diff --git a/models/__pycache__/neuralnet.cpython-38.pyc b/models/__pycache__/neuralnet.cpython-38.pyc
index cbe052be930dd43036f0f60dbcc2069a4a430de5..682fd872d624cdd604379504894e89096240c244 100644
Binary files a/models/__pycache__/neuralnet.cpython-38.pyc and b/models/__pycache__/neuralnet.cpython-38.pyc differ
diff --git a/models/neuralnet.py b/models/neuralnet.py
index e1f32908d6c4a966334f8b6b06ca5a05b504842f..6a07c4e91d9b1656529cdb7b8d56ba06b33e0dd3 100644
--- a/models/neuralnet.py
+++ b/models/neuralnet.py
@@ -153,7 +153,7 @@ class FNN_HyperModel(kt.HyperModel):
         self.model = model
         return model
 
-    def fit(self, hp, model, x, y, validation_data, epochs, **kwargs):
+    def fit(self, hp, model, x, y, validation_data=None,  **kwargs):
         def make_ds(x, y):
             ds_x = tf.data.Dataset.from_tensor_slices(x)\
                     .batch(self.batch_size, drop_remainder=True)
@@ -169,14 +169,12 @@ class FNN_HyperModel(kt.HyperModel):
             val_ds = make_ds(*validation_data)
             history = model.fit(train_ds,
                                 validation_data=val_ds,
-                                epochs=epochs,
                                 verbose=self.verbose,
                                 **kwargs
                                )
         else:
             val_ds = None
             history = model.fit(x, y,
-                                epochs=epochs,
                                 verbose=self.verbose,
                                 batch_size=self.batch_size,
                                 **kwargs
diff --git a/modules/__pycache__/utils.cpython-38.pyc b/modules/__pycache__/utils.cpython-38.pyc
index 0ae60611424d313f7060707ed9e97db8ad855b88..b33d8e0b67828c2e0c10591ec40526fbe33e3837 100644
Binary files a/modules/__pycache__/utils.cpython-38.pyc and b/modules/__pycache__/utils.cpython-38.pyc differ
diff --git a/modules/utils.py b/modules/utils.py
index 295d2b4252b33af02095966eb455cec0a1aa1db5..f633da551c64fde12f6361f387325d610e1617df 100644
--- a/modules/utils.py
+++ b/modules/utils.py
@@ -224,11 +224,17 @@ def model_training(mdl_str, x_train, y_train, marker,
             lstm_mdl = tuner.load_model(is_training=True)
             lstm_hypermodel.verbose = True
             callbacks = tuner.get_callbacks(epochs=extra_train)
+            fit_kwargs = {'epochs': extra_train,
+                          'callbacks': callbacks,
+                         }
+            if validation_data is not None:
+                fit_kwargs['validation_split'] = None
+            else:
+                fit_kwargs['validation_split'] = 0.2
+
             history = lstm_hypermodel.fit(
                 None, lstm_mdl, x_train, y_train,
-                validation_data=validation_data, epochs=extra_train,
-                callbacks=callbacks
-            )
+                **fit_kwargs,)
             tuner.save_weights_to_path()
 
         tuner.load_model(is_training=False)
@@ -267,11 +273,17 @@ def model_training(mdl_str, x_train, y_train, marker,
             hypermodel.verbose = True
             callbacks = tuner.get_callbacks(epochs=extra_train)
 
+            fit_kwargs = {'epochs': extra_train,
+                          'callbacks': callbacks,
+                         }
+            if validation_data is not None:
+                fit_kwargs['validation_split'] = None
+            else:
+                fit_kwargs['validation_split'] = 0.2
+
             history = hypermodel.fit(
                 None, mdl, x_train, y_train,
-                validation_data=validation_data, epochs=extra_train,
-                callbacks=callbacks,
-            )
+                **fit_kwargs,)
             tuner.save_weights_to_path()
 
         tuner.load_model(is_training=False)
diff --git a/regress_rr.py b/regress_rr.py
index 506396864b80d539537d0d7945c655f788aa6cfe..b4dce1ce8808107c32430e1bb6f352d81fc1b979 100644
--- a/regress_rr.py
+++ b/regress_rr.py
@@ -1424,7 +1424,7 @@ def arg_parser():
                        )
     parser.add_argument("-s", '--subject', type=int,
                         default=1,
-                        choices=list(range(1,N_SUBJECT_MAX))+[-1],
+                        choices=list(range(1,N_SUBJECT_MAX+1))+[-1],
                        )
     parser.add_argument("-f", '--feature_method', type=str,
                         default='minirocket',