How to perform hyperparameter tuning in PyTorch?
There are several common methods for hyperparameter tuning in PyTorch.
- Manual adjustment: Adjust the hyperparameters by manually changing the values in the code. This method is more straightforward, but can be tried for simple models or initial tuning.
- Utilize Grid Search: Exhaustively search within a given range of hyperparameters using the Grid Search method. Use itertools.product to generate all possible combinations, train the model on each combination, and then select the best performing hyperparameter combination.
- Utilizing Random Search: In contrast to Grid Search, Random Search involves training by randomly selecting combinations of hyperparameters. This method is relatively more efficient as it does not require exhaustively trying all possible combinations.
- You can also utilize specially designed hyperparameter optimization algorithms such as Bayesian Optimization, Hyperband, Population-based Training, etc., to more effectively search the hyperparameter space and find the optimal combination within limited resources.
Generally, it is recommended to start with Grid Search and then choose a more suitable parameter adjustment method based on experimental results. Additionally, tools provided by PyTorch such as torch.optim and torch.lr_scheduler can be used for hyperparameter tuning.
