How do I choose a learning rate for my model?

How do I choose a learning rate for my model?

Choosing a learning rate is crucial for training your model effectively. A learning rate that's too high can cause the model to converge too quickly to a suboptimal solution, while a rate that's too low can make the training process unnecessarily slow. It's often a good idea to start with a common value like 0.01 and adjust based on your model's performance.

What are some methods to find the best learning rate?

You can use techniques like grid search, random search, or learning rate schedules. Additionally, the learning rate finder method can help you visualize how the learning rate affects loss.

What happens if I set the learning rate too high?

If the learning rate is too high, your model may fail to converge, resulting in erratic training behavior and potentially increasing loss instead of decreasing it.

Can I use adaptive learning rates?

Yes, adaptive learning rate methods like Adam or RMSprop adjust the learning rate during training, which can help improve convergence.