diff --git a/ginka/train_vae.py b/ginka/train_vae.py
index b529c71..7ec75e4 100644
--- a/ginka/train_vae.py
+++ b/ginka/train_vae.py
@@ -54,7 +54,7 @@ from shared.image import matrix_to_image_cv
 
 BATCH_SIZE = 128
 LATENT_DIM = 48
-KL_BETA = 0.05
+KL_BETA = 0.1
 
 device = torch.device(
     "cuda:1" if torch.cuda.is_available()
@@ -145,21 +145,22 @@ def train():
         )
         
         # 验证集
-        with torch.no_grad():
-            val_loss_total = torch.Tensor([0]).to(device)
-            for batch in tqdm(dataloader_val, desc="Validating generator.", leave=False, disable=disable_tqdm):
-                target_map = batch["target_map"].to(device)
+        # with torch.no_grad():
+        #     val_loss_total = torch.Tensor([0]).to(device)
+        #     for batch in tqdm(dataloader_val, desc="Validating generator.", leave=False, disable=disable_tqdm):
+        #         target_map = batch["target_map"].to(device)
                 
-                fake_logits, mu, logvar = vae(target_map, 1 - gt_prob)
+        #         fake_logits, mu, logvar = vae(target_map, 1 - gt_prob)
 
-                loss, reco_loss, kl_loss = criterion.vae_loss(fake_logits, target_map, mu, logvar, KL_BETA)
-                val_loss_total += loss.detach()
+        #         loss, reco_loss, kl_loss = criterion.vae_loss(fake_logits, target_map, mu, logvar, KL_BETA)
+        #         val_loss_total += loss.detach()
         
-        avg_loss_val = val_loss_total.item() / len(dataloader_val)
-        if avg_loss_val < 0.5 and gt_prob > 0:
+        # avg_loss_val = val_loss_total.item() / len(dataloader_val)
+        # 先使用训练集的损失值，因为过拟合比较严重，后续再想办法
+        if avg_loss < 0.5 and gt_prob > 0:
             gt_prob -= 0.01
         
-        scheduler_ginka.step(avg_loss_val)
+        scheduler_ginka.step(avg_loss)
 
         # 每若干轮输出一次图片，并保存检查点
         if (epoch + 1) % args.checkpoint == 0: