diff --git a/.DS_Store b/.DS_Store
index 3576c0f..a1ebc19 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/Neural_Network_Analysis.py b/Neural_Network_Analysis.py
index e5a55e5..63ac9a8 100644
--- a/Neural_Network_Analysis.py
+++ b/Neural_Network_Analysis.py
@@ -289,7 +289,7 @@ def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions,
 # the average of networks trained om them
 # Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs 
 # Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
-def inverse_session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
+def inverse_session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, log_to_csv=True, batch_size=64, epochs=30):
     session_training_results = []
     for i in range(nr_sessions):
 
@@ -316,8 +316,9 @@ def inverse_session_cross_validation(model_name:str, X, y, session_lengths, nr_s
         train(model, X_train_session, y_train_session, verbose=1, batch_size=batch_size, epochs=epochs)
         test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=0)
         session_training_results.append(test_acc)
-        #if log_to_csv:
-            #prediction_csv_logger(X_test_session, y_test_session, model_name, model, i)
+        if log_to_csv:
+            custom_path = '/{}_train_session{}_log.csv'
+            prediction_csv_logger(X_test_session, y_test_session, model_name, model, i, custom_path)
         del model
         K.clear_session()
         #print('Session', i, 'as test data gives accuracy:', test_acc)
@@ -329,9 +330,12 @@ def inverse_session_cross_validation(model_name:str, X, y, session_lengths, nr_s
 # Takes in test data and logs input data and the prediction from a model
 # Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs 
 # Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
-def prediction_csv_logger(X, y, model_name, model, session_nr):
+def prediction_csv_logger(X, y, model_name, model, session_nr, custom_path=None):
     
     csv_path = str(Path.cwd()) + '/logs/{}/{}_session{}_log.csv'.format(model_name, model_name, session_nr+1)
+    if custom_path:
+        path = str(Path.cwd()) + '/logs/{}' + custom_path
+        csv_path = path.format(model_name, model_name, session_nr+1)
 
     layerOutput = model.predict(X, verbose=0)
 
@@ -502,16 +506,20 @@ if __name__ == "__main__":
     #'''
     # ----- Inverse cross-validation ------
     # Trained on one session, tested on three
-    average_GRU = inverse_session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS, 
+    average_GRU = inverse_session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS,
+                                                                        log_to_csv=True, 
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)
     average_LSTM = inverse_session_cross_validation('LSTM', X, y, session_lengths, nr_sessions=NR_SESSIONS,
+                                                                        log_to_csv=True,
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)
     average_FFN = inverse_session_cross_validation('FFN', X, y, session_lengths, nr_sessions=NR_SESSIONS,
+                                                                        log_to_csv=True,
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)
     average_CNN = inverse_session_cross_validation('CNN_1D', X, y, session_lengths, nr_sessions=NR_SESSIONS,
+                                                                        log_to_csv=True,
                                                                         batch_size=BATCH_SIZE, 
                                                                         epochs=EPOCHS)