diff --git a/_paramters.py b/_paramters.py
index c5d4785..36e2cc5 100644
--- a/_paramters.py
+++ b/_paramters.py
@@ -54,7 +54,7 @@ main_arg_parser.add_argument("--train_outpath", type=str, default="output", help
 main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
 # FIXME: Stochastic weight Avaraging is not good, maybe its my implementation?
 main_arg_parser.add_argument("--train_sto_weight_avg", type=strtobool, default=False, help="")
-main_arg_parser.add_argument("--train_weight_decay", type=float, default=1e-7, help="")
+main_arg_parser.add_argument("--train_weight_decay", type=float, default=1e-8, help="")
 main_arg_parser.add_argument("--train_opt_reset_interval", type=int, default=0, help="")
 main_arg_parser.add_argument("--train_epochs", type=int, default=51, help="")
 main_arg_parser.add_argument("--train_batch_size", type=int, default=300, help="")
diff --git a/multi_run.py b/multi_run.py
index b3cf957..207e681 100644
--- a/multi_run.py
+++ b/multi_run.py
@@ -25,19 +25,26 @@ if __name__ == '__main__':
         for model in ['CC', 'BCMC', 'BCC', 'RCC']:
             arg_dict.update(model_type=model)
             raw_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.0,
-                            data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0)
+                            data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0,
+                            data_stretch=False)
             all_conf = dict(data_speed_factor=0.7, data_speed_ratio=0.2, data_mask_ratio=0.2,
-                              data_noise_ratio=0.4, data_shift_ratio=0.4, data_loudness_ratio=0.4)
+                            data_noise_ratio=0.4, data_shift_ratio=0.4, data_loudness_ratio=0.4,
+                            data_stretch=True)
             speed_conf = dict(data_speed_factor=0.7, data_speed_ratio=0.2, data_mask_ratio=0.0,
-                            data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0)
+                              data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0,
+                              data_stretch=True)
             mask_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.2,
-                             data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0)
+                             data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0,
+                             data_stretch=True)
             noise_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.0,
-                              data_noise_ratio=0.4, data_shift_ratio=0.0, data_loudness_ratio=0.0)
+                              data_noise_ratio=0.4, data_shift_ratio=0.0, data_loudness_ratio=0.0,
+                              data_stretch=True)
             shift_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.0,
-                              data_noise_ratio=0.0, data_shift_ratio=0.4, data_loudness_ratio=0.0)
+                              data_noise_ratio=0.0, data_shift_ratio=0.4, data_loudness_ratio=0.0,
+                              data_stretch=True)
             loudness_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.0,
-                                  data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.4)
+                                 data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.4,
+                                 data_stretch=True)
 
             for dicts in [raw_conf, all_conf, speed_conf, mask_conf,noise_conf, shift_conf, loudness_conf]: