PyTorch LR Scheduler - Adjust The Learning Rate For Better Results

In this PyTorch Tutorial we learn how to use a Learning Rate (LR) Scheduler to adjust the LR during training.


In this PyTorch Tutorial we learn how to use a Learning Rate (LR) Scheduler to adjust the LR during training. Models often benefit from this technique once learning stagnates, and you get better results. We will go over the different methods we can use and I'll show some code examples that apply the scheduler.

  • torch.optim.lr_scheduler provides several methods to adjust the learning rate based on the number of epochs.
  • `torch.optim.lr_scheduler.ReduceLROnPlateau allows dynamic learning rate reducing based on some validation measurements.

Documentation:

https://pytorch.org/docs/stable/optim.html


FREE VS Code / PyCharm Extensions I Use

鉁 Write cleaner code with Sourcery, instant refactoring suggestions: Link*


PySaaS: The Pure Python SaaS Starter Kit

馃殌 Build a software business faster with pure Python: Link*

* These are affiliate link. By clicking on it you will not have any additional costs. Instead, you will support my project. Thank you! 馃檹