Correct indentation

GATANeuralNetTraining.m

@@ -25,28 +25,28 @@ function GATANeuralNetTraining()
                                     source_classes_cell_array);
     disp('Done!')
 
-%%
-% Now we train the classifier
-disp('Training a Shallow Neural Network model for classification...')
-[nn_model, tr_x, tr_y, training_Pct_Err] = neural_network_classificator_training(training_dataset, ...
-                                                    desired_features_indices, classes_names, weight_classes, ...
-                                                    hiddenLayerSize, trainFcn, performFcn);
-disp('Done!')
-
-%%
-disp('Using NN for predicting classes...')
-[nn_classificated_dataset] = use_nn_classificator(nn_model, ... 
-    testing_dataset, desired_features_indices);  
+    %%
+    % Now we train the classifier
+    disp('Training a Shallow Neural Network model for classification...')
+    [nn_model, tr_x, tr_y, training_Pct_Err] = neural_network_classificator_training(training_dataset, ...
+                                                        desired_features_indices, classes_names, weight_classes, ...
+                                                        hiddenLayerSize, trainFcn, performFcn);
+    disp('Done!')
+
+    %%
+    disp('Using NN for predicting classes...')
+    [nn_classificated_dataset] = use_nn_classificator(nn_model, ... 
+        testing_dataset, desired_features_indices);  
  
-disp('Done!')
+    disp('Done!')
 
-%% Now we evaluate the system performance
-disp('Computing performance statistics...')
-[precission, recall, f1_score, overall_precission, ...
+    %% Now we evaluate the system performance
+    disp('Computing performance statistics...')
+    [precission, recall, f1_score, overall_precission, ...
     overall_recall, overall_f1_score, IoU] = ... 
     evaluating_segmentation_results(nn_classificated_dataset);
-disp('Done!')
+    disp('Done!')
 
-%% Finally we export the model weights to csv file to use with GATA in ROS
-save_model_to_csv(nn_model, neural_net_filename);
+    %% Finally we export the model weights to csv file to use with GATA in ROS
+    save_model_to_csv(nn_model, neural_net_filename);
 end