neuronika 0.2.0

Tensors and dynamic neural networks.
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use super::super::{L2, SGD};
use super::{ExponentialLR, LambdaLR, MultiStepLR, MultiplicativeLR, StepLR};

#[test]
fn lambda_lr() {
    const EPOCHS: usize = 5;
    let optim = SGD::new(Vec::new(), 1., L2::new(0.1));
    let scheduler = LambdaLR::new(&optim, |epoch| epoch as f32);

    scheduler.set_current_epoch(5);
    assert_eq!(scheduler.get_current_epoch(), 5);
    scheduler.set_current_epoch(0);
    assert_eq!(scheduler.get_current_epoch(), 0);

    for epoch in 0..EPOCHS {
        if epoch > 0 {
            assert!((scheduler.get_current_lr() - epoch as f32).abs() <= f32::EPSILON);
        }
        optim.zero_grad();
        optim.step();
        assert_eq!(scheduler.get_current_epoch(), epoch);
        scheduler.step();
        scheduler.print_lr();
    }
    assert!((scheduler.get_last_lr() - 4_f32).abs() <= f32::EPSILON);
}

#[test]
fn multiplicative_lr() {
    const EPOCHS: usize = 5;
    let optim = SGD::new(Vec::new(), 1., L2::new(0.1));
    let scheduler = MultiplicativeLR::new(&optim, |epoch| epoch as f32);

    scheduler.set_current_epoch(5);
    assert_eq!(scheduler.get_current_epoch(), 5);
    scheduler.set_current_epoch(0);
    assert_eq!(scheduler.get_current_epoch(), 0);

    for epoch in 0..EPOCHS {
        optim.zero_grad();
        assert_eq!(scheduler.get_current_epoch(), epoch);
        optim.step();
        scheduler.step();
        scheduler.print_lr();
    }

    assert!((scheduler.get_last_lr() - 24_f32).abs() <= f32::EPSILON);
    assert!((scheduler.get_current_lr() - 120_f32).abs() <= f32::EPSILON);
    // Should be 5!.
}

#[test]
fn step_lr() {
    const EPOCHS: usize = 5;
    let optim = SGD::new(Vec::new(), 1., L2::new(0.1));
    let scheduler = StepLR::new(&optim, 1, 2.);

    scheduler.set_current_epoch(5);
    assert_eq!(scheduler.get_current_epoch(), 5);
    scheduler.set_current_epoch(0);
    assert_eq!(scheduler.get_current_epoch(), 0);

    for epoch in 0..EPOCHS {
        assert!((scheduler.get_current_lr() - 2_f32.powi(epoch as i32)).abs() <= f32::EPSILON);
        optim.zero_grad();
        optim.step();
        assert_eq!(scheduler.get_current_epoch(), epoch);
        scheduler.step();
        scheduler.print_lr();
    }
    assert!((scheduler.get_last_lr() - 2_f32.powi(4)).abs() <= f32::EPSILON);
}

#[test]
fn multistep_lr() {
    const EPOCHS: usize = 5;
    let optim = SGD::new(Vec::new(), 1., L2::new(0.1));
    let scheduler = MultiStepLR::new(&optim, [1, 2, 3, 4], 2.);

    scheduler.set_current_epoch(5);
    assert_eq!(scheduler.get_current_epoch(), 5);
    scheduler.set_current_epoch(0);
    assert_eq!(scheduler.get_current_epoch(), 0);

    for epoch in 0..EPOCHS {
        optim.zero_grad();
        optim.step();
        assert_eq!(scheduler.get_current_epoch(), epoch);
        scheduler.step();
        scheduler.print_lr();
    }
    assert!((scheduler.get_last_lr() - 16_f32).abs() <= f32::EPSILON);
    assert!((scheduler.get_current_lr() - 16_f32).abs() <= f32::EPSILON); // Should be 2^4.
}

#[test]
fn exponential_lr() {
    const EPOCHS: usize = 5;
    let optim = SGD::new(Vec::new(), 1., L2::new(0.1));
    let scheduler = ExponentialLR::new(&optim, 5.);

    scheduler.set_current_epoch(5);
    assert_eq!(scheduler.get_current_epoch(), 5);
    scheduler.set_current_epoch(0);
    assert_eq!(scheduler.get_current_epoch(), 0);

    for epoch in 0..EPOCHS {
        optim.zero_grad();
        assert_eq!(scheduler.get_current_epoch(), epoch);
        optim.step();
        scheduler.step();
        scheduler.print_lr();
    }
    assert!((scheduler.get_last_lr() - 5_f32.powi(4)).abs() <= f32::EPSILON);
    assert!((scheduler.get_current_lr() - 5_f32.powi(5)).abs() <= f32::EPSILON);
    // Should be 5^5.
}