Progress tracking and reporting in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the training loss after each epoch.

Agentic AI

for epoch in range(5):
    loss = model.train_one_epoch(data)
    print('Epoch', epoch, 'Loss:', [1])

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

Aepoch

Bloss

Cdata

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Printing the epoch number instead of the loss.

Printing the model object.

The variable loss holds the training loss for the current epoch, so printing it shows progress.

2fill in blank

medium

Complete the code to update the progress bar with the current accuracy.

Agentic AI

for batch in data_loader:
    predictions = model.predict(batch)
    accuracy = compute_accuracy(predictions, batch.labels)
    progress_bar.update([1]=accuracy)

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

Ascore

Baccuracy

Cvalue

Dprogress

Attempts:

3 left

💡 Hint

Common Mistakes

Using the variable name accuracy as the parameter name.

Using score which is undefined.

The progress bar's update method expects the current progress as the value argument.

3fill in blank

hard

Fix the error in the code to correctly log the validation accuracy after each epoch.

Agentic AI

for epoch in range(10):
    val_acc = evaluate(model, val_data)
    logger.log('Validation Accuracy:', [1])

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

Aval_acc()

Bmodel

Cevaluate

Dval_acc

Attempts:

3 left

💡 Hint

Common Mistakes

Calling val_acc as a function.

Logging the model object instead of accuracy.

The variable val_acc stores the accuracy value. Calling it as a function causes an error.

4fill in blank

hard

Fill both blanks to create a dictionary that maps epoch numbers to their loss values.

Agentic AI

loss_history = { [1]: [2] for [1] in range(1, 6) }

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

Aepoch

Bloss

Cval_loss

Daccuracy

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same variable for both key and value.

Using validation loss or accuracy instead of training loss.

The dictionary keys are epoch numbers stored in epoch, and values are the corresponding loss values.

5fill in blank

hard

Fill all three blanks to filter and store epochs where accuracy is above 0.8.

Agentic AI

high_acc_epochs = { [1]: [2] for [1] in epochs if [3][[1]] > 0.8 }

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

Aepoch

Baccuracy

Cacc_dict

Dloss

Attempts:

3 left

💡 Hint

Common Mistakes

Using loss instead of accuracy.

Using wrong variable names for dictionary or keys.

We use epoch as the key, accuracy as the value, and check if acc_dict[epoch] is greater than 0.8.

Practice

1. What is the main purpose of progress tracking during machine learning model training?

easy

A. To record how the model improves over time

B. To increase the size of the training data

C. To change the model architecture automatically

D. To speed up the training hardware

Solution

1. Step 1: Understand progress tracking

   Progress tracking means keeping a record of how well the model is learning as it trains.

2. Step 2: Identify the main goal

   The goal is to see improvements over time, not to change data size or hardware.

3. Final Answer:

   To record how the model improves over time -> Option A

4. Quick Check:

   Progress tracking = record improvement [OK]

Hint: Progress tracking = recording learning progress [OK]

Common Mistakes:

• Confusing progress tracking with data augmentation
• Thinking it changes model structure automatically
• Assuming it speeds up hardware

2. Which of the following is the correct way to log training loss after each epoch in Python?

easy

A. print('Loss:', loss)

B. log('Loss:' + loss)

C. print('Loss:' loss)

D. echo 'Loss:' loss

Solution

1. Step 1: Check Python print syntax

   In Python, print() requires arguments separated by commas or concatenated as strings.

2. Step 2: Validate each option

   print('Loss:', loss) uses print with a comma, which is correct. log('Loss:' + loss) uses undefined log function. print('Loss:' loss) misses a comma. echo 'Loss:' loss uses echo, which is not Python.

3. Final Answer:

   print('Loss:', loss) -> Option A

4. Quick Check:

   Correct print syntax = print('Loss:', loss) [OK]

Hint: Use print() with commas to separate text and variables [OK]

Common Mistakes:

• Missing commas in print statements
• Using non-Python functions like echo or log
• Concatenating strings without conversion

3. Given the code below, what will be printed after training for 3 epochs?

losses = []
for epoch in range(3):
    loss = 1 / (epoch + 1)
    losses.append(loss)
    print(f'Epoch {epoch+1}, Loss: {loss:.2f}')
print('Final losses:', losses)

medium

A. Epoch 1, Loss: 1.00
Epoch 2, Loss: 0.50
Epoch 3, Loss: 0.33
Final losses: [1, 2, 3]

B. Epoch 1, Loss: 0.00
Epoch 2, Loss: 0.50
Epoch 3, Loss: 0.33
Final losses: [0, 0.5, 0.3333333333333333]

C. Epoch 1, Loss: 1.00
Epoch 2, Loss: 0.50
Epoch 3, Loss: 0.33
Final losses: [1.0, 0.5, 0.3333333333333333]

D. Epoch 1, Loss: 1.00
Epoch 2, Loss: 0.50
Epoch 3, Loss: 0.33
Final losses: [1, 0.5, 0.33]

Solution

1. Step 1: Calculate loss values for each epoch

   Epoch 1: 1/(1) = 1.0, Epoch 2: 1/(2) = 0.5, Epoch 3: 1/(3) ≈ 0.3333

2. Step 2: Check printed output and final list

   Print shows formatted loss with 2 decimals. Final losses list stores full float values.

3. Final Answer:

   Epoch 1, Loss: 1.00 Epoch 2, Loss: 0.50 Epoch 3, Loss: 0.33 Final losses: [1.0, 0.5, 0.3333333333333333] -> Option C

4. Quick Check:

   Loss calculation and print formatting = Epoch 1, Loss: 1.00 Epoch 2, Loss: 0.50 Epoch 3, Loss: 0.33 Final losses: [1.0, 0.5, 0.3333333333333333] [OK]

Hint: Calculate loss per epoch and check print formatting carefully [OK]

Common Mistakes:

• Confusing integer division with float division
• Rounding losses in the list incorrectly
• Misreading the range function output

4. The following code is meant to track accuracy after each training epoch, but it throws an error. What is the error?

accuracies = []
for epoch in range(5):
    accuracy = 0.8 + epoch * 0.03
    accuracies.append(accuracy)
print('Accuracies:', accuracies)

medium

A. SyntaxError due to missing colon in for loop

B. No error; code runs correctly

C. TypeError because accuracy is not a number

D. IndexError from accessing out-of-range list element

Solution

1. Step 1: Review the code syntax and logic

   The for loop has a colon, accuracy is calculated as a float, and appended to the list.

2. Step 2: Check for runtime errors

   No invalid operations or out-of-range accesses occur.

3. Final Answer:

   No error; code runs correctly -> Option B

4. Quick Check:

   Code syntax and logic correct = No error; code runs correctly [OK]

Hint: Check for syntax and type errors carefully [OK]

Common Mistakes:

• Assuming missing colon when it is present
• Confusing variable types
• Expecting index errors without list access

5. You want to create a report that shows both training loss and accuracy after each epoch in a clear table format. Which approach best helps you track and report this progress effectively?

hard

A. Store losses and accuracies in separate lists and print them after training

B. Print loss and accuracy inside the training loop without storing values

C. Only track loss since accuracy is less important

D. Use a dictionary to store epoch as key and a tuple of (loss, accuracy) as value, then print a formatted table after training

Solution

1. Step 1: Understand the need for clear reporting

   Clear reports require organized data storage and formatted output.

2. Step 2: Evaluate each option

   Store losses and accuracies in separate lists and print them after training stores separately but may be harder to align epochs. Print loss and accuracy inside the training loop without storing values prints without storing, losing history. Use a dictionary to store epoch as key and a tuple of (loss, accuracy) as value, then print a formatted table after training uses a dictionary to link epochs with both metrics, enabling clear table printing. Only track loss since accuracy is less important ignores accuracy, which is important.

3. Final Answer:

   Use a dictionary to store epoch as key and a tuple of (loss, accuracy) as value, then print a formatted table after training -> Option D

4. Quick Check:

   Organized storage + formatted report = Use a dictionary to store epoch as key and a tuple of (loss, accuracy) as value, then print a formatted table after training [OK]

Hint: Use dictionary with epoch keys for clear progress reports [OK]

Common Mistakes:

• Not storing metrics together per epoch
• Printing inside loop without history
• Ignoring accuracy tracking