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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

Creating an inverse image search index with deep learning

Because the whole point of an autoencoder is to learn an encoding or a low-dimensional representation of a set of images, they make for great feature extractors. Furthermore, we can use them as the perfect building blocks of image search indices, as we'll discover in this recipe.

Getting ready

Let's install OpenCV with pip. We'll use it to visualize the outputs of our autoencoder, in order to visually assess the effectiveness of the image search index:

$> pip install opencv-python

We'll start implementing the recipe in the next section.

How to do it…

Follow these steps to create your own image search index:

  1. Import the necessary libraries:
    import cv2
    import numpy as np
    from tensorflow.keras import Model
    from tensorflow.keras.datasets import fashion_mnist
    from tensorflow.keras.layers import *
  2. Define build_autoencoder(), which instantiates the autoencoder. First, let&apos...
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