A Lernrate schedule is a strategy im maschinellen Lernen, particularly in the training of neuronale Netze, to adjust the learning rate over time. The learning rate is a hyperparameter that determines the size of the step taken during optimization to minimize the loss function. Setting an appropriate learning rate is crucial, as a value that is too high can lead to overshooting the optimal solution, while a value that is too low can slow down convergence.
Lernratenpläne können statisch oder dynamisch sein. Ein statischer Plan bleibt konstante Lernrate throughout the training process, which may not be optimal for complex training tasks. In contrast, dynamic schedules adjust the learning rate based on certain criteria, such as the number of epochs, the training loss, or performance metrics.
Gängige Arten von Lernratenplänen umfassen:
- Schritt Abnahme: Reduziert die Lernrate um einen Faktor in festgelegten Intervallen.
- Exponentieller Zerfall: Verringert die Lernrate exponentiell, während das Training voranschreitet.
- Kosinus-Annealing: Gradually reduces the learning rate following a cosine curve, which allows for a longer training phase with smaller learning rates.
- Reduzieren bei Plateau: Verringert die Lernrate, wenn eine Metrik keine Verbesserung mehr zeigt.
Die Verwendung eines Lernratenplans kann zu einer besseren Konvergenz und verbesserten Modellleistung, as it allows the model to make larger updates in the early stages of training and smaller, more refined adjustments as it approaches the optimal solution.