TensorFlow Saving and Loading Models

Question 1

Which method saves a Keras model in HDF5 format?

Accepted Answer

model.save('model.h5')

Answer

Calling model.save() with an .h5 extension tells Keras to serialize the model in HDF5 format, storing architecture, weights, and optimizer state.

Question 2

Which function is used to reload a previously saved Keras model?

Accepted Answer

tf.keras.models.load_model()

Answer

tf.keras.models.load_model() is the standard API for loading a saved Keras model and reconstructing its architecture, weights, and optimizer.

Question 3

What does the SavedModel format store that HDF5 does not?

Accepted Answer

The full TensorFlow computation graph and serving signatures

Answer

SavedModel persists the complete TensorFlow graph (including traced tf.function endpoints and serving signatures) alongside weights, making it suitable for serving without Python.

Question 4

Which TensorFlow class is the modern API for saving and restoring model variables as checkpoints?

Accepted Answer

tf.train.Checkpoint

Answer

tf.train.Checkpoint is the TensorFlow 2.x mechanism for tracking and checkpointing variables, replacing the legacy tf.train.Saver.

Question 5

Which Keras callback automatically saves the model during training at configurable intervals?

Accepted Answer

tf.keras.callbacks.ModelCheckpoint

Answer

tf.keras.callbacks.ModelCheckpoint monitors training and saves the model (or just its weights) whenever a specified condition is met, such as improvement in validation loss.

TensorFlow Practice Test

TensorFlow Practice Test

TensorFlow Saving and Loading Models