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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 17: trying to wrap my head around autoencoders Day 13: BPTT, and debugging why a model isn't training is hard Day 11: learning about learning rates Day 10: Training an RNN to count to three Day 9: Generating a lot of nonsense with an RNN Day 8: Start with something that works Day 5: drawing lots of faces with sketch-rnn Day 3: an infinitely tall fridge Day 2: Rails associations & dragging divs around Day 1: a confusing Rails error message I'm doing another Recurse Center batch!
Day 18: an answer to an autoencoder question
Julia Evans · 2020-12-03 · via Rc-2020 on Julia Evans

I’m going to keep this one short because I want to get back to coding!

One of the questions I had in the last post was:

The Encoder class in the translation tutorial outputs 2 vectors, an output and a hidden vector. Which one is the encoding, the output or the hidden vector? (or both???)

I’ve been struggling a bit to find out the answers to questions like this – there are 10 million blogs about deep learning, but somehow I feel like they often don’t answer my questions. My best strategy so far has to been to search the PyTorch forums, and that’s how I found the answer to this one! I searched for something like “autoencoder” and found this answer

The answer to that question is that the hidden vector is the encoding, and you just throw out the output.

That answer helped me a LOT and I managed to get a simple autoencoder to work once I had the answer! It’s always surprising to me every single time how helpful it is to focus and articulate the questions I have.

I still don’t know what the relu is for, but it’s nice to have the answer to at least one question.