TensorFlow Advanced TensorFlow Techniques 3

Question 1

What is the purpose of tf.keras.utils.plot_model()?

Accepted Answer

Generates a visualization of the model architecture as an image

Answer

Plots training loss curves

Answer

Visualizes weight distributions

Answer

Creates confusion matrix plots

Question 2

What is the main advantage of using XLA (Accelerated Linear Algebra) with TensorFlow?

Accepted Answer

Compiles computation graphs into optimized machine code for faster execution

Answer

Accelerates data loading

Answer

Provides hardware-specific matrix libraries

Answer

Speeds up Python preprocessing

Question 3

Which TensorFlow method computes gradients of a loss with respect to variables?

Accepted Answer

tape.gradient(loss, variables) inside tf.GradientTape

Answer

tf.compute_gradients()

Answer

optimizer.gradients()

Answer

model.backprop()

Question 4

What does tf.keras.applications provide?

Accepted Answer

Pre-built, pre-trained models like ResNet, VGG, and MobileNet for transfer learning

Answer

Application deployment tools

Answer

App store integration for TFLite

Answer

API connection utilities

Question 5

What is the purpose of tf.keras.metrics.Mean()?

Accepted Answer

Tracks the running average of a scalar value across multiple update calls

Answer

Computes the mean of a tensor in a single call

Answer

Averages model weights across epochs

Answer

Calculates mean absolute error loss

Question 6

Which TensorFlow 2 feature replaces Session.run() from TF1?

Accepted Answer

Eager execution (direct tensor evaluation)

Answer

tf.compat.v1.Session

Answer

tf.run()

Answer

tf.evaluate()