diff --git a/ANN_Training.py b/ANN_Training.py
index 6ce796aec14430cc5705cd558ee52724a5421e7d..3171fb39f74115bb9aeee3e698470b98a63c940a 100644
--- a/ANN_Training.py
+++ b/ANN_Training.py
@@ -3,13 +3,14 @@
 @author: Laura C. Kühle, Soraya Terrab (sorayaterrab)
 
 TODO: Improve 'epoch_training()'
-TODO: Add ANN testing from Soraya -> Done
 TODO: Add ANN classification from Soraya
-TODO: Improve naming of training data/model (maybe different folders?)
-TODO: Adjust input file naming to fit training data -> Done
-TODO: Change code to add model directory if not existing -> Done
-TODO: Remove unnecessary comments -> Done
-TODO: Add option to set plot directory -> Done
+TODO: Improve naming of training data/model (maybe different folders?; split training data leter;
+    total amount + percentage?)
+TODO: Give option to compare multiple models
+TODO: Improve "valid_loss" calculation
+TODO: Use sklearn for classification
+TODO: Fix difference between accuracies (stems from rounding; choose higher value instead)
+TODO: Add more evaluation measures (AUROC, ROC, F1, training accuracy, etc.)
 
 """
 import numpy as np
@@ -114,10 +115,11 @@ class ModelTrainer(object):
         self._model.eval()
 
         x_test, y_test = self._training_data['test']
+        print(self._model(x_test.float()))
         model_output = torch.round(self._model(x_test.float()))
         # acc = np.sum(model_output.numpy() == y_test.numpy())
-        test_accuracy = (model_output == y_test).float().mean()
-        print(test_accuracy)
+        # test_accuracy = (model_output == y_test).float().mean()
+        # print(test_accuracy)
         # print(model_output.nelement())
         # accuracy1 = torch.sum(torch.eq(model_output, y_test)).item()  # /model_output.nelement()
         # print(test_accuracy, accuracy1/model_output.nelement())
diff --git a/DG_Approximation.py b/DG_Approximation.py
index 732977c5ff9677fd7b240b09cc45c2f659dfef76..d0d1bd5eab44b294cc998bb6f8096c21dc10cd4b 100644
--- a/DG_Approximation.py
+++ b/DG_Approximation.py
@@ -6,10 +6,10 @@ Plotter:
 TODO: Double-check everything!
 TODO: Replace loops with list comprehension if feasible
 TODO: Write documentation for all methods (important)
-TODO: Contemplate how to make shock tubes comparable
 TODO: Check whether 'projection' is always a np.array()
 TODO: Check whether all instance variables sensible
 TODO: Use cfl_number for updating, not just time
+TODO: Adjust code to allow classes for all equations (Burger, linear advection, 1D Euler)
 
 """
 import os
diff --git a/Plotting.py b/Plotting.py
index aaac2b5078eafcaaa5f930040e5507af6e807a7e..43a16632d2358d1a57a3c3024937e7da4b3ea1be 100644
--- a/Plotting.py
+++ b/Plotting.py
@@ -3,7 +3,6 @@
 @author: Laura C. Kühle
 
 TODO: Give option to select plotting color
-TODO: Fix bug for ticks in bar plot -> Done
 
 """
 import numpy as np
diff --git a/Troubled_Cell_Detector.py b/Troubled_Cell_Detector.py
index 028add527c6b4a0270497eee419e1210b46a8d32..b73e1f9d99fc1edc1ffff841c5a2ae14236d0ec7 100644
--- a/Troubled_Cell_Detector.py
+++ b/Troubled_Cell_Detector.py
@@ -2,6 +2,9 @@
 """
 @author: Laura C. Kühle, Soraya Terrab (sorayaterrab)
 
+TODO: Adjust TCs for wavelet detectors (sliding window over all cells instead of every second)
+TODO: Adjust Boxplot approach (adjacent cells, outer fence, etc.)
+
 """
 import numpy as np
 import matplotlib.pyplot as plt