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Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

You're reading from Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

Product type Book

Published in Feb 2020

Publisher Packt

ISBN-13 9781838821654

Pages 512 pages

Edition 2nd Edition

Languages

Concepts

Programming Language

Author (1):

Rowel Atienza

Table of Contents (16) Chapters

Preface

1. Introducing Advanced Deep Learning with Keras

2. Deep Neural Networks

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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Authors (1)

Rowel Atienza

Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence. Rowel has been fascinated with intelligent robots since he graduated from the University of the Philippines. He received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution on the field of active gaze tracking for human-robot interaction. Rowel's current research work focuses on AI and computer vision. He dreams on building useful machines that can perceive, understand, and reason. To help make his dreams become real, Rowel has been supported by grants from the Department of Science and Technology (DOST), Samsung Research Philippines, and Commission on Higher Education-Philippine California Advanced Research Institutes (CHED-PCARI).

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