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https://github.com/madlittlemods/zig-ocr-neural-network
A from scratch neural network implementation in Zig, trained against the MNIST dataset to recognize handwritten digits.
https://github.com/madlittlemods/zig-ocr-neural-network
deep-learning machine-learning mnist mnist-handwriting-recognition neural-network ocr zig
Last synced: about 1 month ago
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A from scratch neural network implementation in Zig, trained against the MNIST dataset to recognize handwritten digits.
- Host: GitHub
- URL: https://github.com/madlittlemods/zig-ocr-neural-network
- Owner: MadLittleMods
- Created: 2023-10-05T04:39:25.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-13T15:38:24.000Z (about 1 year ago)
- Last Synced: 2024-11-01T13:05:43.706Z (3 months ago)
- Topics: deep-learning, machine-learning, mnist, mnist-handwriting-recognition, neural-network, ocr, zig
- Language: Zig
- Homepage:
- Size: 113 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Basic OCR example using a neural network against the MNIST dataset
> [!TIP]
>
> If you are looking for a neural network library that you can include in your project,
> I've created a library based on this project ->
> https://github.com/MadLittleMods/zig-neural-networks
A from scratch neural network implementation in Zig, trained against the MNIST dataset
to recognize handwritten digits.
A lot of the phrasing and concepts are taken from the resources linked in the
[*developer notes*](./dev-notes.md). I'm just trying to piece together all of those
resources into something that works and is understandable to me as I learn. Major kudos
to Sebastian Lague, 3Blue1Brown, The Independent Code (Omar Aflak), and Samson Zhang for
their excellent resources. And a special shoutout to Hans Musgrave
([@hmusgrave](https://github.com/hmusgrave)) for the immense amount of help to get my
head around these concepts as I got stuck through this process.
## Setup
Download and extract the MNIST dataset from http://yann.lecun.com/exdb/mnist/ to a
directory called `data/` in the root of this project. Here is a copy-paste command
you can run:
```sh
# Make a data/ directory
mkdir data/ &&
cd data/ &&
# Download the MNIST dataset
curl -O http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz &&
curl -O http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz &&
curl -O http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz &&
curl -O http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz &&
# Unzip the files
gunzip *.gz
```
## Building and running
Tested with Zig 0.11.0
With the OCR example, on my machine, I can complete 1 epoch of training in ~1 minute
which gets to 94% accuracy and creeps to 97% after a few more epochs (60k training
images, 10k test images):
```sh
$ zig build run-mnist_ocr
debug: Created normalized data points. Training on 60000 data points, testing on 10000
debug: Here is what the first training data point looks like:
┌──────────┐
│ Label: 5 │
┌────────────────────────────────────────────────────────┐
│ │
│ │
│ │
│ │
│ │
│ ░░░░░░░░▒▒▓▓▓▓░░▓▓████▒▒ │
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│ ░░████████████████████▒▒▒▒▒▒░░░░ │
│ ░░██████████████▓▓████ │
│ ▒▒▓▓▒▒██████░░ ░░▓▓ │
│ ░░░░▓▓██▒▒ │
│ ▓▓██▓▓░░ │
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│ ██████▒▒ │
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│ ▓▓████████▓▓▓▓░░ │
│ │
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└────────────────────────────────────────────────────────┘
debug: epoch 0 batch 0 3s -> cost 331.64265899045563, accuracy with 100 test points 0.11
debug: epoch 0 batch 5 4s -> cost 242.16033395427667, accuracy with 100 test points 0.56
debug: epoch 0 batch 10 5s -> cost 155.62913461977217, accuracy with 100 test points 0.7
debug: epoch 0 batch 15 5s -> cost 118.45908401769115, accuracy with 100 test points 0.75
[...]
```
### Other examples
#### Simple animal example
This is a small dataset that I made up to test the neural network. There are only 2
arbitrary features (x and y) where the labeled data points (fish and goat) occupy
distinct parts of the graph. Since there are only 2 input features (which means 2
dimensions), we can easily graph the neural network's decision/classification boundary.
It's a good way to visualize the neural network and see how it evolves while training.
This example produces an image called `simple_xy_animal_graph.ppm` every 1,000 epochs
showing the decision/classification boundary.
```sh
$ zig build run-simple_xy_animal_sample
```
#### Barebones XOR example
There is also a barebones XOR example which just trains a neural network to act like a
XOR ("exclusive or") gate.
```sh
$ zig build run-xor
```
## Testing
```sh
$ zig build test --summary all
```
## Dev notes
See the [*developer notes*](./dev-notes.md) for more information.