Hyperparameter tuning is tedious but essential: a 2× difference in learning rate can mean 10× difference in convergence speed. Learn rate warmup prevents training instability. Batch size affects memory/quality trade-offs. Random search finds good hyperparameters faster than grid search.
Learning Rate Warmup
# Linear warmup: lr increases from 0 to target over warmup_steps
from torch.optim.lr_scheduler import LinearLR
scheduler = LinearLR( optimizer, start_factor=0.1, total_iters=1000 # Warmup steps )
for epoch in range(num_epochs): train() scheduler.step() # Gradually increase learning rate
Warmup prevents training instability in the first steps.
Batch Size Effects
Large batch size: Fast training, lower memory per sample, but may converge slower
Small batch size: Slower training, higher memory per sample, but may converge faster
A common approach: Start with the largest batch that fits in memory, then tune learning rate.
Hyperparameter Search
import optuna
def objective(trial): lr = trial.suggest_float('lr', 1e-5, 1e-2, log=True) batch_size = trial.suggest_categorical('batch_size', [32, 64, 128])
model = train_and_evaluate(lr=lr, batch_size=batch_size) return model.val_accuracy
study = optuna.create_study() study.optimize(objective, n_trials=100)
Random search (via Optuna) finds better hyperparameters than grid search with the same budget.
Conclusion
Hyperparameter tuning dramatically affects training. Warmup stabilizes early training, batch size balances speed/quality, and random search finds good hyperparameters efficiently. Developing tuning intuition is essential for effective model training. This completes the optimization series.