miniature: a toy deep learning library written in Rust
A miniature is a toy deep learning library written in Rust.
The miniature is:
- implemented for author's Rust practice.
- designed as simple as possible.
The miniature is NOT:
- supporting CUDA.
- optimized for computational costs.
- a product-ready library.
features
- define-by-run style API
- easy as Python libraries (e.g. TensorFlow, PyTorch, nnabla)
- easy to add more features (e.g. layers, optimizers)
run MNIST
Download MNIST dataset for the first time.
$ python scripts/download_mnist.py
Then, run.
$ cargo run --release
example
use miniature::functions as F;
use miniature::graph::backward;
use miniature::optimizer as S;
use miniature::parametric_functions as PF;
use miniature::variable::Variable;
use std::rc::Rc;
use std::cell::RefCell;
fn main() {
// define layers
let fc1 = PF::linear(28 * 28, 256);
let fc2 = PF::linear(256, 256);
let fc3 = PF::linear(256, 10);
// define optimizer
let mut optim = S::adam(0.001, (0.9, 0.999), 1e-8);
optim.set_params(fc1.get_params());
optim.set_params(fc2.get_params());
optim.set_params(fc3.get_params());
let x = Rc::new(RefCell::new(Variable::rand(vec![32, 28 * 28])));
let t = Rc::new(RefCell::new(Variable::rand(vec![32])));
// forward
let h1 = F::relu(fc1.call(x));
let h2 = F::relu(fc2.call(h1));
let y = fc3.call(h2);
// loss
let loss = F::cross_entropy_loss(y, F::onehot(t, 10));
// update
optim.zero_grad();
backward(loss);
optim.update();
}