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

Subclassing pre-trained models for fine-tuning

Answer

Extending Keras with C++ layers

Answer

Inheriting optimizer behavior

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

Define a function and wrap with tf.keras.layer()

Answer

Add layers using tf.custom_layer()

Answer

Use tf.keras.layers.Lambda()

Question 3

What does tf.Variable differ from tf.constant?

Accepted Answer

Variables are mutable and track gradients; constants are immutable

Answer

Variables are stored on CPU; constants on GPU

Answer

Variables hold integers; constants hold floats

Answer

Variables are shared across models; constants are local

Question 4

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

Accepted Answer

TensorFlow Hub

Answer

TensorFlow Zoo

Answer

TF Model Garden only

Answer

Keras Applications only

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

Controls weight reduction during pruning

Answer

Defines gradient reduction strategy

Answer

Sets learning rate reduction behavior

Question 6

What is tf.keras.layers.Lambda used for?

Accepted Answer

Wrapping arbitrary Python/TF expressions as a Keras layer

Answer

Creating lambda functions in TF graphs

Answer

Defining anonymous loss functions

Answer

Applying regularization functions