diff --git a/.gitignore b/.gitignore
index ef009a2..935f522 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,7 +3,8 @@ data/
 etc/
 result/
 save_model/
-gen_data/
+vae_gen_data/
+gan_gen_data/
 tensorboard/
 
 ### Notebook ###
diff --git a/run_timegan.py b/run_timegan.py
index 3f4c3a2..f49434a 100644
--- a/run_timegan.py
+++ b/run_timegan.py
@@ -63,8 +63,7 @@
     print('>>>> TRAINING COMPLETE!')
 if args.is_generate:
     gen_data=timegan_generator(model, args.num_generation, args)
-    np.save(os.getcwd()+"\\save_data\\",gen_data)
-
+    np.save(f'./gan_gen_data/gen_data',gen_data)
 
 
 
diff --git a/run_vrae.py b/run_vrae.py
index b0f3fa2..fdf7203 100644
--- a/run_vrae.py
+++ b/run_vrae.py
@@ -99,13 +99,13 @@
     
     # save original data
     train_org = pd.DataFrame(TRAIN_DF if args.undo == True else TRAIN_SCALED, columns= cols)
-    train_org.to_csv(f'./gen_data/train/original_{args.scale_type}_un_{args.undo}.csv')
-    print('>> SAVED TRAIN ORIGINAL Data!! (Loc: gen_data)')
+    train_org.to_csv(f'./vae_gen_data/train/original_{args.scale_type}_un_{args.undo}.csv')
+    print('>> SAVED TRAIN ORIGINAL Data!! (Loc: vae_gen_data)')
     
     # save reconstructed data
     train_gen = pd.DataFrame(train_recon, columns= cols)
-    train_gen.to_csv(f'./gen_data/train/VRAE_{args.scale_type}_un_{args.undo}_hidden_{args.hidden_layer_depth}_win_{args.sequence_length}_ep_{args.n_epochs}.csv')
-    print('>> SAVED TRAIN RECONSTRUCTED Data!! (Loc: gen_data)')
+    train_gen.to_csv(f'./vae_gen_data/train/VRAE_{args.scale_type}_un_{args.undo}_hidden_{args.hidden_layer_depth}_win_{args.sequence_length}_ep_{args.n_epochs}.csv')
+    print('>> SAVED TRAIN RECONSTRUCTED Data!! (Loc: vae_gen_data)')
     
 # TEST dataset reconstruction
 if args.is_generate_test:
@@ -132,13 +132,13 @@
     
     # save original data
     test_org = pd.DataFrame(TRAIN_DF if args.undo == True else TRAIN_SCALED, columns= cols)
-    test_org.to_csv(f'./gen_data/test/original_{args.scale_type}_un_{args.undo}.csv')
-    print('>> SAVED TEST ORIGINAL Data!! (Loc: gen_data)')
+    test_org.to_csv(f'./vae_gen_data/test/original_{args.scale_type}_un_{args.undo}.csv')
+    print('>> SAVED TEST ORIGINAL Data!! (Loc: vae_gen_data)')
     
     # save reconstructed data
     test_gen = pd.DataFrame(test_recon, columns= cols)
-    test_gen.to_csv(f'./gen_data/test/VRAE_{args.scale_type}_un_{args.undo}_hidden_{args.hidden_layer_depth}_win_{args.sequence_length}_ep_{args.n_epochs}.csv')
-    print('>> SAVED TEST RECONSTRUCTED Data!! (Loc: gen_data)')
+    test_gen.to_csv(f'./vae_gen_data/test/VRAE_{args.scale_type}_un_{args.undo}_hidden_{args.hidden_layer_depth}_win_{args.sequence_length}_ep_{args.n_epochs}.csv')
+    print('>> SAVED TEST RECONSTRUCTED Data!! (Loc: vae_gen_data)')
 
 # IF Both TRAIN and TEST data reconstruction is conducted
 if args.is_generate_train and args.is_generate_test:
diff --git a/vrae_experiment.sh b/vrae_experiment.sh
new file mode 100644
index 0000000..cd66856
--- /dev/null
+++ b/vrae_experiment.sh
@@ -0,0 +1,22 @@
+#!/bin/sh
+
+BATCH_SIZE=8
+EPOCH=10
+LR=5e-5
+ACCUMULATION_STEP=1
+
+# N_ENC=6
+# N_DEC=6
+
+# run distilBART-6-3
+python ./src/kobart/main.py\
+    --batch-size=${BATCH_SIZE}\
+    --lr=${LR}\
+    --epoch=${EPOCH}\
+    --gradient-accumulation-step=${ACCUMULATION_STEP}\
+    --amp\
+    --distributed
+#     --distill\
+    # --n_enc=${N_ENC}\
+    # --n_dec=${N_DEC}\
+