4 releases (2 breaking)
0.3.1 | Mar 13, 2023 |
---|---|
0.3.0 | Mar 13, 2023 |
0.2.0 | Nov 17, 2021 |
0.1.0 | Nov 12, 2021 |
#415 in Machine learning
13KB
246 lines
vinyana@0.3.1
vinyana - stands for mind in pali language.
Goal
The goal of this project is to create a neural network library that is easy to use and understand in rust.
Usage
use std::path::Path;
use rand::prelude::*;
use vinyana::{activation::ActivationType, NeuralNetwork};
fn main() {
let mut nn = NeuralNetwork::new(vec![2, 2, 1]);
nn.set_learning_rate(0.01); // default is 0.01 but you can change it
nn.set_activation(ActivationType::Tanh); // default is Sigmoid but you can change it
// We will train this network with 4 scenarios of XOR problem
let scenarios = vec![
(vec![1.0, 1.0], vec![0.0f32]),
(vec![0.0, 1.0], vec![1.0]),
(vec![1.0, 0.0], vec![1.0]),
(vec![0.0, 0.0], vec![0.0]),
];
let mut rng = thread_rng();
for _ in 0..500000 {
let random = rng.gen_range(0..4) as usize;
let (train_data, target_data) = scenarios.get(random).unwrap();
// we will pick a random scenario from the dataset and feed it to the network with the expected target
nn.train(train_data.clone(), target_data.clone())
}
let result = nn.predict(vec![1.0, 0.0]);
println!("Result: {:?}", result);
// we can store our trained model and play with it later
nn.save(Path::new("xor_model.nn")).unwrap();
}
// Load your model from file
let nn = NeuralNetwork::load(Path::new("xor_model.nn")).unwrap();
let result = nn.predict(vec![1.0, 1.0]);
println!("{:?}", result);
Dependencies
~2.5–3.5MB
~67K SLoC