Book Image

Automated Machine Learning with AutoKeras

By : Luis Sobrecueva
Book Image

Automated Machine Learning with AutoKeras

By: Luis Sobrecueva

Overview of this book

AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you. This book teaches you how to develop and use state-of-the-art AI algorithms in your projects. It begins with a high-level introduction to automated machine learning, explaining all the concepts required to get started with this machine learning approach. You will then learn how to use AutoKeras for image and text classification and regression. As you make progress, you'll discover how to use AutoKeras to perform sentiment analysis on documents. This book will also show you how to implement a custom model for topic classification with AutoKeras. Toward the end, you will explore advanced concepts of AutoKeras such as working with multi-modal data and multi-task, customizing the model with AutoModel, and visualizing experiment results using AutoKeras Extensions. By the end of this machine learning book, you will be able to confidently use AutoKeras to design your own custom machine learning models in your company.
Table of Contents (15 chapters)
1
Section 1: AutoML Fundamentals
5
Section 2: AutoKeras in Practice
11
Section 3: Advanced AutoKeras

Exploring models with multiple inputs or outputs

As we will see later, sometimes, it may interest us that our model feeds on information from different sources (multimodal) and/or predicts multiple targets at the same time (multitask). AutoKeras has a class called AutoModel that allows us to define several sources and targets as a list of parameters. Let's dive a little deeper into this before looking at a practical example.

What is AutoModel?

AutoModel is a class that allows us to define a model in a granular way by defining not only its inputs and outputs but also its intermediate layers.

It can be used in two different ways:

  • Basic: Here, the input/output nodes are specified and AutoModel infers the remaining part of the model.
  • Advanced: Here, the high-level architecture is defined by connecting the layers (blocks) with the Functional API, which is the same as the Keras functional API.

Let's look at an example of each one.

Basic example

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