Struct LAMB 

pub struct LAMB<A: Float + ScalarOperand + Debug> { /* private fields */ }

LAMB (Layer-wise Adaptive Moments) optimizer

LAMB is designed for large batch optimization. It extends AdamW with layer-wise adaptive learning rates, making it particularly effective for training large models with high batch sizes.

Formula: m_t = beta1 * m_{t-1} + (1 - beta1) * g_t v_t = beta2 * v_{t-1} + (1 - beta2) * g_t^2 m_hat_t = m_t / (1 - beta1^t) v_hat_t = v_t / (1 - beta2^t) r1 = ||theta_t|| g’ = m_hat_t / (sqrt(v_hat_t) + epsilon) + lambda * theta_t r2 = ||g’|| ratio = r1/r2 if r1 > 0 and r2 > 0, else 1.0 theta_t = theta_{t-1} - lr * ratio * g’

Examples

use scirs2_core::ndarray::Array1;
use optirs_core::optimizers::{LAMB, Optimizer};

// Initialize parameters and gradients
let params = Array1::zeros(5);
let gradients = Array1::from_vec(vec![0.1, 0.2, -0.3, 0.0, 0.5]);

// Create a LAMB optimizer with default hyperparameters
let mut optimizer = LAMB::new(0.001);

// Update parameters
let new_params = optimizer.step(&params, &gradients).unwrap();

Implementations

impl<A: Float + ScalarOperand + Debug + Send + Sync> LAMB<A>

pub fn new(learning_rate: A) -> Self

Creates a new LAMB optimizer with the given learning rate and default settings

Arguments

• learning_rate - The learning rate for parameter updates

pub fn new_with_config( learning_rate: A, beta1: A, beta2: A, epsilon: A, weight_decay: A, bias_correction: bool, ) -> Self

Creates a new LAMB optimizer with the full configuration

Arguments

• learning_rate - The learning rate for parameter updates
• beta1 - Exponential decay rate for the first moment estimates (default: 0.9)
• beta2 - Exponential decay rate for the second moment estimates (default: 0.999)
• epsilon - Small constant for numerical stability (default: 1e-6)
• weight_decay - Weight decay factor for L2 regularization (default: 0.0)
• bias_correction - Whether to use bias correction (default: true)

pub fn set_beta1(&mut self, beta1: A) -> &mut Self

Sets the beta1 parameter

pub fn get_beta1(&self) -> A

Gets the beta1 parameter

pub fn set_beta2(&mut self, beta2: A) -> &mut Self

Sets the beta2 parameter

pub fn get_beta2(&self) -> A

Gets the beta2 parameter

pub fn set_epsilon(&mut self, epsilon: A) -> &mut Self

Sets the epsilon parameter

pub fn get_epsilon(&self) -> A

Gets the epsilon parameter

pub fn set_weight_decay(&mut self, weight_decay: A) -> &mut Self

Sets the weight decay parameter

pub fn get_weight_decay(&self) -> A

Gets the weight decay parameter

pub fn learning_rate(&self) -> A

Gets the current learning rate

pub fn set_lr(&mut self, lr: A)

Sets the learning rate

pub fn reset(&mut self)

Resets the internal state of the optimizer

Trait Implementations

impl<A: Clone + Float + ScalarOperand + Debug> Clone for LAMB<A>

fn clone(&self) -> LAMB<A>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<A: Debug + Float + ScalarOperand + Debug> Debug for LAMB<A>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<A, D> Optimizer<A, D> for LAMB<A>

where A: Float + ScalarOperand + Debug + Send + Sync, D: Dimension,

fn step( &mut self, params: &Array<A, D>, gradients: &Array<A, D>, ) -> Result<Array<A, D>>

Updates parameters using the given gradients

fn get_learning_rate(&self) -> A

Gets the current learning rate

fn set_learning_rate(&mut self, learning_rate: A)

Sets a new learning rate

fn step_list( &mut self, params_list: &[&Array<A, D>], gradients_list: &[&Array<A, D>], ) -> Result<Vec<Array<A, D>>>

Updates multiple parameter arrays at once