You can optimize learning rates scheduled to prevent overfitting or underfitting by following these strategies:
- Learning Rate Warmup: Gradually increases the learning rate from a small initial value to the target learning rate over a few epochs to stabilize training.
- Step Decay: Reduces the learning rate by a fixed factor at predefined steps or epochs, typically after a set number of iterations.
- Exponential Day: Decreases the learning rate exponentially over time, typically by a fixed multiplicative factor per epoch.
- Cosine Annealing: Reduces the learning rate following a concise curve, starting high and slowly decreasing to a minimum, often with restarts.
- Reduce on Plateau: Lowers the learning rate when a metric stops improving for a specified number of epochs, helping avoid stagnant training.
These strategies above will balance effective learning, leading to the prevention of overfitting or underfitting.