A learning rate schedule is a strategy used in training neural networks to adjust the learning rate over time. The learning rate is a crucial hyperparameter that determines how much to change the model’s weights in response to the estimated error each time the model weights are updated. Properly setting the learning rate can significantly impact the performance and convergence of the model.

There are various types of learning rate schedules. For instance, a common approach is to reduce the learning rate as the number of training epochs increases. This is based on the intuition that a high learning rate can help the model converge quickly in the early stages, while a lower learning rate can refine the model’s performance in later stages ¹.

Another popular method is the cosine learning rate schedule, which gradually decreases the learning rate in a cosine-like manner, allowing for a more refined optimization towards the end of training ³. Additionally, warmup strategies can be employed to stabilize training, especially for complex network architectures ³.