Warmup strategies in PyTorch - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why use a learning rate warmup in training?

Which of the following best explains the main reason for using a learning rate warmup at the start of training a neural network?

A To prevent the model from diverging by starting with a very high learning rate

B To reduce the total training time by skipping early epochs

C To gradually increase the learning rate to avoid large updates that can destabilize early training

D To immediately reach the maximum learning rate for faster convergence

💡 Hint

Think about how sudden large updates affect a new model's weights.

A warmup gradually increases the learning rate from a small value to the target value to avoid large, unstable updates early in training.

❓ Predict Output

intermediate

Output of learning rate scheduler with warmup

What will be the learning rate printed at epoch 3 in the following PyTorch code?

PyTorch

import torch
from torch.optim import SGD
from torch.optim.lr_scheduler import LambdaLR

optimizer = SGD([torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))], lr=0.1)
warmup_epochs = 5

def lr_lambda(epoch):
    if epoch < warmup_epochs:
        return (epoch + 1) / warmup_epochs
    else:
        return 1.0

scheduler = LambdaLR(optimizer, lr_lambda=lr_lambda)

for epoch in range(7):
    scheduler.step()
    print(f"Epoch {epoch}: lr = {optimizer.param_groups[0]['lr']}")

A Epoch 3: lr = 0.06

B Epoch 3: lr = 0.04

C Epoch 3: lr = 0.1

D Epoch 3: lr = 0.08

💡 Hint

Calculate (epoch + 1) / warmup_epochs * base_lr for epoch 3.

At epoch 3, lr_lambda returns (3+1)/5 = 0.8, so lr = 0.8 * 0.1 = 0.08.

❓ Model Choice

advanced

Choosing warmup strategy for transformer training

You are training a transformer model on a large text dataset. Which warmup strategy is most suitable to stabilize training and improve final accuracy?

A Linear warmup followed by cosine decay

B Step warmup with abrupt jumps in learning rate

C Exponential warmup with sudden drop after warmup

D No warmup, start with a fixed learning rate

💡 Hint

Consider smooth transitions in learning rate for stable training.

Linear warmup gradually increases the learning rate, and cosine decay smoothly reduces it, which is effective for transformers.

❓ Hyperparameter

advanced

Determining warmup steps for a training schedule

If you have a total of 100 epochs and want to use a warmup phase that lasts 10% of training, how many warmup steps should you set?

A 5 steps

B 10 steps

C 20 steps

D 50 steps

💡 Hint

Calculate 10% of 100 epochs.

10% of 100 epochs is 10 epochs, so warmup steps should be 10.

❓ Metrics

expert

Effect of warmup on training loss curve

During training with and without learning rate warmup, which difference in the training loss curve is expected?

A With warmup, loss starts higher and decreases smoothly; without warmup, loss fluctuates or spikes early

B With warmup, loss decreases abruptly; without warmup, loss decreases smoothly

C With warmup, loss increases steadily; without warmup, loss decreases steadily

D With warmup, loss remains constant; without warmup, loss decreases steadily

💡 Hint

Think about stability of updates at the start of training.

Warmup prevents large early updates, so loss decreases smoothly. Without warmup, large updates can cause spikes or fluctuations.