Learning Rate Scheduler
We can adjust the learning rate depending on some conditions which helps to improve the model performance. We are going to briefly check some popular methods in this post.
1. Lambda LR
$ lr_{epoch} = lr_0*\lambda $
where lr_lambda is a function or list(of functions to each group of parameters) and it is multiplied by the initial learning rate.
Pytorch:
lambda1 = lambda epoch: epoch // 30
lambda2 = lambda epoch: 0.95 ** epoch
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=[lambda1, lambda2])
2. Step LR
$ lr_{epoch} =
\begin{cases}
\gamma*lr_{epoch-1} & \text{if epoch * step_size = 0}
lr_{epoch-1} & \text{otherwise}
\end{cases} $
Decays the learning rate of each parameter by gamma every step_size epochs. Normally, set the step_size to be five epochs; meaning that decaying the lr every five epoch. Assume that the initial learning rate is 0.05 for all groups, step_size = 2 and gamma = 0.1, lr = 0.005 if 2 <= epoch < 4, lr = 0.0005 if 4 <= epoch < 6, so on.
Pytorch:
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.1)
Also, add “scheduler.step()” after “optimizer.step()” in the train step.
outputs = model(X)
loss = criterion(outputs, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
3. Exponential LR
$ lr_{epoch} = \gamma * lr_{epoch-1} $
Decays the learning rate by gamma every epoch.
Pytorch:
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.1)
4. CosineAnnealingLR
$ \eta_{t}=\eta_{\min }+\frac{1}{2}\left(\eta_{\max }-\eta_{\min }\right)\left(1+\cos \left(\frac{T_{c u r}}{T_{\max }} \pi\right)\right) $
Decays the learning rate using a cosine annealing schedule; thus combining both periods with hot learning rates and cold learning rates - improving model performance.
Reference
- https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
- https://towardsdatascience.com/learning-rate-schedules-and-adaptive-learning-rate-methods-for-deep-learning-2c8f433990d1
- https://towardsdatascience.com/learning-rate-scheduler-d8a55747dd90
- https://www.kaggle.com/isbhargav/guide-to-pytorch-learning-rate-scheduling