Try it yourself: https://nn-viz.ameo.design
The goal of this project was to understand neural networks better by building them from the ground up. I wanted to be able to see dynamically how choosing different network architectures + training parameters affects network performance and how well networks can various functions.
It allows users to define a neural network infrastructure by adding hidden layers, picking neuron counts + activation function types, and setting training parameters like learning rate. The network then learns one of a variety of selectable target functions from which examples are randomly sampled to train it.
The "response" of the network over the entire range of possible inputs is then plotted as a 3D surface along with the target function to show how well the network has learned.
The neural network implementation itself is built in Rust and compiled to WebAssembly using Wasm SIMD to accelerate training.
The training takes place on a dedicated thread by using a web worker and the excellent Comlink library to communicate between the main/render thread and the training thread.
All the charts + visualizations are created using the excellent echarts library.
The UI is created using react-control-panel which is a React port of the excellent control-panel library for easy GUI creation.
You'll need Rust nightly with WebAssembly support. You can install Rust via easily via rustup: https://rustup.rs/
Then, add the latest nightly toolchain + switch to it:
rustup default nightly
Add WebAssembly support:
rustup target add wasm32-unknown-unknown
This project uses the just command runner to simplify many tasks. Install it with:
cargo install just.
You'll also need wasm-bindgen:
cargo install wasm-bindgen --version=0.2.74
You'll need to install the wasm-opt tool from binaryen. You can download the executable from the Releases section on Github or build it yourself with CMake.
Then, you'll need tools for the web stack. I use yarn for node package management, and you can either update the Justfile to change yarn to npm or install yarn 1.0 from here: https://classic.yarnpkg.com/en/docs/install/
That should be all you need! To start the webpack dev server for hot-reloading and development, execute just run in the project root.
To create a release build, execute just build in the project root. That will produce a fully functional static website in the dist directory.
This is a list of some of the resources that I made use of while learning about neural networks and building this project:
- https://machinelearningmastery.com/implement-backpropagation-algorithm-scratch-python/
- https://mlfromscratch.com/neural-networks-explained/#/
- MIT 6.S094: Recurrent Neural Networks for Steering Through Time <- Really helped me break through the wall of understanding some of the core concepts behind neural networks