R.A.I.L: A Rust Artificial Intelligence Library

RAIL is designed to be a library for easily creating and training Neural Networks, akin to the Keras API. It aims to be fast and easy to use.

Dependencies

RAIL depends on arrayfire-rust, so before using RAIL make sure you have arrayfire installed.

A Simple XOR Problem

Solving the XOR Problem with Mold is super easy! Simply add the crate to your Cargo.toml: toml rail = { git = "https://github.com/nlsnightmare/rail" } Then add this to your code ```rust use rail::model::Model; use rail::layers::dense::Dense; use rail::layers::activations::Activation;

pub fn main() { let mut model = Model::new() .learningrate(0.01) .inputsize(2) .layer(Dense::new(2).activation(Activation::Tanh)) .layer(Dense::new(1).activation(Activation::Tanh)) .build(true) .unwrap();

let tranining_data = vec![
    (vec![0., 0.], vec![0.]),
    (vec![0., 1.], vec![1.]),
    (vec![1., 0.], vec![1.]),
    (vec![1., 1.], vec![0.]),
];

// Train with a batch of 2 for 4000 epochs
model.train(&tranining_data, 2, 4000);

println!("[0, 0] -> {}", model.predict(vec![0., 0.])[0]); // should be close to 0
println!("[0, 1] -> {}", model.predict(vec![0., 1.])[0]); // should be close to 1
println!("[1, 0] -> {}", model.predict(vec![1., 0.])[0]); // should be close to 1
println!("[1, 1] -> {}", model.predict(vec![1., 1.])[0]); // should be close to 0

} ```

Plans

As of now, RAIL is in a very early state, and under heavy development. The API will change a lot.
So far, only Dense (aka fully connected) layers are supported, and batched SGD is the only way of training the network. However, there are plans to support: - Convolutional Layers - RNN Cells - LSTM Cells - Genetic Crossover - ADAM optimizer - More Activation functions - More Error functions - Documentation