TensorFlow Test 3

Question 1

Which TensorFlow utility helps you measure per-class precision and recall on a test set?

Accepted Answer

sklearn.metrics.classification_report (used alongside TF)

Answer

scikit-learn's classification_report is commonly paired with TensorFlow predictions to show per-class precision, recall, and F1.

Question 2

When testing a SavedModel loaded with tf.saved_model.load(), how do you run inference?

Accepted Answer

Access the serving function via model.signatures and call it

Answer

A raw SavedModel exposes its computation through signatures (e.g., model.signatures['serving_default'](input=tensor)).

Question 3

What does tf.keras.metrics.MeanAbsoluteError() measure in a regression test?

Accepted Answer

Average absolute difference between predictions and targets

Answer

MAE averages the absolute differences |y_pred - y_true| over all samples, giving an interpretable error in the original units.

Question 4

What is the correct way to reset a Keras metric object between test batches?

Accepted Answer

Call metric.reset_state()

Answer

reset_state() clears accumulated state so the metric can start fresh for a new evaluation pass.

Question 5

During model testing, which TensorFlow context manager prevents unnecessary gradient computation?

Accepted Answer

No special context is needed; use inference_mode in PyTorch instead

Answer

TensorFlow does not track gradients outside GradientTape by default, so inference requires no special context unlike PyTorch's torch.no_grad().

TensorFlow Practice Test