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

Visualizing your models with TensorBoard

To develop efficient and successful models, you will need to know what is happening during your experiments so that you can react as soon as possible by correcting possible anomalous or unwanted results, such as overfitting and slow learning. This is where the concept of a tactile callback comes into play.

A callback is an object (a class instance that implements specific methods) that is passed to the model on the call to fit and that is called by the model at various points during training. You have access to all available data on the status of the model and its performance and, based on this, take measures including the following:

  • Interrupt training, because you have stopped learning or are overfitting
  • Save a model; in this way, the training could be resumed from the saved point in the future
  • Record metrics, such as precision or loss
  • Alter its state, and modify its structure or hyperparameters, such as the learning...