How can I integrate learning rate schedulers into the training loop of a transformer model

Question

With the help of Python programming, can you explain How can I integrate learning rate schedulers into the training loop of a transformer model?

Answer 1

You can integrate learning rate schedulers into the training loop of a transformer model by using a scheduler function that adjusts the learning rate dynamically based on the current epoch or step. Here is the steps you can follow:

Steps:

• Define the learning rate scheduler.
• Update the learning rate during training based on the scheduler.

Here is the code you can refer to:

In the above code, we are using the following:

• Scheduler: Use StepLR to decrease the learning rate every 5 epochs with a decay factor of 0.5.
• Integration: Call scheduler.step() at the end of each epoch to update the learning rate.
• Monitoring: Print the current learning rate to track changes during training.

Hence, this approach can be adapted to other schedulers like CosineAnnealingLR or ReduceLROnPlateau.