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TensorFlow 2.0 Computer Vision Cookbook

You're reading from   TensorFlow 2.0 Computer Vision Cookbook Implement machine learning solutions to overcome various computer vision challenges

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781838829131
Length 542 pages
Edition 1st Edition
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Author (1):
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Jesús Martínez Jesús Martínez
Author Profile Icon Jesús Martínez
Jesús Martínez
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Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Getting Started with TensorFlow 2.x for Computer Vision 2. Chapter 2: Performing Image Classification FREE CHAPTER 3. Chapter 3: Harnessing the Power of Pre-Trained Networks with Transfer Learning 4. Chapter 4: Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution 5. Chapter 5: Reducing Noise with Autoencoders 6. Chapter 6: Generative Models and Adversarial Attacks 7. Chapter 7: Captioning Images with CNNs and RNNs 8. Chapter 8: Fine-Grained Understanding of Images through Segmentation 9. Chapter 9: Localizing Elements in Images with Object Detection 10. Chapter 10: Applying the Power of Deep Learning to Videos 11. Chapter 11: Streamlining Network Implementation with AutoML 12. Chapter 12: Boosting Performance 13. Other Books You May Enjoy

Exporting and importing a model in AutoKeras

One worry we might have when working with AutoML is the black-box nature of the tools available. Do we have control over the produced models? Can we extend them? Understand them? Reuse them?

Of course we can! The good thing about AutoKeras is that it is built on top of TensorFlow, so despite its sophistication, under the hood, the models being trained are just TensorFlow graphs that we can export and tweak and tune later if we need to.

In this recipe, we'll learn how to export a model trained on AutoKeras, and then import it as a plain old TensorFlow network.

Are you ready? Let's begin.

How to do it…

Follow these steps to complete this recipe:

  1. Import the necessary dependencies:
    from autokeras import *
    from tensorflow.keras.datasets import fashion_mnist as fm
    from tensorflow.keras.models import load_model
    from tensorflow.keras.utils import plot_model
  2. Load the train and test splits of the Fashion...
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