Struct GradientCentralization 

pub struct GradientCentralization { /* private fields */ }

Gradient Centralization optimizer wrapper.

Wraps any optimizer and applies gradient centralization before the optimizer step. GC normalizes gradients by subtracting their mean, which improves training dynamics.

Example

use tensorlogic_train::*;
use scirs2_core::ndarray::Array2;
use std::collections::HashMap;

// Create base optimizer
let config = OptimizerConfig { learning_rate: 0.001, ..Default::default() };
let adam = AdamOptimizer::new(config);

// Wrap with gradient centralization
let mut gc_adam = GradientCentralization::new(
    Box::new(adam),
    GcConfig::default(),
);

// Use as normal optimizer - GC is applied automatically
let mut params = HashMap::new();
params.insert("w1".to_string(), Array2::zeros((10, 5)));

let mut grads = HashMap::new();
grads.insert("w1".to_string(), Array2::ones((10, 5)));

gc_adam.step(&mut params, &grads).unwrap();

Implementations

impl GradientCentralization

pub fn new(inner_optimizer: Box<dyn Optimizer>, config: GcConfig) -> Self

Create a new gradient centralization optimizer.

pub fn with_default(inner_optimizer: Box<dyn Optimizer>) -> Self

Create with default configuration.

pub fn config(&self) -> &GcConfig

Get GC configuration.

pub fn config_mut(&mut self) -> &mut GcConfig

Get mutable GC configuration.

pub fn stats(&self) -> &GcStats

Get statistics.

pub fn reset_stats(&mut self)

Reset statistics.

Trait Implementations

impl Optimizer for GradientCentralization

fn step( &mut self, params: &mut HashMap<String, Array2<f64>>, grads: &HashMap<String, Array2<f64>>, ) -> TrainResult<()>

Update parameters with computed gradients.

fn zero_grad(&mut self)

Zero all gradients.

fn get_lr(&self) -> f64

Get current learning rate.

fn set_lr(&mut self, lr: f64)

Set learning rate.

fn state_dict(&self) -> HashMap<String, Vec<f64>>

Get optimizer state for checkpointing.

fn load_state_dict(&mut self, state: HashMap<String, Vec<f64>>)

Load optimizer state from checkpoint.