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kholdrex merged 1 commit into from
Jun 6, 2026
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test: bound advanced LR scheduling example #24

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197 changes: 109 additions & 88 deletions examples/advanced_lr_scheduling.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -12,13 +12,29 @@ use rust_lstm::{
ScheduledLSTMTrainer, ScheduledOptimizer, StepLR, TrainingConfig, WarmupScheduler,
};

pub const DEMO_TRAIN_SEQUENCES: usize = 12;
pub const DEMO_VAL_SEQUENCES: usize = 4;
pub const DEMO_SEQUENCE_LENGTH: usize = 6;
pub const DEMO_HIDDEN_SIZE: usize = 4;
pub const DEMO_ADVANCED_HIDDEN_SIZE: usize = 6;
pub const DEMO_POLYNOMIAL_EPOCHS: usize = 5;
pub const DEMO_CYCLICAL_EPOCHS: usize = 5;
pub const DEMO_WARMUP_EPOCHS: usize = 5;
pub const DEMO_ADVANCED_EPOCHS: usize = 5;
pub const DEMO_POLYNOMIAL_ITERS: usize = DEMO_POLYNOMIAL_EPOCHS;
pub const DEMO_CYCLICAL_STEP_SIZE: usize = 2;
pub const DEMO_WARMUP_EPOCH_COUNT: usize = 2;
pub const DEMO_BASE_STEP_SIZE: usize = 2;
pub const DEMO_VISUALIZATION_STEP_SIZE: usize = 2;
pub const DEMO_VISUALIZATION_STEPS: usize = 20;

fn main() {
println!("🚀 Advanced Learning Rate Scheduling for Rust-LSTM");
println!("===================================================\n");

// Generate sample training data
let train_data = generate_sine_wave_data(50, 0.0);
let val_data = generate_sine_wave_data(10, 1000.0);
let train_data = generate_sine_wave_data(DEMO_TRAIN_SEQUENCES, 0.0);
let val_data = generate_sine_wave_data(DEMO_VAL_SEQUENCES, 1000.0);

// 1. Polynomial Decay Example
polynomial_decay_example(&train_data, &val_data);
Expand All @@ -43,24 +59,18 @@ fn polynomial_decay_example(
println!("1️⃣ Polynomial Decay Example");
println!(" Smoothly decays LR using polynomial function\n");

let network = LSTMNetwork::new(1, 8, 1);
let network = LSTMNetwork::new(1, DEMO_HIDDEN_SIZE, 1);

let loss_function = MSELoss;
let scheduled_optimizer = ScheduledOptimizer::polynomial(
Adam::new(0.01),
0.01, // base_lr
25, // total_iters
2.0, // power
0.001, // end_lr
0.01, // base_lr
DEMO_POLYNOMIAL_ITERS, // total_iters
2.0, // power
0.001, // end_lr
);

let config = TrainingConfig {
epochs: 30,
print_every: 5,
clip_gradient: Some(1.0),
log_lr_changes: true,
early_stopping: None,
};
let config = polynomial_decay_training_config();

let mut trainer =
ScheduledLSTMTrainer::new(network, loss_function, scheduled_optimizer).with_config(config);
Expand All @@ -80,23 +90,17 @@ fn cyclical_lr_examples(

// 2a. Triangular Cyclical LR
println!("2a. Triangular Cyclical LR");
let network = LSTMNetwork::new(1, 8, 1);
let network = LSTMNetwork::new(1, DEMO_HIDDEN_SIZE, 1);

let loss_function = MSELoss;
let scheduled_optimizer = ScheduledOptimizer::cyclical(
Adam::new(0.001),
0.001, // base_lr
0.01, // max_lr
8, // step_size
0.001, // base_lr
0.01, // max_lr
DEMO_CYCLICAL_STEP_SIZE, // step_size
);

let config = TrainingConfig {
epochs: 25,
print_every: 5,
clip_gradient: Some(1.0),
log_lr_changes: false, // Too frequent for cyclical
early_stopping: None,
};
let config = cyclical_lr_training_config();

let mut trainer =
ScheduledLSTMTrainer::new(network, loss_function, scheduled_optimizer).with_config(config);
Expand All @@ -106,23 +110,17 @@ fn cyclical_lr_examples(

// 2b. Triangular2 Cyclical LR (halving amplitude each cycle)
println!("2b. Triangular2 Cyclical LR (halving amplitude each cycle)");
let network = LSTMNetwork::new(1, 8, 1);
let network = LSTMNetwork::new(1, DEMO_HIDDEN_SIZE, 1);

let loss_function = MSELoss;
let scheduled_optimizer = ScheduledOptimizer::cyclical_triangular2(
Adam::new(0.001),
0.001, // base_lr
0.01, // max_lr
8, // step_size
0.001, // base_lr
0.01, // max_lr
DEMO_CYCLICAL_STEP_SIZE, // step_size
);

let config2 = TrainingConfig {
epochs: 25,
print_every: 5,
clip_gradient: Some(1.0),
log_lr_changes: false,
early_stopping: None,
};
let config2 = cyclical_lr_training_config();

let mut trainer =
ScheduledLSTMTrainer::new(network, loss_function, scheduled_optimizer).with_config(config2);
Expand All @@ -132,24 +130,18 @@ fn cyclical_lr_examples(

// 2c. ExpRange Cyclical LR (exponential scaling)
println!("2c. ExpRange Cyclical LR (exponential scaling)");
let network = LSTMNetwork::new(1, 8, 1);
let network = LSTMNetwork::new(1, DEMO_HIDDEN_SIZE, 1);

let loss_function = MSELoss;
let scheduled_optimizer = ScheduledOptimizer::cyclical_exp_range(
Adam::new(0.001),
0.001, // base_lr
0.01, // max_lr
8, // step_size
0.95, // gamma
0.001, // base_lr
0.01, // max_lr
DEMO_CYCLICAL_STEP_SIZE, // step_size
0.95, // gamma
);

let config3 = TrainingConfig {
epochs: 25,
print_every: 5,
clip_gradient: Some(1.0),
log_lr_changes: false,
early_stopping: None,
};
let config3 = cyclical_lr_training_config();

let mut trainer =
ScheduledLSTMTrainer::new(network, loss_function, scheduled_optimizer).with_config(config3);
Expand All @@ -167,26 +159,15 @@ fn warmup_scheduler_example(
println!("3️⃣ Warmup Scheduler Example");
println!(" Gradually increases LR during warmup, then applies base scheduler\n");

let network = LSTMNetwork::new(1, 8, 1);
let network = LSTMNetwork::new(1, DEMO_HIDDEN_SIZE, 1);

// Create warmup scheduler with step decay after warmup
let base_scheduler = StepLR::new(10, 0.5); // Reduce by half every 10 epochs
let warmup_scheduler = WarmupScheduler::new(
5, // warmup_epochs
base_scheduler, // base_scheduler
0.001, // warmup_start_lr
);
let base_scheduler = StepLR::new(DEMO_BASE_STEP_SIZE, 0.5);
let warmup_scheduler = WarmupScheduler::new(DEMO_WARMUP_EPOCH_COUNT, base_scheduler, 0.001);

let loss_function = MSELoss;
let scheduled_optimizer = ScheduledOptimizer::new(Adam::new(0.01), warmup_scheduler, 0.01);

let config = TrainingConfig {
epochs: 30,
print_every: 3,
clip_gradient: Some(1.0),
log_lr_changes: true,
early_stopping: None,
};
let config = warmup_scheduler_training_config();

let mut trainer =
ScheduledLSTMTrainer::new(network, loss_function, scheduled_optimizer).with_config(config);
Expand All @@ -202,21 +183,27 @@ fn schedule_visualization() {
println!(" ASCII visualization of different schedulers\n");

// Visualize StepLR
println!("StepLR (step_size=10, gamma=0.5):");
let step_scheduler = StepLR::new(10, 0.5);
LRScheduleVisualizer::print_schedule(step_scheduler, 0.01, 50, 60, 10);
println!("StepLR (step_size=2, gamma=0.5):");
let step_scheduler = StepLR::new(DEMO_VISUALIZATION_STEP_SIZE, 0.5);
LRScheduleVisualizer::print_schedule(step_scheduler, 0.01, DEMO_VISUALIZATION_STEPS, 40, 5);
println!();

// Visualize PolynomialLR
println!("PolynomialLR (power=2.0, end_lr=0.001):");
let poly_scheduler = PolynomialLR::new(50, 2.0, 0.001);
LRScheduleVisualizer::print_schedule(poly_scheduler, 0.01, 50, 60, 10);
let poly_scheduler = PolynomialLR::new(DEMO_VISUALIZATION_STEPS, 2.0, 0.001);
LRScheduleVisualizer::print_schedule(poly_scheduler, 0.01, DEMO_VISUALIZATION_STEPS, 40, 5);
println!();

// Visualize CyclicalLR
println!("CyclicalLR Triangular (base_lr=0.001, max_lr=0.01, step_size=8):");
let cyclical_scheduler = CyclicalLR::new(0.001, 0.01, 8);
LRScheduleVisualizer::print_schedule(cyclical_scheduler, 0.001, 50, 60, 10);
println!("CyclicalLR Triangular (base_lr=0.001, max_lr=0.01, step_size=2):");
let cyclical_scheduler = CyclicalLR::new(0.001, 0.01, DEMO_CYCLICAL_STEP_SIZE);
LRScheduleVisualizer::print_schedule(
cyclical_scheduler,
0.001,
DEMO_VISUALIZATION_STEPS,
40,
5,
);
println!();

println!("----------------------------------------\n");
Expand All @@ -230,25 +217,20 @@ fn advanced_training_example(
println!(" Warmup + Cyclical LR + Dropout + Gradient Clipping\n");

// Create network with dropout
let network = LSTMNetwork::new(1, 16, 1)
let network = LSTMNetwork::new(1, DEMO_ADVANCED_HIDDEN_SIZE, 1)
.with_input_dropout(0.1, true) // Variational dropout
.with_recurrent_dropout(0.2, true) // Variational recurrent dropout
.with_output_dropout(0.1); // Standard output dropout

// Create warmup scheduler with cyclical base scheduler
let base_scheduler = CyclicalLR::new(0.001, 0.01, 10).with_mode(CyclicalMode::Triangular2);
let warmup_scheduler = WarmupScheduler::new(5, base_scheduler, 0.0001);
let base_scheduler =
CyclicalLR::new(0.001, 0.01, DEMO_CYCLICAL_STEP_SIZE).with_mode(CyclicalMode::Triangular2);
let warmup_scheduler = WarmupScheduler::new(DEMO_WARMUP_EPOCH_COUNT, base_scheduler, 0.0001);

let loss_function = MSELoss;
let scheduled_optimizer = ScheduledOptimizer::new(Adam::new(0.01), warmup_scheduler, 0.01);

let config = TrainingConfig {
epochs: 40,
print_every: 5,
clip_gradient: Some(1.0), // Gradient clipping
log_lr_changes: false, // Too frequent for cyclical
early_stopping: None,
};
let config = advanced_training_config();

let mut trainer =
ScheduledLSTMTrainer::new(network, loss_function, scheduled_optimizer).with_config(config);
Expand All @@ -272,18 +254,17 @@ fn advanced_training_example(
println!("\n✅ Advanced training complete!");
}

fn generate_sine_wave_data(
pub fn generate_sine_wave_data(
num_sequences: usize,
offset: f64,
) -> Vec<(Vec<Array2<f64>>, Vec<Array2<f64>>)> {
let mut data = Vec::new();

for i in 0..num_sequences {
let sequence_length = 8;
let mut inputs = Vec::new();
let mut targets = Vec::new();

for t in 0..sequence_length {
for t in 0..DEMO_SEQUENCE_LENGTH {
let x = (offset + i as f64 * 0.1 + t as f64 * 0.2).sin();
let y = (offset + i as f64 * 0.1 + (t + 1) as f64 * 0.2).sin();

Expand All @@ -297,19 +278,59 @@ fn generate_sine_wave_data(
data
}

pub fn polynomial_decay_training_config() -> TrainingConfig {
TrainingConfig {
epochs: DEMO_POLYNOMIAL_EPOCHS,
print_every: 1,
clip_gradient: Some(1.0),
log_lr_changes: true,
early_stopping: None,
}
}

pub fn cyclical_lr_training_config() -> TrainingConfig {
TrainingConfig {
epochs: DEMO_CYCLICAL_EPOCHS,
print_every: 1,
clip_gradient: Some(1.0),
log_lr_changes: false,
early_stopping: None,
}
}

pub fn warmup_scheduler_training_config() -> TrainingConfig {
TrainingConfig {
epochs: DEMO_WARMUP_EPOCHS,
print_every: 1,
clip_gradient: Some(1.0),
log_lr_changes: true,
early_stopping: None,
}
}

pub fn advanced_training_config() -> TrainingConfig {
TrainingConfig {
epochs: DEMO_ADVANCED_EPOCHS,
print_every: 1,
clip_gradient: Some(1.0),
log_lr_changes: false,
early_stopping: None,
}
}

#[cfg(test)]
mod tests {
use super::*;
use rust_lstm::SGD;

#[test]
fn test_advanced_schedulers() {
// Test polynomial scheduler
let poly_scheduler = PolynomialLR::new(100, 2.0, 0.01);
let schedule = LRScheduleVisualizer::generate_schedule(poly_scheduler, 0.1, 100);
assert_eq!(schedule.len(), 100);
let schedule = LRScheduleVisualizer::generate_schedule(poly_scheduler, 0.1, 101);
assert_eq!(schedule.len(), 101);
assert_eq!(schedule[0].1, 0.1);
assert!((schedule[99].1 - 0.01).abs() < 1e-10);
assert!(schedule[99].1 < schedule[0].1);
assert!((schedule[100].1 - 0.01).abs() < 1e-10);

// Test cyclical scheduler
let cyclical_scheduler = CyclicalLR::new(0.01, 0.1, 10);
Expand Down
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