46 lines
1.5 KiB
Rust
46 lines
1.5 KiB
Rust
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use optimizer::prelude::*;
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fn main() {
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// Multi-parameter optimization with TPE sampler.
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let sampler = TpeSampler::builder().seed(42).build().unwrap();
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let mut study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
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study.set_pruner(MedianPruner::new(Direction::Minimize));
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let lr = FloatParam::new(1e-5, 1e-1)
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.log_scale()
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.name("learning_rate");
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let n_layers = IntParam::new(1, 5).name("n_layers");
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let dropout = FloatParam::new(0.0, 0.5).step(0.05).name("dropout");
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let batch_size = CategoricalParam::new(vec![16, 32, 64, 128]).name("batch_size");
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study
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.optimize(80, |trial| {
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let lr_val = lr.suggest(trial)?;
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let layers = n_layers.suggest(trial)?;
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let drop = dropout.suggest(trial)?;
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let bs = batch_size.suggest(trial)?;
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// Simulate training with intermediate reporting.
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let mut loss = 1.0;
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for epoch in 0..10 {
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loss *= 0.7 + 0.3 * lr_val.ln().abs() / 12.0;
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loss += drop * 0.05;
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loss += (1.0 / bs as f64) * 0.1;
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loss -= layers as f64 * 0.02;
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trial.report(epoch, loss);
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if trial.should_prune() {
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return Err(TrialPruned.into());
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}
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}
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Ok::<_, Error>(loss)
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})
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.unwrap();
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println!("{}", study.summary());
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let path = "optimization_report.html";
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generate_html_report(&study, path).unwrap();
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println!("\nReport saved to {path}");
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}
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