diff --git a/lenet5.py b/lenet5.py
index 2845c2f..7ed8728 100644
--- a/lenet5.py
+++ b/lenet5.py
@@ -138,6 +138,16 @@ def train_lenet5(model):
                     validation_data=validation_generator, validation_steps=validation_steps,
                     shuffle=True, callbacks=[tensorboard])
 
+def print_netout(i, netout, maximum_value):
+    """
+    Prints the netout of LeNet-5.
+    """
+    print("[%.4d] : [%.0f %.0f %.0f %.0f %.0f %.0f %.0f %.0f %.0f %.0f] = >>> %d <<<"
+          % (i,
+             netout[0], netout[1], netout[2], netout[3], netout[4],
+             netout[5], netout[6], netout[7], netout[8], netout[9],
+             maximum_value))
+
 def main():
     """
     Defined starting point of source code.
@@ -160,10 +170,9 @@ def main():
     print('Number of test images:', test['features'].shape[0])
 
     # Step 4:
-    # Display some images
-    # display_image(train, 0)
-    # display_image(train, 1)
-    # display_image(train, 2)
+    # Display three images of training dataset
+    for i in range(3):
+        display_image(train, i)
 
     # Step 5:
     # Plot information about the training data
@@ -198,28 +207,24 @@ def main():
     # Train and save the LeNet-5 model
     # train_lenet5(model)
     # model.save('lenet5.h5')
-
     model = load_model('lenet5.h5')
 
     # Step 9:
-    # Print results
+    # PEvaluate with test data and print results
     score = model.evaluate(test['features'], to_categorical(test['labels']))
     print('Test loss:', score[0])
     print('Test accuracy:', score[1])
 
     # Step 10:
     # Execute model and predict results
-
-    # display_image(test, 0)
-    # display_image(test, 1)
-    # display_image(test, 2)
-
     netouts = model.predict(test['features'])
+    maximum_values = netouts.argmax(axis=1)
 
-    for i, netout in enumerate(netouts):
-        print("%d: %.0f %.0f %.0f %.0f %.0f %.0f %.0f %.0f %.0f %.0f"
-              % (i, netout[0], netout[1], netout[2], netout[3], netout[4],
-                    netout[5], netout[6], netout[7], netout[8], netout[9]))
+    # Step 11:
+    # Investigate ten predictions (randomly selected)
+    for i in np.random.choice(np.arange(0, len(test['labels'])), size=(10,)):
+        print_netout(i, netouts[i], maximum_values[i])
+        display_image(test, i)
 
 if __name__ == "__main__":
     main()