You're reading from Automated Machine Learning with AutoKeras

Product typeBook

Published inMay 2021

Reading LevelBeginner

PublisherPackt

ISBN-139781800567641

Edition1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1)

Luis Sobrecueva

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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Automated Machine Learning with AutoKeras

Published in: May 2021Publisher: PacktISBN-13: 9781800567641

Author (1)

Luis 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.
Read more about Luis Sobrecueva

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