Diving Deeper Into Deep Dream: Different Distortions

The moment you get Google Research's Deep Dream project set up, you can make it do incredible things. If you want to generate trippy zooms with lots of dogs and eyes, the code Google provides works more or less out-of-the-box.

For reference, here is a gist for a .py file containing all the code from the notebook. This is meant to be run through an interactive interpreter, e.g. by providing the -i flag at runtime or, in iPython, starting a new notebook and then executing e.g. "run demo.py".

And, here is a gist for a similar but extended piece of code which takes an input file on the command line and performs the iterative process described at the very end of the researchers' original iPython notebook. Both these files are almost entirely composed of code from that notebook; they're just provided as references, so we can have some well-established starting points from which to branch out.

Setting Up Deep Dream, Google Research's Hallucinatory Work of Genius

Google Research wrote recently about a technique they call "Inceptionism", which needs to be seen to be believed. Click through and check out their pictures. A full gallery of official images can be found here. The techniques used to generate these sorts of images were described in broad strokes in this blog post, but the level of detail stopped just short of what someone wanting to replicate their results might have wanted.

That is, until they published their source code.

In this repo (which was created just this morning, as of this post's writing) is an IPython notebook which contains everything necessary to duplicate the results you see in their blog post. That's so cool that it's honestly a little hard to believe. The dependencies are detailed in the repo. There are surprisingly few of them, and they're all surprisingly easy to get set up. Let me walk you through what I had to do on my Debian Stretch system to get everything up and running.