TensorFlow Test 2

Question 1

Which TensorFlow function is used to evaluate a model on test data and return loss and metrics?

Accepted Answer

model.evaluate()

Answer

model.evaluate() runs forward passes on the provided dataset and returns the loss and any compiled metrics.

Question 2

When calling model.evaluate(), what does setting verbose=0 do?

Accepted Answer

Suppresses all output to stdout

Answer

verbose=0 silences the progress bar and per-batch logging during evaluation.

Question 3

In TensorFlow, which dataset split should NEVER be used to tune hyperparameters?

Accepted Answer

Test set

Answer

The test set must remain unseen during development to provide an unbiased final performance estimate.

Question 4

What is the purpose of tf.data.Dataset.batch() when preparing test data?

Accepted Answer

Groups samples into mini-batches for efficient evaluation

Answer

batch() groups consecutive elements into tensors so the GPU can process multiple samples in parallel during evaluation.

Question 5

Which Keras callback is most useful for logging metrics to TensorBoard during evaluation?

Accepted Answer

TensorBoard

Answer

The TensorBoard callback writes loss and metric summaries to log files that TensorBoard can visualize.

TensorFlow Practice Test