Struct ScheduledOptimizer

pub struct ScheduledOptimizer<O: Optimizer, S: LearningRateScheduler> { /* private fields */ }

Wrapper that combines an optimizer with a learning rate scheduler

Implementations

impl<O: Optimizer, S: LearningRateScheduler> ScheduledOptimizer<O, S>

pub fn new(optimizer: O, scheduler: S, base_lr: f64) -> Self

pub fn step(&mut self)

Step the scheduler (should be called at the end of each epoch)

pub fn step_with_val_loss(&mut self, val_loss: f64)

Step with validation loss (for ReduceLROnPlateau)

pub fn get_current_lr(&self) -> f64

Get the current learning rate

pub fn get_current_epoch(&self) -> usize

Get the current epoch

pub fn reset(&mut self)

Reset both optimizer and scheduler

pub fn scheduler_name(&self) -> &'static str

Get the scheduler name for logging

impl<O: Optimizer> ScheduledOptimizer<O, ConstantLR>

Helper functions to create common optimizer-scheduler combinations

pub fn constant(optimizer: O, lr: f64) -> Self

impl<O: Optimizer> ScheduledOptimizer<O, StepLR>

pub fn step_lr(optimizer: O, lr: f64, step_size: usize, gamma: f64) -> Self

impl<O: Optimizer> ScheduledOptimizer<O, ExponentialLR>

pub fn exponential(optimizer: O, lr: f64, gamma: f64) -> Self

impl<O: Optimizer> ScheduledOptimizer<O, CosineAnnealingLR>

pub fn cosine_annealing( optimizer: O, lr: f64, t_max: usize, eta_min: f64, ) -> Self

impl<O: Optimizer> ScheduledOptimizer<O, PolynomialLR>

pub fn polynomial( optimizer: O, lr: f64, total_iters: usize, power: f64, end_lr: f64, ) -> Self

impl<O: Optimizer> ScheduledOptimizer<O, CyclicalLR>

pub fn cyclical( optimizer: O, base_lr: f64, max_lr: f64, step_size: usize, ) -> Self

pub fn cyclical_triangular2( optimizer: O, base_lr: f64, max_lr: f64, step_size: usize, ) -> Self

pub fn cyclical_exp_range( optimizer: O, base_lr: f64, max_lr: f64, step_size: usize, gamma: f64, ) -> Self

impl<O: Optimizer> ScheduledOptimizer<O, OneCycleLR>

pub fn one_cycle(optimizer: O, max_lr: f64, total_steps: usize) -> Self

Trait Implementations

impl<O: Optimizer, S: LearningRateScheduler> Optimizer for ScheduledOptimizer<O, S>

fn update( &mut self, param_id: &str, param: &mut Array2<f64>, gradient: &Array2<f64>, )

fn reset(&mut self)

fn set_learning_rate(&mut self, lr: f64)

Set the learning rate dynamically (for compatibility with schedulers)

fn get_learning_rate(&self) -> f64

Get the current learning rate