Keras as TensorFlow's high-level API - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Keras in the context of TensorFlow?

Keras is a simple and user-friendly high-level API within TensorFlow that helps you build and train neural networks easily without dealing with low-level details.

beginner

Why do we use Keras instead of raw TensorFlow code?

Keras makes building models faster and easier by providing simple building blocks like layers and models, so you can focus on designing your network instead of complex code.

intermediate

What are the two main ways to build models in Keras?

You can build models using the Sequential API, which stacks layers one after another, or the Functional API, which allows more flexible and complex model designs.

beginner

How does Keras handle training a model?

Keras uses the .compile() method to set the optimizer, loss function, and metrics, and the .fit() method to train the model on your data, showing progress with loss and accuracy.

intermediate

What is the role of callbacks in Keras?

Callbacks are tools that let you customize what happens during training, like saving the best model, stopping early if no improvement, or adjusting learning rates.

Which of the following best describes Keras in TensorFlow?

A A low-level library for matrix operations

B A hardware accelerator

C A data visualization tool

D A high-level API for building and training neural networks

Keras is designed as a high-level API to simplify building and training neural networks within TensorFlow.

What method do you use in Keras to start training a model?

A .fit()

B .evaluate()

C .predict()

D .compile()

The .fit() method trains the model on your data after you have compiled it.

Which Keras API allows you to build models by stacking layers one after another?

A Functional API

B Sequential API

C Subclassing API

D Estimator API

The Sequential API is the simple way to build models by stacking layers in order.

What is the purpose of the .compile() method in Keras?

A To set optimizer, loss function, and metrics before training

B To define the model architecture

C To make predictions on new data

D To save the model to disk

The .compile() method prepares the model for training by setting the optimizer, loss, and metrics.

Which of these is NOT a typical use of Keras callbacks?

A Stopping training early if no improvement

B Saving the best model during training

C Changing the model architecture during training

D Adjusting learning rates dynamically

Callbacks customize training behavior but do not change the model architecture during training.

Explain how Keras simplifies building and training neural networks in TensorFlow.

Think about how Keras hides complex details and provides simple tools.

Describe the difference between the Sequential API and the Functional API in Keras.

Consider model complexity and flexibility.