Training and validation loss tracking in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to initialize the training loss list.

PyTorch

train_losses = [1]

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

A{}

B[]

C0

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Using a dictionary {} instead of a list.

Initializing with 0 or None which cannot store multiple values.

We use an empty list [] to store training loss values over epochs.

2fill in blank

medium

Complete the code to calculate the average training loss for an epoch.

PyTorch

epoch_train_loss = sum(train_losses) / [1]

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

Alen(train_losses)

Bmax(train_losses)

Cmin(train_losses)

Dtrain_losses

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by the maximum or minimum loss instead of the count.

Dividing by the list itself which causes an error.

To get the average loss, divide the sum by the number of loss values using len(train_losses).

3fill in blank

hard

Fix the error in the code to append validation loss after each epoch.

PyTorch

validation_losses.[1](val_loss)

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

Aadd

Binsert

Cappend

Dextend

Attempts:

3 left

💡 Hint

Common Mistakes

Using add which is not a list method.

Using extend which expects an iterable.

Using insert without specifying an index.

Use append to add a single loss value to the list.

4fill in blank

hard

Fill both blanks to compute and store average training and validation losses.

PyTorch

avg_train_loss = sum(train_losses) [1] len(train_losses)
avg_val_loss = sum(validation_losses) [2] len(validation_losses)

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

A/

B*

C-

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Using multiplication or addition instead of division.

Using subtraction which gives incorrect results.

Divide the sum of losses by the count to get the average for both training and validation.

5fill in blank

hard

Fill all three blanks to track losses during training and validation phases.

PyTorch

for epoch in range(num_epochs):
    model.train()
    train_loss = 0
    for data, target in train_loader:
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.[1]()
        optimizer.step()
        train_loss += loss.item()
    train_losses.[2](train_loss / len(train_loader))

    model.eval()
    val_loss = 0
    with torch.no_grad():
        for data, target in val_loader:
            output = model(data)
            loss = criterion(output, target)
            val_loss += loss.item()
    validation_losses.[3](val_loss / len(val_loader))

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

Abackward

Bappend

Dstep

Attempts:

3 left

💡 Hint

Common Mistakes

Using step instead of backward for gradients.

Using step or wrong methods to add losses to lists.

Call loss.backward() to compute gradients, then append average losses to the lists.

Practice

1. Why do we track both training and validation loss during model training in PyTorch?

easy

A. To speed up the training process

B. To increase the batch size automatically

C. To monitor if the model is learning and generalizing well to new data

D. To reduce the number of model parameters

Solution

1. Step 1: Understand the purpose of training loss

   Training loss shows how well the model fits the training data during learning.

2. Step 2: Understand the purpose of validation loss

   Validation loss shows how well the model performs on unseen data, indicating generalization.

3. Final Answer:

   To monitor if the model is learning and generalizing well to new data -> Option C

4. Quick Check:

   Training and validation loss track learning and generalization [OK]

Hint: Training loss = fit, validation loss = generalization [OK]

Common Mistakes:

• Thinking validation loss speeds up training
• Confusing loss tracking with batch size changes
• Assuming loss tracking reduces parameters

2. Which PyTorch method should you call before starting the validation phase to ensure correct behavior?

easy

A. model.train()

B. model.eval()

C. model.validate()

D. model.freeze()

Solution

1. Step 1: Recall PyTorch modes

   model.train() sets the model to training mode, enabling dropout and batch norm updates.

2. Step 2: Identify validation mode

   model.eval() sets the model to evaluation mode, disabling dropout and batch norm updates for stable validation.

3. Final Answer:

   model.eval() -> Option B

4. Quick Check:

   Use model.eval() before validation [OK]

Hint: Use model.eval() to disable training-only layers [OK]

Common Mistakes:

• Using model.train() during validation
• Assuming model.validate() exists
• Confusing freeze() with eval()

3. Given the code below, what will be the printed average training loss after one epoch?

train_losses = []
for inputs, targets in train_loader:
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()
    train_losses.append(loss.item())
avg_train_loss = sum(train_losses) / len(train_losses)
print(f"Average training loss: {avg_train_loss:.4f}")

Assuming losses per batch are [0.8, 0.6, 0.4], what is the output?

medium

A. Average training loss: 0.4000

B. Average training loss: 1.8000

C. Average training loss: 0.0000

D. Average training loss: 0.6000

Solution

1. Step 1: Sum the batch losses

   Sum = 0.8 + 0.6 + 0.4 = 1.8

2. Step 2: Calculate average loss

   Average = 1.8 / 3 = 0.6

3. Final Answer:

   Average training loss: 0.6000 -> Option D

4. Quick Check:

   Sum 1.8 / 3 batches = 0.6 [OK]

Hint: Average = sum of losses ÷ number of batches [OK]

Common Mistakes:

• Printing sum instead of average
• Dividing by wrong number of batches
• Using loss tensor instead of loss.item()

4. What is the bug in this code snippet for calculating average validation loss?

model.eval()
val_losses = []
for inputs, targets in val_loader:
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    val_losses.append(loss)
avg_val_loss = sum(val_losses) / len(val_losses)
print(f"Validation loss: {avg_val_loss:.4f}")

medium

A. Appending loss tensor instead of loss.item()

B. Not calling model.train() before validation

C. Using model.eval() disables loss calculation

D. Forgetting to zero gradients before validation

Solution

1. Step 1: Check what is appended to val_losses

   The code appends loss tensors, not scalar values.

2. Step 2: Understand sum behavior on tensors list

   Summing loss tensors retains computation graphs from each forward pass, preventing memory release and causing high memory usage over multiple epochs.

3. Final Answer:

   Appending loss tensor instead of loss.item() -> Option A

4. Quick Check:

   Use loss.item() to get scalar loss [OK]

Hint: Append loss.item(), not loss tensor, for correct averaging [OK]

Common Mistakes:

• Appending loss tensor directly
• Confusing model.train() and model.eval() roles
• Expecting loss calculation to stop in eval mode

5. You want to track training and validation loss per epoch and plot them to check overfitting. Which approach correctly implements this in PyTorch?

hard

A. Use separate lists for training and validation losses, append average loss each epoch, then plot both lists

B. Append batch losses directly to one list for both training and validation, then plot

C. Only track training loss since validation loss is not useful

D. Call model.train() during validation to get accurate loss

Solution

1. Step 1: Understand loss tracking per epoch

   We need to store average losses per epoch separately for training and validation.

2. Step 2: Use separate lists and append averages

   Appending average loss per epoch to separate lists allows clear plotting and comparison.

3. Final Answer:

   Use separate lists for training and validation losses, append average loss each epoch, then plot both lists -> Option A

4. Quick Check:

   Track average losses per epoch separately [OK]

Hint: Track average losses per epoch separately for clear plots [OK]

Common Mistakes:

• Mixing batch losses in one list
• Ignoring validation loss tracking
• Using model.train() during validation