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Hands-On Computer Vision with TensorFlow 2

You're reading from   Hands-On Computer Vision with TensorFlow 2 Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788830645
Length 372 pages
Edition 1st Edition
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Authors (2):
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Eliot Andres Eliot Andres
Author Profile Icon Eliot Andres
Eliot Andres
Benjamin Planche Benjamin Planche
Author Profile Icon Benjamin Planche
Benjamin Planche
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Table of Contents (16) Chapters Close

Preface 1. Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision FREE CHAPTER
2. Computer Vision and Neural Networks 3. TensorFlow Basics and Training a Model 4. Modern Neural Networks 5. Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6. Influential Classification Tools 7. Object Detection Models 8. Enhancing and Segmenting Images 9. Section 3: Advanced Concepts and New Frontiers of Computer Vision
10. Training on Complex and Scarce Datasets 11. Video and Recurrent Neural Networks 12. Optimizing Models and Deploying on Mobile Devices 13. Migrating from TensorFlow 1 to TensorFlow 2 14. Assessments 15. Other Books You May Enjoy

Caching and reusing datasets

Finally, TensorFlow offers several functions to cache generated samples or to save tf.data pipeline states.

Samples can be cached by calling the dataset's .cache(filename) method. If cached, data will not have to undergo the same transformations when iterated over again (that is, for the next epochs). Note that the content of the cached data will not be the same depending on when the method is applied. Take the following example:

dataset = tf.data.TextLineDataset('/path/to/file.txt')
dataset_v1 = dataset.cache('cached_textlines.temp').map
(parse_fn)
dataset_v2 = dataset.map(parse_fn).cache('cached_images.temp')

The first dataset will cache the samples returned by TextLineDataset, that is, the text lines (the cached data is stored in the specified file, cached_textlines.temp). The transformation done by parse_fn (for instance, opening and decoding the corresponding image file for each text line) will have to be repeated for each...

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