StepLR and MultiStepLR in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a StepLR scheduler that decreases the learning rate every 5 epochs.

PyTorch

scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=[1], gamma=0.1)

Drag options to blanks, or click blank then click option'

A10

B5

C3

D7

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong step_size value that doesn't match the desired interval.

The step_size parameter controls how often the learning rate decreases. Here, it should be 5 to reduce every 5 epochs.

2fill in blank

medium

Complete the code to create a MultiStepLR scheduler that decreases the learning rate at epochs 10 and 20.

PyTorch

scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[1], gamma=0.1)

Drag options to blanks, or click blank then click option'

A[12, 22]

B[5, 15]

C[8, 18]

D[10, 20]

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong milestone epochs that don't match the schedule.

The milestones parameter is a list of epochs where the learning rate will be reduced. Here, it should be [10, 20].

3fill in blank

hard

Fix the error in the code to correctly update the learning rate scheduler after each epoch.

PyTorch

for epoch in range(num_epochs):
    train()
    validate()
    [1]

Drag options to blanks, or click blank then click option'

Ascheduler.step()

Bscheduler.update()

Cscheduler.adjust()

Dscheduler.reset()

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like update(), adjust(), or reset().

The correct method to update the learning rate scheduler after each epoch is scheduler.step().

4fill in blank

hard

Fill both blanks to create a StepLR scheduler with a step size of 7 and a decay factor of 0.5.

PyTorch

scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=[1], gamma=[2])

Drag options to blanks, or click blank then click option'

A7

B0.1

C0.5

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up step_size and gamma values.

The step_size should be 7 and the gamma (decay factor) should be 0.5 as specified.

5fill in blank

hard

Fill all three blanks to create a MultiStepLR scheduler with milestones at epochs 8 and 16, and a decay factor of 0.2.

PyTorch

scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[1], gamma=[2])

for epoch in range(num_epochs):
    train()
    validate()
    [3]

Drag options to blanks, or click blank then click option'

A[8, 16]

Bscheduler.step()

C0.2

D0.5

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong milestone epochs, wrong gamma, or forgetting to call scheduler.step().

Milestones are [8, 16], gamma is 0.2, and the scheduler is updated each epoch with scheduler.step().

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