Learning rate warmup is a technique used during the training of machine learning models, particularly deep neural networks, to gradually increase the learning rate from a very small value to its target value in the initial stages of training. The purpose of learning rate warmup is to address certain issues that can arise at the beginning of the training process. Here's why we need learning rate warmup and how it works:
Why We Need Learning Rate Warmup:
Avoiding Divergence: At the start of training, when the model's weights are randomly initialized, the learning rate determines the size of weight updates. If the learning rate is too large from the beginning, it can lead to overly large weight updates that cause the optimization process to diverge, preventing the model from converging to a good solution.
Exploration: In the early stages, the model's weights are far from optimal, and the loss landscape is relatively unknown. A small learning rate may cause the optimization process to get stuck in poor local minima. Learning rate warmup allows the model to explore a larger part of the loss landscape by gradually increasing the learning rate.
How Learning Rate Warmup Works:
Learning rate warmup involves the following steps:
Initial Learning Rate: Set an initial learning rate (e.g., a small value, often referred to as the "warmup learning rate").
Warmup Period: Specify a warmup period, which is the number of initial training iterations (or epochs) during which the learning rate is gradually increased.
Linear Increase: Linearly increase the learning rate from its initial value to its target value over the warmup period. This means that at each iteration during the warmup period, the learning rate is adjusted slightly higher until it reaches the target learning rate.
Target Learning Rate: After the warmup period is complete, the learning rate remains constant at its target value for the remainder of the training.
Benefits of Learning Rate Warmup:
Stable Training: Learning rate warmup helps prevent the optimization process from diverging due to overly large initial weight updates.
Exploration: Gradually increasing the learning rate allows the model to explore the loss landscape effectively, increasing the chances of finding good solutions.
Faster Convergence: Learning rate warmup can speed up the convergence of training, as it ensures that the model starts with an appropriate learning rate.
Robustness: It makes the training process more robust to variations in initialization and hyperparameter settings.
Common Warmup Strategies:
Constant Learning Rate: In some cases, a constant, small learning rate may be used during the warmup period.
Linear Warmup: Linearly increasing the learning rate over the warmup period is a common approach.
Exponential Warmup: Some practitioners use an exponential increase in the learning rate, starting from a very small value and gradually growing exponentially until it reaches the target rate.
Learning rate warmup is particularly beneficial when training very deep neural networks or using large-batch training, as these scenarios can be more sensitive to the choice of the initial learning rate. It helps strike a balance between stability and exploration during the crucial early stages of training.
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