TensorFlow Advanced TensorFlow Techniques

Question 1

What is the purpose of tf.keras.Model subclassing?

Accepted Answer

Creating custom model architectures with arbitrary forward pass logic

Answer

Model subclassing lets you define the forward pass in a call() method, enabling architectures with conditional logic, loops, and multiple paths.

Question 2

How do you create a custom Keras layer in TensorFlow?

Accepted Answer

Subclass tf.keras.layers.Layer and implement build() and call()

Answer

Custom layers inherit from tf.keras.layers.Layer; build() creates weights and call() defines the computation.

Question 3

What does tf.Variable differ from tf.constant?

Accepted Answer

Variables are mutable and track gradients; constants are immutable

Answer

tf.Variable holds mutable state whose values can be updated during training, while tf.constant creates an immutable tensor.

Question 4

Which TensorFlow module provides pre-built reusable model components called modules?

Accepted Answer

TensorFlow Hub

Answer

TensorFlow Hub hosts pre-trained models and reusable SavedModel components that can be downloaded and integrated into custom models.

Question 5

What is the role of tf.keras.losses.Reduction in custom loss functions?

Accepted Answer

Specifies how per-sample losses are aggregated (sum, mean, or none)

Answer

The Reduction enum controls whether the loss over a batch is summed, averaged, or left as a per-sample array.

TensorFlow Practice Test