Points
900
Description
This is a classic machine learning problem. Thankfully, I’ve done a bit of machine learning already.
We came across this article and copy much of the template code from there. I wrote my code in a Jupyter notebook.
The only problem that I encountered was that of the learning rate. The example’s learning rate was way too small, and resulted in no progress. In order to solve, I set the learning rate to 1e7 and decayed it by 0.99 per iteration. Although these hyperparameters can probably be tuned better, these result in a solve in about 15 seconds with 100% accuracy.
Sample training
Error
# I'm pretty sure I borrowed this function from somewhere, but cannot remember
# the source to cite them properly.
def hash_hamming_distance(h1, h2):
s1 = str(h1)
s2 = str(h2)
print(s1)
print(s2)
return sum(map(lambda x: 0 if x[0] == x[1] else 1, zip(s1, s2)))
def is_similar_img(path1, path2):
image1 = Image.open(path1)
image2 = Image.open(path2)
dist = hash_hamming_distance(phash(image1), phash(image2))
return dist <= 2
assert is_similar_img("./img/trixi.png", "./hacked-image.png")
My full solution can be found here.