To select an optimal beta schedule for your machine learning model, you can run several experiments focused on adjusting and evaluating different beta values over a defined training period. A beta schedule can significantly impact how quickly your model converges and adapts during training, particularly in methods like Adam or RMSprop that use adaptive learning rates. The goal is to find a balance that helps your model learn effectively without overshooting the optimal solution or getting stuck in suboptimal areas.
One effective experiment is to implement a grid search over various beta values. This involves selecting a range of beta1 and beta2 values (for instance, beta1 values between 0.8 and 0.99, and beta2 values between 0.9 and 0.999) and training the model for a fixed number of epochs using a consistent dataset. Track the performance metrics such as accuracy, loss reduction, or validation metrics across different runs. By analyzing which combination of beta values led to the best performance, you can hone in on a suitable beta schedule for your particular problem.
Another approach is to use a more dynamic method called learning rate scheduling coupled with beta value adjustment. For example, you might start with a standard beta schedule and then adjust it based on the model's performance feedback during training. If the model isn't improving, consider switching to alternatives such as using a higher beta value for momentum or reducing beta2 if the model is taking too long to settle. By monitoring key metrics and adopting an iterative approach, you can adjust the beta values in real-time to find an optimal schedule tailored to your specific dataset and model architecture.