diff --git a/ginka/generator/loss.py b/ginka/generator/loss.py
index 1220cbb..d3f0cfb 100644
--- a/ginka/generator/loss.py
+++ b/ginka/generator/loss.py
@@ -408,4 +408,4 @@ class RNNGinkaLoss:
     
     def rnn_loss(self, fake, target):
         target = F.one_hot(target, num_classes=32).float()
-        return F.cross_entropy(fake, target)
+        return F.cross_entropy(fake, target, label_smoothing=0.05)
diff --git a/ginka/train_rnn.py b/ginka/train_rnn.py
index 8807c08..9bd7a84 100644
--- a/ginka/train_rnn.py
+++ b/ginka/train_rnn.py
@@ -83,7 +83,7 @@ def train():
     dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
     dataloader_val = DataLoader(dataset_val, batch_size=BATCH_SIZE)
     
-    optimizer_ginka = optim.Adam(ginka_rnn.parameters(), lr=1e-3)
+    optimizer_ginka = optim.AdamW(ginka_rnn.parameters(), lr=1e-4, weight_decay=1e-4)
     scheduler_ginka = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer_ginka, T_0=10, T_mult=2)
 
     criterion = RNNGinkaLoss()
@@ -119,6 +119,7 @@ def train():
             loss = criterion.rnn_loss(fake_logits, target_map)
             
             loss.backward()
+            torch.nn.utils.clip_grad_norm_(ginka_rnn.parameters(), max_norm=1.0)
             optimizer_ginka.step()
             loss_total_ginka += loss.detach()
             
@@ -133,9 +134,9 @@ def train():
             #         f"LR: {optimizer_ginka.param_groups[0]['lr']:.6f}"
             #     )
                 
-        avg_loss_ginka = loss_total_ginka.item() / iters
+        avg_loss_ginka = loss_total_ginka.item() / len(dataloader)
         tqdm.write(
-            f"[Epoch {epoch} {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] " +
+            f"[Epoch {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] " +
             f"E: {epoch + 1} | Loss: {avg_loss_ginka:.6f} | " +
             f"LR: {optimizer_ginka.param_groups[0]['lr']:.6f}"
         )
@@ -170,7 +171,7 @@ def train():
                     
             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"[Validate {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] E: {epoch + 1} | " +
                 f"Loss: {avg_loss_val:.6f}"
             )