Week 4: Donkey

Hello my fellow insignificant quantum wave functions oscillating fruitlessly in the great abyss that we call our universe! 

Soooo. How’s it going? Genuinely, how are you doing? (not that you can respond, but you know, just take a second after reading this sentence, to just take a deep breath, a nice long inhale and exhale, ’cause this blog might be slightly more difficult to bear with than previous ones, but trust me, it’s interesting)

What? Are you telling me to stop stalling and to get to it? Jeez, stop rushing me, I’m just checking in on my dear readers’ wellbeings and definitely not trying to stack up on the word count for this blog.

Alright, so last week I mentioned that I had very possibly created a machine learning model that was better at detecting asteroids than the machine learning models that the very smart computer scientists had developed. And this week, I tried to prove it! 

But before I show you the cool data that proves my model is slightly, potentially, maybe, possibly better, you might be wondering — “So how did you create this model? Did you make it from scratch? Did you steal it from another person’s github without abiding by the copyright licenses they had on their repo? Did you pull it out of your donkey (replacing all instances of donkey with a popular synonym will provide for a much more colorful read for the rest of this blog)?” 

No. I’ve done most of those things before, but not for this project. 

Getting a machine learning model to perform the task at hand is kinda like getting a perfect sculpture of a donkey. EfficientNet (the big, smart, crazy-good model that the big, smart, crazy-good computer scientists created) is like a hand-sculpted marble sculpture of a horse (that looks really nice and could pass for a donkey, but is ultimately is not a donkey). And this marble sculpture you or I can just order, already completed and pre-packaged. 

On the other hand, there’s this library called Keras, which basically provides components of a machine learning model that you can assemble by yourself to create your ideal model — or to continue the analogy, lego blocks that you and I can order and assemble together into a donkey. Of course, lego blocks are no donkey, but after enough work and polishing, perhaps the lego blocks can be built and shaped into something which looks a lot like a donkey, more like a donkey than a marble horse.

So, for this project, I used the lego blocks and constructed numerous different models to see which one would look the most like a donkey. You might be wondering (you’re probably not actually wondering) just how many models did you create and test? 40+ models, and multiple configurations for each model. And it’s not just like testing these bad-boys is easy work, each test takes about a day to run. You might be doing a little math in your head — it’s Week 4, 7×4=28 — 28 < 40+ — something ain’t adding up! (keep this down low but I might have done a little testing work before the official start of the senior project)

Alright, anyways, so after a whole lot of building and testing, here’s the most donkey-resembling model I found. 

Looks pretty simple but just know that it took a lot of work to get here (just a special note of gratitude to my mentor and my fellow mentees if they ever read this — they deserve quite a lot of credit). 

Dang this blog got way too long way too fast! Guess the juicy data that you were waiting for to prove that my model is more donkey-looking than EfficientNet will just have to wait till next week!

Until then, donkey out!