Layer-wise Learning Rate (LWR) is a technique used in training neural networks, where the learning rate is adjusted individually for each layer of the network. In traditional training methods, a single learning rate is applied across all layers, which can be inefficient due to varying sensitivities of different layers to weight updates.
In many neural networks, particularly deep ones, different layers learn at different rates. For instance, lower layers may require smaller updates to their weights because they are learning more fundamental features, while higher layers, which capture more complex abstractions, may benefit from larger updates. By assigning different learning rates to each layer, LWR allows for a more tailored approach to training.
This method can help improve convergence speed and overall model performance. It is particularly useful in transfer learning scenarios, where pre-trained networks are fine-tuned for new tasks. In these cases, the lower layers (which often capture general features) might use a smaller learning rate, while the higher layers (which need to adapt more to the new task) might employ a larger learning rate.
Implementing LWR can involve various strategies, such as defining a fixed ratio of learning rates between layers or employing a dynamic adjustment based on the training progress. Popular deep learning libraries, like TensorFlow and PyTorch, often provide built-in support for customizing learning rates across different layers.