StepLR and MultiStepLR in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is StepLR in PyTorch?

StepLR is a learning rate scheduler that decreases the learning rate by a fixed factor every set number of epochs. It helps the model learn more slowly as training progresses.

intermediate

How does MultiStepLR differ from StepLR?

MultiStepLR decreases the learning rate at specific epochs (called milestones) instead of fixed intervals. This allows more control over when the learning rate changes.

beginner

What parameters do you need to provide to StepLR?

You need to provide the optimizer, step_size (number of epochs between each decrease), and gamma (the factor to multiply the learning rate by).

beginner

What are milestones in MultiStepLR?

Milestones are a list of epoch numbers where the learning rate will be decreased by multiplying with gamma.

beginner

Why use learning rate schedulers like StepLR or MultiStepLR?

They help the model converge better by reducing the learning rate over time, which can improve accuracy and prevent overshooting the best solution.

What does StepLR do to the learning rate?

A Keeps it constant

B Increases it gradually

C Randomly changes it

D Reduces it by a fixed factor every few epochs

StepLR reduces the learning rate by multiplying it by gamma every step_size epochs.

In MultiStepLR, what are milestones?

A Number of batches per epoch

B Epochs where learning rate changes

C Initial learning rates

D Loss values

Milestones are specific epochs where the learning rate is decreased.

Which parameter controls how much the learning rate decreases in StepLR?

A gamma

B step_size

C milestones

D momentum

Gamma is the factor multiplied with the learning rate to reduce it.

If you want to reduce learning rate at epochs 30 and 80, which scheduler is best?

A StepLR

B No scheduler

C MultiStepLR

D ExponentialLR

MultiStepLR lets you specify exact epochs (milestones) to reduce the learning rate.

What is the main benefit of using learning rate schedulers?

A Improve model convergence and accuracy

B Prevent model from learning

C Increase model size

D Faster training without accuracy loss

Schedulers help the model converge better by adjusting learning rate during training.

Explain how StepLR works and what parameters it needs.

Think about a clock ticking every few epochs to reduce learning rate.

Describe the difference between StepLR and MultiStepLR and when you might use each.

Compare fixed steps vs chosen milestones.

Practice

1. What is the main difference between StepLR and MultiStepLR in PyTorch?

easy

A. StepLR decreases learning rate at fixed intervals; MultiStepLR decreases at specific epochs.

B. StepLR increases learning rate; MultiStepLR decreases learning rate.

C. StepLR changes learning rate randomly; MultiStepLR keeps it constant.

D. StepLR is used only for batch size adjustment; MultiStepLR for learning rate.

Solution

1. Step 1: Understand StepLR behavior

   StepLR reduces the learning rate by a factor every fixed number of epochs (step size).

2. Step 2: Understand MultiStepLR behavior

   MultiStepLR reduces the learning rate at specific epochs defined by a list of milestones.

3. Final Answer:

   StepLR decreases learning rate at fixed intervals; MultiStepLR decreases at specific epochs. -> Option A

4. Quick Check:

   StepLR fixed steps, MultiStepLR specific milestones [OK]

Hint: StepLR uses fixed steps; MultiStepLR uses milestone epochs [OK]

Common Mistakes:

• Confusing increase vs decrease of learning rate
• Thinking StepLR changes learning rate randomly
• Mixing learning rate with batch size adjustments

2. Which of the following is the correct way to create a StepLR scheduler in PyTorch that reduces learning rate every 5 epochs by a factor of 0.1?

easy

A. scheduler = StepLR(optimizer, step_size=5, gamma=0.1)

B. scheduler = StepLR(optimizer, milestones=[5], gamma=0.1)

C. scheduler = MultiStepLR(optimizer, step_size=5, gamma=0.1)

D. scheduler = MultiStepLR(optimizer, milestones=[5], gamma=0.1)

Solution

1. Step 1: Recall StepLR parameters

   StepLR takes step_size (int) and gamma (decay factor).

2. Step 2: Identify correct syntax

   scheduler = StepLR(optimizer, step_size=5, gamma=0.1) uses step_size=5 and gamma=0.1, which matches the requirement.

3. Final Answer:

   scheduler = StepLR(optimizer, step_size=5, gamma=0.1) -> Option A

4. Quick Check:

   StepLR uses step_size, not milestones [OK]

Hint: StepLR uses step_size, MultiStepLR uses milestones list [OK]

Common Mistakes:

• Using milestones parameter with StepLR
• Confusing MultiStepLR and StepLR syntax
• Passing step_size as a list

3. Given the following code, what will be the learning rate after epoch 7?

optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
scheduler = MultiStepLR(optimizer, milestones=[3, 6], gamma=0.1)
for epoch in range(8):
    scheduler.step()
    print(f"Epoch {epoch}: lr = {optimizer.param_groups[0]['lr']}")

medium

A. 0.01

B. 0.001

C. 0.1

D. 0.0001

Solution

1. Step 1: Understand milestones and gamma

   Learning rate reduces by factor 0.1 at epochs 3 and 6.

2. Step 2: Calculate learning rate at epoch 7

   Initial lr=0.1; after epoch 3: 0.1*0.1=0.01; after epoch 6: 0.01*0.1=0.001; so at epoch 7 lr=0.001.

3. Final Answer:

   0.001 -> Option B

4. Quick Check:

   Two milestones reduce lr twice: 0.1 -> 0.01 -> 0.001 [OK]

Hint: Multiply lr by gamma at each milestone passed [OK]

Common Mistakes:

• Forgetting to apply gamma at both milestones
• Assuming lr changes before first milestone
• Confusing StepLR with MultiStepLR behavior

4. Identify the error in this code snippet using StepLR:

optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
scheduler = StepLR(optimizer, milestones=[10, 20], gamma=0.5)
for epoch in range(25):
    scheduler.step()
    print(optimizer.param_groups[0]['lr'])

medium

A. scheduler.step() must be called after optimizer.step() inside loop.

B. Optimizer Adam cannot be used with StepLR scheduler.

C. StepLR does not accept milestones parameter; use step_size instead.

D. Gamma value must be greater than 1 for StepLR.

Solution

1. Step 1: Check StepLR parameters

   StepLR expects step_size, not milestones.

2. Step 2: Identify misuse of milestones

   Passing milestones causes error; correct is step_size=10 for example.

3. Final Answer:

   StepLR does not accept milestones parameter; use step_size instead. -> Option C

4. Quick Check:

   StepLR uses step_size, not milestones [OK]

Hint: StepLR uses step_size, not milestones list [OK]

Common Mistakes:

• Using milestones with StepLR
• Thinking Adam optimizer is incompatible
• Misunderstanding gamma parameter range

5. You want to train a model for 30 epochs. You want the learning rate to drop by 0.1 at epochs 10 and 20, and then again every 5 epochs after epoch 20. Which scheduler setup correctly achieves this?

hard

A. Use StepLR with step_size=10 and gamma=0.1

B. Use StepLR with step_size=5 and gamma=0.1

C. Use MultiStepLR with milestones=[10, 20, 25, 30] and gamma=0.1

D. Use MultiStepLR with milestones=[10, 20] and gamma=0.1, then StepLR with step_size=5 after epoch 20

Solution

1. Step 1: Understand the requirement

   Learning rate drops at epochs 10 and 20, then every 5 epochs after 20 (i.e., 25, 30).

2. Step 2: Analyze scheduler options

   MultiStepLR can handle fixed milestones (10, 20). StepLR can handle regular steps (every 5 epochs). Combining both after epoch 20 fits the requirement.

3. Step 3: Evaluate options

   Use MultiStepLR with milestones=[10, 20, 25, 30] and gamma=0.1 misses epochs after 20 beyond 25 and 30; Use StepLR with step_size=5 and gamma=0.1 drops every 5 epochs from start; Use StepLR with step_size=10 and gamma=0.1 drops every 10 epochs only; Use MultiStepLR with milestones=[10, 20] and gamma=0.1, then StepLR with step_size=5 after epoch 20 correctly combines both schedulers.

4. Final Answer:

   Use MultiStepLR with milestones=[10, 20] and gamma=0.1, then StepLR with step_size=5 after epoch 20 -> Option D

5. Quick Check:

   Combine MultiStepLR for early milestones + StepLR for regular steps after [OK]

Hint: Combine MultiStepLR for milestones + StepLR for regular steps [OK]

Common Mistakes:

• Trying to use only one scheduler for mixed schedule
• Misplacing milestones or step_size values
• Assuming StepLR can handle irregular milestones