Evaluation of fine-tuned models in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the accuracy of a fine-tuned model's predictions.

Prompt Engineering / GenAI

accuracy = sum(predictions == [1]) / len(predictions)

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

Amodel

Btrue_labels

Cpredictions

Dinputs

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing predictions to themselves instead of true labels.

Using the input data instead of labels.

Accuracy compares the model's predictions to the true labels to see how many are correct.

2fill in blank

medium

Complete the code to compute the loss of the fine-tuned model on test data.

Prompt Engineering / GenAI

loss = model.evaluate(test_inputs, [1])

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

Atrain_labels

Btest_inputs

Ctest_outputs

Dtrain_inputs

Attempts:

3 left

💡 Hint

Common Mistakes

Using training data instead of test data for evaluation.

Passing inputs instead of outputs as labels.

The model's loss is calculated by comparing predictions to the true test outputs (labels).

3fill in blank

hard

Fix the error in the code to generate predictions from the fine-tuned model.

Prompt Engineering / GenAI

predictions = model.[1](new_data)

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

Apredict

Btransform

Cevaluate

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit which trains the model instead of predicting.

Using evaluate which returns loss and metrics.

To get predictions, use the predict method on new data.

4fill in blank

hard

Fill both blanks to create a dictionary of accuracy and loss after evaluation.

Prompt Engineering / GenAI

results = {'accuracy': [1], 'loss': [2]

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

Aaccuracy_score(true_labels, predictions)

Bmodel.evaluate(test_inputs, test_labels)[0]

Cmodel.evaluate(test_inputs, test_labels)[1]

Daccuracy_score(predictions, true_labels)

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the order of loss and accuracy in the dictionary.

Using accuracy_score with arguments reversed.

Accuracy is computed by comparing true labels and predictions. Loss is the first value returned by model.evaluate.

5fill in blank

hard

Fill all three blanks to compute precision, recall, and F1 score for the fine-tuned model.

Prompt Engineering / GenAI

precision = precision_score(true_labels, [1])
recall = recall_score([2], predictions)
f1 = f1_score(true_labels, [3])

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

Apredictions

Btrue_labels

Dtest_labels

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping true labels and predictions in metric functions.

Using test_labels instead of true_labels.

Precision and F1 score compare true labels to predictions. Recall also compares true labels to predictions.

Practice

1. What is the main purpose of evaluating a fine-tuned model?

easy

A. To reduce the number of model layers

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

C. To speed up the training process

D. To increase the size of the training dataset

Solution

1. Step 1: Understand model evaluation

   Evaluation measures how well the model predicts on data it has not seen before.

2. Step 2: Identify the purpose of evaluation

   It helps us know if the model learned useful patterns or just memorized training data.

3. Final Answer:

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

4. Quick Check:

   Evaluation = performance on new data [OK]

Hint: Evaluation checks model on new data, not training data [OK]

Common Mistakes:

• Confusing evaluation with training
• Thinking evaluation changes model structure
• Believing evaluation increases data size

2. Which of the following is the correct way to evaluate a fine-tuned model in Python using TensorFlow?

easy

A. model.compile(optimizer='adam')

B. model.train(test_data, test_labels)

C. model.predict(train_data)

D. model.evaluate(test_data, test_labels)

Solution

1. Step 1: Recall TensorFlow evaluation method

   TensorFlow models use model.evaluate() to measure performance on test data.

2. Step 2: Identify correct usage

   model.evaluate(test_data, test_labels) returns loss and metrics on unseen data.

3. Final Answer:

   model.evaluate(test_data, test_labels) -> Option D

4. Quick Check:

   Use model.evaluate() for testing [OK]

Hint: Use model.evaluate() with test data for evaluation [OK]

Common Mistakes:

• Using model.train() instead of evaluate
• Calling predict() without labels for evaluation
• Confusing compile() with evaluation

3. Given the code below, what will be the output of print(loss, accuracy)?

loss, accuracy = model.evaluate(x_test, y_test)
print(loss, accuracy)

medium

A. The loss value and accuracy metric on the test set

B. The training loss and accuracy values

C. A syntax error because evaluate returns only one value

D. The predicted labels for x_test

Solution

1. Step 1: Understand model.evaluate() output

   It returns loss and metrics (like accuracy) on the test data.

2. Step 2: Analyze the print statement

   Printing loss, accuracy shows these two values from evaluation.

3. Final Answer:

   The loss value and accuracy metric on the test set -> Option A

4. Quick Check:

   evaluate() returns loss and accuracy [OK]

Hint: model.evaluate() returns loss and metrics tuple [OK]

Common Mistakes:

• Thinking evaluate returns training metrics
• Assuming evaluate returns predictions
• Believing evaluate returns only one value

4. You ran model.evaluate(x_test) but got an error. What is the likely cause?

medium

A. The model is not compiled

B. The test data x_test is empty

C. Missing the true labels y_test in the evaluate call

D. The model has too many layers

Solution

1. Step 1: Check evaluate method requirements

   model.evaluate() needs both input data and true labels to compute metrics.

2. Step 2: Identify missing argument

   Calling model.evaluate(x_test) misses y_test, causing an error.

3. Final Answer:

   Missing the true labels y_test in the evaluate call -> Option C

4. Quick Check:

   evaluate() needs inputs and labels [OK]

Hint: Always pass both data and labels to evaluate() [OK]

Common Mistakes:

• Forgetting to pass labels to evaluate()
• Assuming evaluate works with inputs only
• Ignoring model compilation status

5. You fine-tuned two models and got these evaluation results on the same test set:

• Model A: loss=0.25, accuracy=0.90
• Model B: loss=0.20, accuracy=0.85

Which model should you choose and why?

hard

A. Model A, because it has higher accuracy which is more important than loss

B. Model B, because it has lower loss indicating better overall fit

C. Model A, because loss and accuracy must both be higher

D. Model B, because accuracy is less important than loss

Solution

1. Step 1: Understand evaluation metrics

   Accuracy shows correct predictions percentage; loss shows error magnitude.

2. Step 2: Compare models on accuracy and loss

   Model A has higher accuracy (0.90) but slightly higher loss (0.25) than Model B.

3. Step 3: Decide based on goal

   For classification, accuracy is usually more important to pick the better model.

4. Final Answer:

   Model A, because it has higher accuracy which is more important than loss -> Option A

5. Quick Check:

   Higher accuracy preferred for classification [OK]

Hint: Pick model with higher accuracy for classification tasks [OK]

Common Mistakes:

• Choosing model with lower loss but worse accuracy
• Ignoring accuracy when loss differs
• Assuming loss always trumps accuracy