
+                                val_loss_recorder.add(epoch=epoch, step=step, loss=current_loss)   
 
                             if args.logging_dir is not None:
                                 logs = {"loss/current_val_loss": current_loss}
@@ -1009,25 +1008,6 @@ class NetworkTrainer:
             if args.logging_dir is not None:
                 logs = {"loss/epoch_average": loss_recorder.moving_average}                
                 accelerator.log(logs, step=epoch + 1)
-
-            if args.validation_every_n_step is None:               
-                if len(val_dataloader) > 0:
-                    print(f"\nValidating バリデーション処理...")
-                    total_loss = 0.0
-                    with torch.no_grad():
-                        validation_steps = min(args.validation_batches, len(val_dataloader)) if args.validation_batches is not None else len(val_dataloader)
-                        for val_step in tqdm(range(validation_steps), desc='Validation Steps'):
-                            is_train = False
-                            batch = next(cyclic_val_dataloader)
-                            loss = self.process_val_batch(batch, is_train, tokenizers, text_encoders, unet, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args)
-                            total_loss += loss.detach().item()
-                        current_loss = total_loss / args.validation_batches   
-                        val_loss_recorder.add(epoch=epoch, step=global_step, loss=current_loss)
-
-                    if args.logging_dir is not None:
-                        avr_loss: float = val_loss_recorder.moving_average
-                        logs = {"loss/epoch_val_average": avr_loss}
-                        accelerator.log(logs, step=epoch + 1)
                             
             accelerator.wait_for_everyone()
 
@@ -1184,14 +1164,14 @@ def setup_parser() -> argparse.ArgumentParser:
         "--validation_every_n_step",
         type=int,
         default=None,
-        help="Number of steps for counting validation loss. By default, validation per epoch is performed"
+        help="Number of train steps for counting validation loss. By default, validation per train epoch is performed"
     )    
     parser.add_argument(
-        "--validation_batches",
+        "--max_validation_steps",
         type=int,
         default=None,
-        help="Number of val steps for counting validation loss. By default, validation for all val_dataset is performed"
-    )            
+        help="Number of max validation steps for counting validation loss. By default, validation will run entire validation dataset"
+    )    
     return parser