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Rc-2020 on Julia Evans

Day 57: Trying to set up GitHub Actions Day 56: A little WebAssembly Day 53: a little nginx, IPv6, and wireguard Day 52: testing how many Firecracker VMs I can run Day 51: Fixed my logging and made a couple of puzzles Day 50: Building some tarballs for puzzles, and trying to make a kernel boot faster Day 49: making the VMs boot faster Day 48: Another Go program, and a little vim configuration Day 47: Using device mapper to manage Firecracker images Day 46: debugging an iptables problem Day 44: Building my VMs with Docker Day 43: Building VM images Day 42: Writing a Go program to manage Firecracker VMs Day 41: Trying to understand what a bridge is Day 40: screen flickering & a talk about containers Day 39: Customizing gotty's terminal Day 38: Modifying gotty to serve many different terminal applications at once Day 37: A new laptop and a little Vue Day 35: Launching my VMs more reliably Day 34: Learning about qemu Day 33: pairing is magic and beautiful git diffs Day 32: A Rails model that doesn't use the database with ActiveHash Day 24: a short talk about blogging myths, and a debugging tip Day 23: a little Rails testing Day 22: getting OAuth to work in Rails Day 21: wrangling systemd & setting up git deploys to a VM Day 19: Clustering faces (poorly) using an autoencoder Day 20: trying to figure out how Google Cloud IAM works Day 18: an answer to an autoencoder question Day 17: trying to wrap my head around autoencoders
Day 10: Training an RNN to count to three
Julia Evans · 2020-11-21 · via Rc-2020 on Julia Evans

Yesterday I was trying to train an RNN to generate English that sounds kind of like Shakespeare. That was not working, so today I instead tried to do something MUCH simpler: train an RNN to generate sequences like

0 1 2 0 1 2 0 1 2 0 1 2

and slightly more complicated sequences like

0 1 2 1 0 1 2 1 0 1 2 1 0 1 2 1 0

I used (I think) the exact same RNN that I couldn’t get to work yesterday to generate English by training it on Shakespeare, so it was cool to see that I could at least use it for this much simpler task (memorize short sequences of numbers).

the jupyter notebook

It’s late so I won’t explain all the code in this blog post, but here’s the PyTorch code I wrote to train the RNN to count to three.

In the gist there are a few experiments with different sequence lengths, like (unsurprisingly) it takes longer to train it to memorize a sequence of length 20 than a sequence of length 5.

simplifying is nice

I’m super happy that I got an RNN to do something that I actually understand! I feel pretty hopeful that on Monday I’ll be able to go back to the character RNN problem of trying to get the RNN to generate English words now that I have this simpler thing working.