When training deep learning models, setting the learning rate (LR) is a crucial hyperparameter that can have a significant impact on the training process and the quality of the learned model. Typically, the LR is set to a fixed value at the beginning of training and gradually decreased over time. However, it has been observed that starting with a high LR can lead to faster convergence and better generalization performance.
This is where learning rate warmup comes into play. The concept of warmup involves gradually increasing the LR from a small value to the desired maximum value during the early stages of the training process. The motivation for this is to allow the optimizer to explore the parameter space more quickly and find a better initial region to start from. This can lead to faster convergence and better performance, especially when training with large batch sizes or on complex datasets.
One way to implement learning rate warmup in PyTorch is by using a learning rate scheduler. The PyTorch scheduler module provides several classes for adjusting the LR during training, including the ‘torch.optim.lr_scheduler.LambdaLR‘ class, which allows users to define a custom scaling function for the LR. We can define a function which starts with a low LR and gradually increases the LR to the desired maximum value, then pass this function to the scheduler. For example:
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
optimizer = optim.SGD(model.parameters(), lr=0.1)
scheduler = LambdaLR(optimizer, lr_lambda=lambda epoch: epoch / 10 if epoch < 10 else 1.)
for epoch in range(num_epochs):
# training loop
scheduler.step()In this example, the learning rate is initially set to 0.1, and the ‘lr_lambda‘ function gradually increases the LR from 0.01 at epoch 0 to 1 at epoch 10.
In summary, learning rate warmup can be an effective strategy for improving the training of deep learning models. By gradually increasing the LR during the early stages of training, we can help the optimizer explore the parameter space more efficiently and achieve better performance.