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Hands-On Neural Networks with TensorFlow 2.0

You're reading from   Hands-On Neural Networks with TensorFlow 2.0 Understand TensorFlow, from static graph to eager execution, and design neural networks

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Product type Paperback
Published in Sep 2019
Publisher Packt
ISBN-13 9781789615555
Length 358 pages
Edition 1st Edition
Languages
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Author (1):
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Paolo Galeone Paolo Galeone
Author Profile Icon Paolo Galeone
Paolo Galeone
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Neural Network Fundamentals
2. What is Machine Learning? FREE CHAPTER 3. Neural Networks and Deep Learning 4. Section 2: TensorFlow Fundamentals
5. TensorFlow Graph Architecture 6. TensorFlow 2.0 Architecture 7. Efficient Data Input Pipelines and Estimator API 8. Section 3: The Application of Neural Networks
9. Image Classification Using TensorFlow Hub 10. Introduction to Object Detection 11. Semantic Segmentation and Custom Dataset Builder 12. Generative Adversarial Networks 13. Bringing a Model to Production 14. Other Books You May Enjoy

Getting the data

The task we are going to solve in this chapter is a classification problem on a dataset of flowers, which is available in tensorflow-datasets (tfds). The dataset's name is tf_flowers and it consists of images of five different flower species at different resolutions. Using tfds, gathering the data is straightforward, and we can get the dataset's information by looking at the info variable returned by the tfds.load invocation, as shown here:

(tf2)

import tensorflow_datasets as tfds

dataset, info = tfds.load("tf_flowers", with_info=True)
print(info)

The preceding code produces the following dataset description:

tfds.core.DatasetInfo(
name='tf_flowers',
version=1.0.0,
description='A large set of images of flowers',
urls=['http://download.tensorflow.org/example_images/flower_photos.tgz'],
features=FeaturesDict...
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