Here we show the trickery as it evolves. The most important aspects to pay important to are the final predictions (bottom left) and the loss history (bottom right). While the results might initially seem quite drastic, and it might seem logical to completely distrust any results from neural networks that is probably a bit exaggerated. Since we had access to the complete network and could train as we wanted the results are significantly more successful than they would be on a blackbox network (which is the case for most public image APIs for example). The more important take away message is that the networks trained, even if they have been trained on millions of images, still do not really'understand' the images.