Learning rate schedulers in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a learning rate scheduler in PyTorch?

A learning rate scheduler is a tool that changes the learning rate during training to help the model learn better and faster.

beginner

Why do we adjust the learning rate during training?

Adjusting the learning rate helps the model avoid getting stuck and improves accuracy by starting with bigger steps and then taking smaller steps as it learns.

intermediate

Name two common types of learning rate schedulers in PyTorch.

StepLR and ExponentialLR are two common schedulers. StepLR reduces the learning rate after fixed steps, ExponentialLR reduces it smoothly over time.

intermediate

How does StepLR scheduler work?

StepLR lowers the learning rate by a factor every few epochs, like turning down the volume step by step.

beginner

What is the benefit of using a learning rate scheduler?

It helps the model train more efficiently by adjusting learning speed, leading to better results and less chance of missing the best solution.

What does a learning rate scheduler do during training?

A Changes the loss function

B Changes the model architecture

C Changes the training data

D Changes the learning rate over time

A learning rate scheduler adjusts the learning rate during training to improve learning.

Which PyTorch scheduler reduces learning rate after fixed steps?

A ExponentialLR

B StepLR

C CosineAnnealingLR

D ReduceLROnPlateau

StepLR reduces the learning rate by a factor every fixed number of epochs.

Why start training with a higher learning rate?

A To increase loss

B To avoid training

C To make big learning steps initially

D To reduce model size

Starting with a higher learning rate helps the model learn faster at the beginning.

What happens if learning rate is too high all the time?

A Model may not learn well or miss best solution

B Model learns perfectly

C Training is faster and better

D Model size increases

Too high learning rate can cause the model to miss the best solution or not learn properly.

Which scheduler adjusts learning rate based on validation loss?

A ReduceLROnPlateau

B StepLR

C ExponentialLR

D CosineAnnealingLR

ReduceLROnPlateau lowers learning rate when validation loss stops improving.

Explain in your own words why learning rate schedulers are useful in training neural networks.

Think about how changing speed helps when learning something new.

Describe how the StepLR scheduler changes the learning rate during training.

Imagine turning down volume in steps after some time.

Practice

1. What is the main purpose of using a learning rate scheduler in PyTorch training?

easy

A. To change the model architecture dynamically

B. To increase the batch size automatically

C. To shuffle the training data at each epoch

D. To adjust the learning rate during training for better model performance

Solution

1. Step 1: Understand the role of learning rate

   The learning rate controls how fast the model updates its knowledge during training.

2. Step 2: Identify what a scheduler does

   A learning rate scheduler changes the learning rate over time to improve training stability and performance.

3. Final Answer:

   To adjust the learning rate during training for better model performance -> Option D

4. Quick Check:

   Learning rate scheduler adjusts learning rate [OK]

Hint: Schedulers change learning rate, not batch size or model structure [OK]

Common Mistakes:

• Confusing scheduler with batch size adjustment
• Thinking scheduler changes model layers
• Assuming scheduler shuffles data

2. Which of the following is the correct way to create a StepLR scheduler in PyTorch for optimizer opt with step size 10 and gamma 0.1?

easy

A. scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=10, gamma=0.1)

B. scheduler = torch.optim.StepLR(opt, step=10, decay=0.1)

C. scheduler = torch.optim.lr_scheduler.StepLR(opt, steps=10, gamma=0.1)

D. scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=10, decay=0.1)

Solution

1. Step 1: Recall PyTorch StepLR syntax

   The correct class is torch.optim.lr_scheduler.StepLR with parameters step_size and gamma.

2. Step 2: Match parameters correctly

   step_size=10 and gamma=0.1 are the correct parameter names and values.

3. Final Answer:

   scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=10, gamma=0.1) -> Option A

4. Quick Check:

   StepLR uses step_size and gamma [OK]

Hint: Use exact parameter names: step_size and gamma [OK]

Common Mistakes:

• Using wrong parameter names like step or decay
• Calling StepLR from wrong module
• Mixing up parameter order

3. Given the code below, what will be the learning rate after 3 calls to scheduler.step()?

import torch
opt = torch.optim.SGD([torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))], lr=0.1)
scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.5)

for _ in range(3):
    scheduler.step()
    current_lr = opt.param_groups[0]['lr']

medium

A. 0.05

B. 0.1

C. 0.025

D. 0.0125

Solution

1. Step 1: Understand StepLR behavior

   StepLR reduces learning rate by gamma every step_size epochs. Here, step_size=2, gamma=0.5.

2. Step 2: Calculate learning rate after 3 steps

   After 1 step: lr=0.1 (no change, step 1 < 2)
   After 2 steps: lr=0.1 * 0.5 = 0.05 (step 2 reached)
   After 3 steps: lr remains 0.05 (step 3 < 4)

3. Final Answer:

   0.05 -> Option A

4. Quick Check:

   StepLR halves lr every 2 steps [OK]

Hint: Learning rate changes only at multiples of step_size [OK]

Common Mistakes:

• Reducing learning rate every step instead of every step_size
• Multiplying gamma incorrectly
• Ignoring initial learning rate

4. Identify the error in the following PyTorch learning rate scheduler code:

import torch
opt = torch.optim.Adam([torch.nn.Parameter(torch.randn(3, 3, requires_grad=True))], lr=0.01)
scheduler = torch.optim.lr_scheduler.ExponentialLR(opt, gamma=0.9)

for epoch in range(5):
    scheduler.step()
    print(f"Epoch {epoch}: lr = {opt.param_groups[0]['lr']}")

medium

A. Learning rate should be set inside the loop

B. scheduler.step() should be called after optimizer.step()

C. ExponentialLR does not exist in PyTorch

D. gamma value must be greater than 1

Solution

1. Step 1: Recall correct scheduler usage

   In PyTorch, scheduler.step() should be called after optimizer.step() to update learning rate correctly.

2. Step 2: Check code order

   The code calls scheduler.step() before any optimizer.step(), which is incorrect and may cause unexpected lr updates.

3. Final Answer:

   scheduler.step() should be called after optimizer.step() -> Option B

4. Quick Check:

   Call scheduler.step() after optimizer.step() [OK]

Hint: Always call scheduler.step() after optimizer.step() [OK]

Common Mistakes:

• Calling scheduler.step() before optimizer.step()
• Using invalid gamma values
• Misunderstanding scheduler existence

5. You want to train a model where the learning rate starts at 0.1, then reduces by half every 5 epochs, but after 20 epochs, it should decay exponentially by 0.9 every epoch. Which PyTorch scheduler setup achieves this behavior?

hard

A. Use CosineAnnealingLR with T_max=20 and then StepLR with step_size=5, gamma=0.5

B. Use ExponentialLR with gamma=0.9 from start and manually adjust learning rate at epoch 20

C. Use StepLR with step_size=5, gamma=0.5 for first 20 epochs, then switch to ExponentialLR with gamma=0.9

D. Use StepLR with step_size=20, gamma=0.5 and ignore exponential decay

Solution

1. Step 1: Understand the two-phase learning rate schedule

   First phase: reduce lr by half every 5 epochs for 20 epochs.
   Second phase: after 20 epochs, apply exponential decay by 0.9 every epoch.

2. Step 2: Match PyTorch schedulers to phases

   StepLR with step_size=5, gamma=0.5 fits first phase.
   ExponentialLR with gamma=0.9 fits second phase.
   Switching schedulers after 20 epochs achieves desired behavior.

3. Final Answer:

   Use StepLR with step_size=5, gamma=0.5 for first 20 epochs, then switch to ExponentialLR with gamma=0.9 -> Option C

4. Quick Check:

   Combine StepLR then ExponentialLR for phased decay [OK]

Hint: Combine schedulers for multi-phase learning rate changes [OK]

Common Mistakes:

• Trying to use one scheduler for both phases
• Ignoring the switch at epoch 20
• Using wrong scheduler types for phases