Hyperband: When Does It Check for Improvement?
Hyperband, a popular algorithm for hyperparameter optimization, is known for its efficiency. It uses a clever strategy to explore a vast search space while focusing on promising configurations. But how does Hyperband know when to check for improvement? Let's delve into the mechanics of this powerful algorithm.
Understanding Hyperband's Workflow
Hyperband operates in stages, where each stage involves training several models with different hyperparameter configurations. Here's the crucial point: Hyperband doesn't evaluate models at every epoch. Instead, it strategically checks for improvement at specific points during training.
Here's how it works:
- Successive Halving: Hyperband starts with a large number of configurations. It trains each for a few epochs and then eliminates half the configurations with the worst performance.
- Gradually Increasing Training Time: The remaining configurations are trained for a longer period, and the process of halving is repeated. This continues until only a few top performers are left.
- Final Evaluation: These top performers are then trained for a full duration, allowing for a comprehensive evaluation.
Identifying Improvement Points
So, how does Hyperband determine when to check for improvement? It relies on the concept of "brackets". Each bracket represents a specific training time and configuration count. For instance, a bracket could be defined as "training for 5 epochs with 10 configurations."
The key is that Hyperband checks for improvement at the end of each bracket. It compares the performance of the configurations within that bracket and eliminates those with the worst results. This process continues until the final bracket is reached, which corresponds to the full training duration.
Example:
Let's consider a scenario where we want to optimize a neural network for image classification. We set up Hyperband with the following brackets:
- Bracket 1: Train for 2 epochs, 16 configurations
- Bracket 2: Train for 4 epochs, 8 configurations
- Bracket 3: Train for 8 epochs, 4 configurations
- Bracket 4: Train for 16 epochs, 2 configurations
Hyperband will evaluate and discard configurations at the end of each bracket. For example, at the end of Bracket 1, it will compare the performance of the 16 configurations after 2 epochs and remove the 8 worst performers. This process continues until the final bracket, where the remaining 2 configurations are trained for 16 epochs and the best one is selected.
Why This Strategy Works
By checking for improvement only at the end of brackets, Hyperband avoids wasting time evaluating configurations that are unlikely to be optimal. It allows for efficient resource allocation and focuses on exploring promising areas of the hyperparameter space.
Conclusion
Hyperband's strategic approach to checking for improvement during training ensures that it explores a large search space effectively and identifies promising configurations. By leveraging brackets and evaluating at the end of each, it optimizes resource allocation and provides efficient hyperparameter optimization.
References:
