Rust NN

Homework 1 for deep learning course @ FRI written in 🦀 (and python for plotting but let us ignore that).

What

It's a minimal sequential nn implementation in Rust. It only includes support for single input - single output layers. Basically nn.Sequential from Pytorch but strictly worse. It is minimal in the sense that I am lazy so I only implemented stuff necessary for this one specific homework.

It also includes SGD and Adam optimizers, exponential LR scheduler etc.

Why

I was bored and wanted to learn Rust by actually implementing something and not just reading the book. I already did something similar in Java, so I basically just rewrote the necessary parts of that project in Rust (literally rewrite in rust 🚀🤓🦀).

Results

Adam doing its magic.

How to run

1. Download CIFAR-10 bytes from here in ./data and extract the batches in there.

2. Move to ./nn directory and run cargo run --release. The release flag is absolutely crucial if you don't want to have a run time in the range of days. Optionally, capture the terminal output and save it into a file for further analysis.

TODO

• I need to get better at Rust (will probably happen).
• Optimize matmul (will probably not happen here).
• Add layers, optimizers, datasets etc. (will probably never happen).