Annealing the Learning Rate: Learning rate annealing refers to the practice of gradually decreasing the learning rate during training. It combines the benefits of both high and low learning rates. Starting with a higher learning rate can expedite initial learning, helping the model to escape from any poor local minima. Then, as training progresses, reducing the learning rate can help the model to converge to a more optimal solution in the loss landscape. There are several strategies for annealing the learning rate: - Step decay: Reduce the learning rate by a factor after a specified number of epochs. - Exponential decay: Reduce the learning rate exponentially, epoch by epoch. - ReduceLROnPlateau: Monitor a metric (like validation loss), and reduce the learning rate when the metric stops improving. - Cosine annealing: Reduce the learning rate following a part of the cosine curve. - Cyclic learning rates: Instead of monotonically decreasing the learning rate, increase and decrease it cyclically within a range. When to use learning rate annealing: - Deep Networks: Deeper architectures with many parameters tend to benefit from learning rate annealing, as they have more complex loss surfaces. - Training from scratch: When a model is trained from scratch (as opposed to fine-tuning), annealing can be especially beneficial. - To achieve higher accuracy: When squeezing out every bit of performance is essential, annealing can help the model converge to a slightly better minima. When not to use it: - Short trainings: For very short training sessions or for models with a small amount of data, the effect of annealing might not be evident. - Transfer Learning/Fine-tuning: When you're fine-tuning a pre-trained model for a few epochs, the benefits of annealing might be minimal since the model is already starting from a good position. - Additional Complexity: Annealing adds another dimension to hyperparameter tuning. In some scenarios, the added complexity might not be worth the potential gain.