simple.rs

//this is a simple example that uses a neural network with
//   2 input layer neurons,
//   3 hidden layer neurons
//   and 1 output layer neuron
//to approximate a XOR function

use nn::dataset;
use nn::defaults::DefaultRng as Rng;
use nn::nn::NeuralNetwork;

fn main() {
    let mut nn = NeuralNetwork::new(&[2, 3, 3, 1], &mut Rng::new());

    nn.set_err_thres(1e-8);
    nn.set_momentum(0.01);

    nn.train(
        100000,
        &dataset!(
              [0., 0.] => [0.],
              [0., 1.] => [1.],
              [1., 0.] => [1.],
              [1., 1.] => [0.],
              [0., 0.] => [0.],
              [0., 1.] => [1.],
              [1., 0.] => [1.],
              [1., 1.] => [0.],
        ),
    );

    println!("{:?}", nn.predict(vec![0., 0.]));
    println!("{:?}", nn.predict(vec![0., 1.]));
    println!("{:?}", nn.predict(vec![1., 0.]));
    println!("{:?}", nn.predict(vec![1., 1.]));
}