Validation loop in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the model to evaluation mode before validation.

PyTorch

model.[1]()

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

Aeval

Btrain

Cfit

Dvalidate

Attempts:

3 left

💡 Hint

Common Mistakes

Using model.train() instead of model.eval() during validation.

Calling a non-existent method like model.validate().

Calling model.eval() sets the model to evaluation mode, which disables dropout and batch normalization updates during validation.

2fill in blank

medium

Complete the code to disable gradient calculation during validation.

PyTorch

with torch.[1]():
    # validation code here

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

Ano_grad

Bgrad

Cenable_grad

Dautograd

Attempts:

3 left

💡 Hint

Common Mistakes

Not disabling gradients during validation, causing unnecessary computation.

Using torch.grad() which does not exist.

Using torch.no_grad() disables gradient calculation, which saves memory and computations during validation.

3fill in blank

hard

Fix the error in the validation loop to correctly accumulate the total loss.

PyTorch

total_loss = 0
for inputs, labels in val_loader:
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    total_loss [1] loss.item()

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

A=

B*=

C-=

D+=

Attempts:

3 left

💡 Hint

Common Mistakes

Using = which overwrites total_loss each iteration.

Using -= or *= which changes the meaning incorrectly.

Use total_loss += loss.item() to add each batch's loss to the total loss.

4fill in blank

hard

Fill both blanks to compute the average validation loss after the loop.

PyTorch

avg_loss = total_loss [1] len([2])

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

A/

B*

Cval_loader

Dtrain_loader

Attempts:

3 left

💡 Hint

Common Mistakes

Using train_loader length instead of val_loader.

Multiplying instead of dividing.

Divide the total loss by the number of batches in val_loader to get the average loss.

5fill in blank

hard

Fill all three blanks to complete the validation loop with accuracy calculation.

PyTorch

correct = 0
total = 0
with torch.no_grad():
    for inputs, labels in [1]:
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, [2])
        total += labels.size([3])
        correct += (predicted == labels).sum().item()

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

Aval_loader

Bdim=1

C0

Dtrain_loader

Attempts:

3 left

💡 Hint

Common Mistakes

Using train_loader instead of val_loader.

Using wrong dimension for torch.max or labels.size.

Use val_loader to loop over validation data, dim=1 to get max over class scores, and 0 to get batch size dimension.

Practice

1. What is the main purpose of a validation loop in PyTorch?

easy

A. To update the model weights during training

B. To check how well the model performs on unseen data

C. To load the training dataset

D. To save the model to disk

Solution

1. Step 1: Understand the role of validation

   The validation loop is used to test the model on new data that it has not seen during training.

2. Step 2: Differentiate from training

   Unlike training, validation does not update model weights but measures performance like accuracy or loss.

3. Final Answer:

   To check how well the model performs on unseen data -> Option B

4. Quick Check:

   Validation loop = test model on new data [OK]

Hint: Validation loop tests model without changing weights [OK]

Common Mistakes:

• Confusing validation with training
• Thinking validation updates model weights
• Mixing validation with data loading

2. Which PyTorch code snippet correctly sets the model for validation?

easy

A. model.eval()

B. model.train()

C. model.validate()

D. model.test()

Solution

1. Step 1: Recall PyTorch model modes

   In PyTorch, model.eval() sets the model to evaluation mode, disabling dropout and batch norm updates.

2. Step 2: Identify correct method for validation

   Validation requires evaluation mode, so model.eval() is correct; others do not exist or are for training.

3. Final Answer:

   model.eval() -> Option A

4. Quick Check:

   Use model.eval() for validation [OK]

Hint: Use model.eval() to disable training behaviors [OK]

Common Mistakes:

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

3. What will be the output of the following validation loop snippet?

model.eval()
correct = 0
with torch.no_grad():
    for inputs, labels in val_loader:
        outputs = model(inputs)
        predicted = outputs.argmax(dim=1)
        correct += (predicted == labels).sum().item()
accuracy = correct / len(val_loader.dataset)
print(f"Accuracy: {accuracy:.2f}")

medium

A. Prints loss value instead of accuracy

B. Raises an error because of missing optimizer

C. Prints the accuracy of the model on validation data

D. Prints training accuracy instead of validation accuracy

Solution

1. Step 1: Analyze the code logic

   The code sets model to eval mode, disables gradient calculation, and counts correct predictions over validation data.

2. Step 2: Understand the output calculation

   Accuracy is computed as correct predictions divided by total validation samples, then printed.

3. Final Answer:

   Prints the accuracy of the model on validation data -> Option C

4. Quick Check:

   Validation loop code prints accuracy [OK]

Hint: Check if code counts correct predictions for accuracy [OK]

Common Mistakes:

• Expecting loss instead of accuracy
• Thinking optimizer is needed in validation
• Confusing training and validation accuracy

4. Identify the error in this validation loop code:

model.eval()
for inputs, labels in val_loader:
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    total_loss += loss.item()

medium

A. Not zeroing gradients before backward

B. Not using model.train() before loop

C. Missing optimizer.step() after loss.backward()

D. Calling loss.backward() during validation

Solution

1. Step 1: Understand validation loop purpose

   Validation should not update model weights or compute gradients, so backward() is not needed.

2. Step 2: Identify the error

   Calling loss.backward() computes gradients, which is incorrect in validation; it wastes resources and may cause errors.

3. Final Answer:

   Calling loss.backward() during validation -> Option D

4. Quick Check:

   No backward() in validation loop [OK]

Hint: No backward() in validation, only in training [OK]

Common Mistakes:

• Running backward() in validation
• Confusing train and eval modes
• Forgetting torch.no_grad() in validation

5. You want to add accuracy tracking in your validation loop. Which code snippet correctly computes and updates accuracy inside the loop?

model.eval()
total_correct = 0
total_samples = 0
with torch.no_grad():
    for inputs, labels in val_loader:
        outputs = model(inputs)
        predicted = outputs.argmax(dim=1)
        # What goes here?
accuracy = total_correct / total_samples
print(f"Validation Accuracy: {accuracy:.2f}")

hard

A. total_correct += (predicted == labels).sum().item()
total_samples += labels.size(0)

B. total_correct += predicted.sum().item()
total_samples += labels.size(0)

C. total_correct += (predicted != labels).sum().item()
total_samples += labels.size(0)

D. total_correct += (predicted == labels).sum()
total_samples += len(val_loader)

Solution

1. Step 1: Calculate correct predictions

   Use (predicted == labels).sum().item() to count correct predictions as a Python number.

2. Step 2: Update total samples count

   Use labels.size(0) to add the batch size to total samples for accurate accuracy calculation.

3. Final Answer:

   total_correct += (predicted == labels).sum().item() total_samples += labels.size(0) -> Option A

4. Quick Check:

   Sum correct matches and batch size for accuracy [OK]

Hint: Sum correct predictions and batch size for accuracy [OK]

Common Mistakes:

• Summing predicted values instead of correct matches
• Using != instead of == for correct count
• Adding batch count incorrectly as len(val_loader)