I trained a convolutional neural net to recognize latin alphabet characters, and numbers (0-9, A-Z, a-z), then let it predict the category of all characters of a single font, then extracted that into 62 vectors, each describing how the computer sees those pictures of characters. I fed those 62 vectors into a self organizing map.

This is the map I got: map

The CNN had the accuracy of 98-99% (maybe overfitted but idc), and outputs were 128 dimensional vectors, the first layer after flattening. Flattened -> layer -> output layer w 62 categories

Overall I’m pretty happy with how it turned out, here I marked some groups I noticed. Also I think its cool how it separated the curvy from sharp-angled symbols into 2 “main” groups.

This is one of my first machine learning projects, and the first one w SOM’s and CNN’s. Any feedback is appreciated 🙂

Also, I noticed how every time the map is generated, the outlying datapoints are closer to nodes. Specially corners. Does anyone know why would that be? Maybe the data is “spherical”, in it’s 128 dimensions… That recurs every time the map is generated (using random weights initialization)

I did this only to see what I’ll get. I explained that to my mom, saying how it’s interesting but useless. She said “You’ve made a tool, you just have to figure out its use” hahaha. So yea, I have a tool, but I have no idea on what/how to use it. Could you, people of reddit, think of something that would benefit from CNN-SOM hybrids?