You can also play around by passing the filter_indices parameter different indices corresponding to different output classes from the ImageNet dataset. You could also pass it a list of two integers, corresponding to two different output classes. This basically lets your neural network imagine visual combinations of two separate output classes by simultaneously visualizing the activations pertaining to both output classes. These can at times turn out to be very interesting, so let both your imaginations run wild! It is noteworthy that Google's DeepDream leverages similar concepts, showing how overexcited activation maps can be superimposed over input images to generate artistic patterns and images. The intricacy of these patterns is at times remarkable and awe-inspiring:
