Struct AdamW 

pub struct AdamW<T: Float + Send + Default> { /* private fields */ }

AdamW optimizer implementation Adam with decoupled weight decay for better regularization

Implementations

impl<T: Float + Send + Default> AdamW<T>

pub fn new(learning_rate: T) -> Self

Create a new AdamW optimizer with default parameters

pub fn with_beta1(self, beta1: T) -> Self

Set beta1 parameter (momentum coefficient)

pub fn with_beta2(self, beta2: T) -> Self

Set beta2 parameter (variance coefficient)

pub fn with_epsilon(self, epsilon: T) -> Self

Set epsilon for numerical stability

pub fn with_weight_decay(self, weight_decay: T) -> Self

Set weight decay (decoupled from gradient-based updates)

pub fn with_error_function( self, error_function: Box<dyn ErrorFunction<T>>, ) -> Self

Set error function

Trait Implementations

impl<T: Float + Send + Default> TrainingAlgorithm<T> for AdamW<T>

fn train_epoch( &mut self, network: &mut Network<T>, data: &TrainingData<T>, ) -> Result<T, TrainingError>

Train for one epoch

fn calculate_error(&self, network: &Network<T>, data: &TrainingData<T>) -> T

Calculate the current error

fn count_bit_fails( &self, network: &Network<T>, data: &TrainingData<T>, bit_fail_limit: T, ) -> usize

Count bit fails

fn save_state(&self) -> TrainingState<T>

Save training state

fn restore_state(&mut self, state: TrainingState<T>)

Restore training state

fn set_callback(&mut self, callback: TrainingCallback<T>)

Set a callback function

fn call_callback( &mut self, epoch: usize, network: &Network<T>, data: &TrainingData<T>, ) -> bool

Call the callback if set