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Advanced Deep Learning with TensorFlow 2 and Keras

You're reading from   Advanced Deep Learning with TensorFlow 2 and Keras Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781838821654
Length 512 pages
Edition 2nd Edition
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Author (1):
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Rowel Atienza Rowel Atienza
Author Profile Icon Rowel Atienza
Rowel Atienza
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Table of Contents (16) Chapters Close

Preface 1. Introducing Advanced Deep Learning with Keras 2. Deep Neural Networks FREE CHAPTER 3. Autoencoders 4. Generative Adversarial Networks (GANs) 5. Improved GANs 6. Disentangled Representation GANs 7. Cross-Domain GANs 8. Variational Autoencoders (VAEs) 9. Deep Reinforcement Learning 10. Policy Gradient Methods 11. Object Detection 12. Semantic Segmentation 13. Unsupervised Learning Using Mutual Information 14. Other Books You May Enjoy
15. Index

9. Data generator model in Keras

SSD requires a lot of labeled high-resolution images for object detection. Unlike the previous chapters where the dataset used can be loaded into memory to train the model, SSD implements a multi-threaded data generator. The task of the multi-threaded generator is to load multiple mini-batches of images and their corresponding labels. Because of multi-threading, the GPU can be kept busy as one thread feeds it with data while the rest of CPU threads are in the queue ready to feed another batch data or loading a batch of images from the filesystem and computing the ground truth values. Listing 11.9.1 shows the data generator model in Keras.

The DataGenerator class inherits from the Sequence class of Keras to ensure that it supports multi-processing. DataGenerator guarantees that the entire dataset is used in one epoch.

The length of the entire epoch given a batch size is returned by the __len__() method. Every request for a mini-batch of data...

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