Learning rate schedulers in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a learning rate scheduler that decreases the learning rate by a factor of 0.1 every 10 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'

A1

B10

C20

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a step_size too small or too large for the training schedule.

Confusing the gamma parameter with step_size.

The StepLR scheduler reduces the learning rate every step_size epochs. Here, setting step_size=10 means the learning rate drops every 10 epochs.

2fill in blank

medium

Complete the code to initialize a cosine annealing learning rate scheduler with 50 epochs.

PyTorch

scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=[1])

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

A50

B25

C100

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Setting T_max too low causing multiple cycles.

Confusing T_max with learning rate value.

The T_max parameter sets the number of epochs for one cosine annealing cycle. Here, it should be 50 to match the total epochs.

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'

Aoptimizer.step()

Bscheduler.update()

Cscheduler.step()

Dscheduler.reset()

Attempts:

3 left

💡 Hint

Common Mistakes

Calling optimizer.step() instead of scheduler.step().

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

After each epoch, you must call scheduler.step() to update the learning rate according to the scheduler's policy.

4fill in blank

hard

Fill both blanks to create a learning rate scheduler that reduces the learning rate by 10% every 5 epochs.

PyTorch

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

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

A5

B0.1

C0.9

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using gamma=0.1 which reduces learning rate by 90%, not 10%.

Setting step_size incorrectly.

The step_size is 5 to reduce every 5 epochs, and gamma is 0.9 to reduce the learning rate by 10% (multiply by 0.9).

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each epoch to its learning rate from the lrs list (the scheduler's history), only for epochs where the learning rate is greater than 0.001.

PyTorch

lr_history = {epoch: lr for epoch, lr in enumerate(lrs) if lr [1] [2] and epoch [3] 10}

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

A>

B0.001

C<

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators causing empty or incorrect dictionaries.

Confusing epoch and learning rate conditions.

This comprehension keeps epochs where the learning rate is greater than 0.001 and the epoch number is less than 10.

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