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4 hours ago
1
Minimal automatic-differentiation engine written in Rust.
Running the demo trains a tiny Multi-Layer Perceptron to learn the XOR function and writes a rendered computation graph of a single Perceptron to graph.html.
use nanograd::scalar::{Scalar, func};
let x = Scalar::new_grad(2.0);
let y = Scalar::new_grad(-1.0);
// z = relu(x * y + 3)
let z = func::relu(x.clone() * y.clone() + 3.0);
z.backward();
println!("z = {}", z.get_value());
println!("dz/dx = {:?}", x.get_grad());
println!("dz/dy = {:?}", y.get_grad());
- Each Scalar stores value, optional gradient, and an Edge describing the operation that produced it.
- Operator overloads (+, *, -, ...) and helpers in scalar::func build a directed acyclic graph of Scalars while caching the local derivative for every edge.
- backward() starts at an output node and recursively propagates gradients along the graph, accumulating them into the leaf nodes created with Scalar::new_grad.
- The graph can be visualised with plot::dump_graph, which emits a self-contained D3.js HTML file.
