TensorFlow Saving and Loading Models 2

Question 1

What is the default save format when calling model.save('my_model') without a file extension in TensorFlow 2.x?

Accepted Answer

SavedModel directory format

Answer

In TensorFlow 2.x, omitting a file extension causes model.save() to default to SavedModel format, creating a directory with assets, variables, and a saved_model.pb file.

Question 2

Which ModelCheckpoint argument ensures only the best-performing checkpoint is kept on disk?

Accepted Answer

save_best_only=True

Answer

Setting save_best_only=True in ModelCheckpoint overwrites the checkpoint only when the monitored metric improves, keeping disk usage minimal.

Question 3

What is the primary role of tf.train.CheckpointManager?

Accepted Answer

Limiting the number of checkpoints kept with max_to_keep

Answer

tf.train.CheckpointManager wraps a tf.train.Checkpoint and enforces a max_to_keep limit, automatically deleting older checkpoints.

Question 4

Which method on tf.train.Checkpoint restores saved variable values?

Accepted Answer

.restore()

Answer

Calling .restore(checkpoint_path) on a tf.train.Checkpoint object reassigns saved tensor values back to the tracked variables.

Question 5

What does the save_freq parameter in ModelCheckpoint control?

Accepted Answer

How often (in epochs or batches) a checkpoint is written

Answer

save_freq accepts 'epoch' to save after each epoch, or an integer to save every N batches, giving fine-grained control over checkpoint cadence.

TensorFlow Practice Test

TensorFlow Practice Test

TensorFlow Saving and Loading Models 2