læringshastighetsplanlegger

A learning rate scheduler, often referred to as a læringshastighetsplanlegger in Norwegian, is a hyperparameter optimization technique used in machine learning. Its primary purpose is to adjust the learning rate of an optimization algorithm over the course of training a model. The learning rate determines the step size taken during each iteration of an optimization process, such as gradient descent.

Initially, a higher learning rate can facilitate faster convergence by allowing the model to explore the loss landscape more broadly. However, as training progresses, a very high learning rate can lead to oscillations around the minimum or even divergence, preventing the model from settling into an optimal solution. Conversely, a consistently low learning rate can result in very slow convergence or getting stuck in suboptimal local minima.

Learning rate schedulers address this by systematically decreasing the learning rate over time. This allows for aggressive exploration early in training and finer adjustments as the model approaches a minimum. Common scheduling strategies include step decay, where the learning rate is reduced by a factor at predefined epochs; exponential decay, which reduces the learning rate exponentially with each epoch; and cosine annealing, which follows a cosine curve to gradually decrease the learning rate. Other methods like ReduceLROnPlateau monitor a performance metric and reduce the learning rate when the metric stops improving. The choice of scheduler and its parameters can significantly impact the training efficiency and final performance of a machine learning model.