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Automated Machine Learning with AutoKeras

You're reading from Automated Machine Learning with AutoKeras Deep learning made accessible for everyone with just few lines of coding

Product type Paperback

Published in May 2021

Publisher Packt

ISBN-13 9781800567641

Length 194 pages

Edition 1st Edition

Languages

Python

Tools

AutoKeras

Concepts

Deep Learning

Author (1):

Luis Sobrecueva

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1: AutoML Fundamentals

2. Chapter 1: Introduction to Automated Machine Learning FREE CHAPTER

3. Chapter 2: Getting Started with AutoKeras

4. Chapter 3: Automating the Machine Learning Pipeline with AutoKeras

5. Section 2: AutoKeras in Practice

6. Chapter 4: Image Classification and Regression Using AutoKeras

7. Chapter 5: Text Classification and Regression Using AutoKeras

8. Chapter 6: Working with Structured Data Using AutoKeras

9. Chapter 7: Sentiment Analysis Using AutoKeras

10. Chapter 8: Topic Classification Using AutoKeras

11. Section 3: Advanced AutoKeras

12. Chapter 9: Working with Multimodal and Multitasking Data

13. Chapter 10: Exporting and Visualizing the Models

14. Other Books You May Enjoy

Customizing the search space

As we mentioned at the beginning of this chapter, there is an advanced way to use AutoModel. We can do this by defining the whole model architecture by connecting the layers (blocks) with the functional API, which is the same as the Keras functional API.

Let's do this in the following example:

input_node1 = ak.ImageInput()
output_node = ak.Normalization()(input_node1)
output_node = ak.ImageAugmentation()(output_node)
output_node1 = ak.ConvBlock()(output_node)
output_node2 = ak.ResNetBlock(version='v2')(output_node)
output_node1 = ak.Merge()([output_node1, output_node2])
 
input_node2 = ak.StructuredDataInput()
output_node = ak.CategoricalToNumerical()(input_node2)
output_node2 = ak.DenseBlock()(output_node)
 
output_node = ak.Merge()([output_node1, output_node2])
output_node1 = ak.ClassificationHead()(output_node)
output_node2 = ak.RegressionHead()(output_node)
 
model = ak.AutoModel(
    inputs=[input_node1, input_node2...

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Authors (1)

Sobrecueva

Sobrecueva

Luis Sobrecueva is a senior software engineer and ML/DL practitioner currently working at Cabify. He has been a contributor to the OpenAI project as well as one of the contributors to the AutoKeras project.

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