What are learning rate schedules and how can they optimize machine learning models?

1 Constant learning rate

A constant learning rate is the simplest and most common approach, where you set a fixed value for the learning rate and keep it unchanged throughout the training. This can work well if you have a good estimate of the optimal learning rate for your problem, or if you use a trial-and-error method to find the best value. However, a constant learning rate can also have some drawbacks, such as being too slow to reach the minimum, being too sensitive to noise or fluctuations in the loss function, or missing the optimal point due to large steps. Therefore, a constant learning rate is not always the best choice, especially for complex and non-convex problems.

2 Decay learning rate

A decay learning rate is a more flexible and adaptive approach, where you gradually reduce the learning rate as the training progresses. This can help you to avoid overshooting the minimum, as well as to fine-tune the model parameters more precisely. There are different ways to implement a decay learning rate, such as using a fixed or exponential decay rate, or using a step or inverse decay function. The main challenge of using a decay learning rate is to choose the right decay factor and frequency, as well as the initial and final learning rates. A decay learning rate can be implemented in TensorFlow using the tf.keras.optimizers.schedules module, or in PyTorch using the torch.optim.lr_scheduler module.

3 Cyclical learning rate

A cyclical learning rate is a more advanced and innovative approach, where you vary the learning rate between a lower and an upper bound in a cyclical fashion. This can help you to escape from local minima, as well as to explore different regions of the loss function. The idea behind a cyclical learning rate is to use a high learning rate to increase the model's diversity and robustness, and then use a low learning rate to refine the model's accuracy and stability. There are different ways to implement a cyclical learning rate, such as using a triangular, sinusoidal, or sawtooth wave function. The main challenge of using a cyclical learning rate is to choose the right range and frequency of the cycles, as well as the optimal policy to update the learning rate. A cyclical learning rate can be implemented in TensorFlow using the tf.keras.experimental.CosineDecayRestarts class, or in PyTorch using the torch.optim.lr_scheduler.CyclicLR class.

5 How to choose a learning rate schedule

Choosing a learning rate schedule is not an easy task, as it depends on a variety of factors, such as the type and complexity of the problem, the size and quality of the data, the architecture and initialization of the model, and the goal and criteria of the optimization. Therefore, there is no universal solution, so it might be necessary to test different options and compare their results. Nevertheless, some general guidelines that can help you to choose a learning rate schedule include beginning with a constant learning rate and utilizing a grid search or random search to find the best value. If the model converges too slowly or gets stuck in a suboptimal point, try using a decay learning rate and tune the decay factor and frequency. Additionally, if the model suffers from high variance or overfitting, try using a cyclical learning rate and adjust the range and frequency of the cycles. Moreover, if the model has many parameters or the data is heterogeneous or noisy, try using an adaptive learning rate and select the appropriate algorithm and parameters. Finally, monitor the learning curves and validation metrics to evaluate how the learning rate schedule affects the model's performance and stability.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?