diff --git a/ginka/train_rnn.py b/ginka/train_rnn.py
index 933d143..8badc99 100644
--- a/ginka/train_rnn.py
+++ b/ginka/train_rnn.py
@@ -8,7 +8,7 @@ import cv2
 from torch_geometric.loader import DataLoader
 from tqdm import tqdm
 from .common.cond import ConditionEncoder
-from .generator.rnn import GinkaRNN
+from .generator.rnn import GinkaRNNModel
 from .dataset import GinkaRNNDataset
 from .generator.loss import RNNGinkaLoss
 from shared.image import matrix_to_image_cv
@@ -76,15 +76,14 @@ def train():
     
     args = parse_arguments()
     
-    cond_inj = ConditionEncoder().to(device)
-    ginka_rnn = GinkaRNN().to(device)
+    ginka_rnn = GinkaRNNModel(device).to(device)
     
     dataset = GinkaRNNDataset(args.train, device)
     dataset_val = GinkaRNNDataset(args.validate, device)
     dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
     dataloader_val = DataLoader(dataset_val, batch_size=BATCH_SIZE)
     
-    optimizer_ginka = optim.Adam(list(ginka_rnn.parameters()) + list(cond_inj.parameters()), lr=1e-3, betas=(0.0, 0.9))
+    optimizer_ginka = optim.Adam(ginka_rnn.parameters(), lr=1e-3)
     scheduler_ginka = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer_ginka, T_0=10, T_mult=2)
 
     criterion = RNNGinkaLoss()
@@ -112,16 +111,12 @@ def train():
         iters = 0
         
         for batch in tqdm(dataloader, leave=False, desc="Epoch Progress", disable=disable_tqdm):
-            tag_cond = batch["tag_cond"].to(device)
             val_cond = batch["val_cond"].to(device)
             target_map = batch["target_map"].to(device)
             
-            B, D = val_cond.shape
-            stage = torch.Tensor([0]).expand(B, 1).to(device)
-            cond_vec = cond_inj(tag_cond, val_cond, stage)
-            fake = ginka_rnn(target_map, cond_vec)
+            fake_logits, fake_map = ginka_rnn(val_cond, target_map, False)
             
-            loss = criterion.rnn_loss(fake, target_map)
+            loss = criterion.rnn_loss(fake_logits, target_map)
             
             loss.backward()
             optimizer_ginka.step()
@@ -159,23 +154,25 @@ def train():
             with torch.no_grad():
                 idx = 0
                 for batch in tqdm(dataloader_val, desc="Validating generator.", leave=False, disable=disable_tqdm):
-                    tag_cond = batch["tag_cond"].to(device)
                     val_cond = batch["val_cond"].to(device)
                     target_map = batch["target_map"].to(device)
                     
                     B, T = val_cond.shape
-                    stage = torch.Tensor([0]).expand(B, 1).to(device)
-                    cond_vec = cond_inj(tag_cond, val_cond, stage)
-                    fake = ginka_rnn(target_map, cond_vec)
+                    fake_logits, fake_map = ginka_rnn(val_cond, target_map, False)
                     
-                    val_loss_total += criterion.rnn_loss(fake, target_map).detach()
+                    val_loss_total += criterion.rnn_loss(fake_logits, target_map).detach()
                     
-                    B, T, C = fake.shape
-                    fake_map = torch.argmax(fake, dim=-1).reshape(B, 13, 13).cpu().numpy()
+                    fake_map = fake_map.cpu().numpy()
                     fake_img = matrix_to_image_cv(fake_map[0], tile_dict)
                     cv2.imwrite(f"result/ginka_rnn_img/{idx}.png", fake_img)
                     
                     idx += 1
+                    
+            avg_loss_val = val_loss_total.item() / len(dataloader_val)
+            tqdm.write(
+                f"[Validate {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] E: {epoch} | " +
+                f"Loss: {avg_loss_val:.6f}"
+            )
                 
     print("Train ended.")
     torch.save({