Learning Rate Decay
Learning rate decay is a technique used in machine learning models, especially deep neural networks. It is sometimes referred to as learning rate scheduling or learning rate annealing. Throughout the training phase, it entails gradually lowering the learning rate. Learning rate decay is used to gradually adjust the learning rate, usually by lowering it, to facilitate the optimization algorithm’s more rapid convergence to a better solution. This method tackles problems that are frequently linked to a fixed learning rate, such as oscillations and sluggish convergence.
Learning rate decay can be accomplished by a variety of techniques, such as step decay, exponential decay, and 1/t decay. Degradation strategy selection is based on the particular challenge and architecture. When training deep learning models, learning rate decay is a crucial hyperparameter that, when used properly, can result in faster training, better convergence, and increased model performance.
Learning Rate Decay
Imagine you’re looking for a coin you dropped in a big room. At first, you take big steps, covering a lot of ground quickly. But as you get closer to the coin, you take tinier steps to look more precisely. This is similar to how learning rate decay works in machine learning.
In training a machine learning model, the “learning rate” decides how much we adjust the model in response to the error it made. Start with a high learning rate, and the model might learn quickly, but it can overshoot and miss the best solution. Start too low, and it might be too slow or get stuck. So, instead of keeping the learning rate constant, we gradually reduce it. This method is called “learning rate decay.” We start off taking big steps (high learning rate) when we’re far from the best solution. But as we get closer, we reduce the learning rate, taking smaller steps, and ensuring we don’t miss the optimal solution. This approach helps the model train faster and more accurately.
There are various ways to reduce the learning rate: some reduce it gradually over time, while others drop it sharply after a set number of training rounds. The key is to find a balance that lets the model learn efficiently without missing the best possible solution.
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