TensorFlow Advanced TensorFlow Techniques 2

Question 1

What is the purpose of tf.keras.callbacks.LambdaCallback?

Accepted Answer

Allows arbitrary functions to be called at training events without writing a full callback class

Answer

LambdaCallback lets you pass Python functions for events like on_epoch_end or on_batch_begin without subclassing tf.keras.callbacks.Callback.

Question 2

What does tf.config.list_physical_devices('GPU') return?

Accepted Answer

A list of available GPU devices on the system

Answer

tf.config.list_physical_devices('GPU') returns a list of PhysicalDevice objects representing each GPU visible to TensorFlow.

Question 3

Which strategy distributes training across multiple GPUs on a single machine?

Accepted Answer

tf.distribute.MirroredStrategy

Answer

MirroredStrategy replicates the model on each GPU and uses all-reduce to synchronize gradient updates across devices.

Question 4

What is the purpose of tf.debugging.assert_shapes()?

Accepted Answer

Verifies tensor shapes match expectations and raises an error if not

Answer

tf.debugging.assert_shapes() checks that tensors have specified shapes, raising an InvalidArgument error if they don't match.

Question 5

What does tf.keras.backend.clear_session() do?

Accepted Answer

Resets Keras state including the global graph and frees GPU memory

Answer

clear_session() destroys the current Keras session, resetting all state and freeing associated memory, useful when training multiple models in a loop.

TensorFlow Practice Test

TensorFlow Practice Test

TensorFlow Advanced TensorFlow Techniques 2