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

Loading data into AutoKeras in multiple formats

As we mentioned previously, AutoKeras performs normalization automatically. However, in the following chapters, you will see that you can create your model in a more personalized way by stacking blocks. More specifically, you can use special blocks to normalize your data.

Now, let's look at the different data structures that we can use to feed our model.

AutoKeras models accept three types of input:

  • A NumPy array is an array that's commonly used by Scikit-Learn and many other Python-based libraries. This is always the fastest option, as long as your data fits in memory.
  • Python generators load batches of data from disk to memory, so this is a good option when the entire dataset does not fit in memory.
  • TensorFlow Dataset is a high-performance option that is similar to Python generators, but it is best suited for deep learning and large datasets. This is because data can be streamed from disk or from a distributed...