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Advanced Deep Learning with Keras

You're reading from  Advanced Deep Learning with Keras

Product type Book
Published in Oct 2018
Publisher Packt
ISBN-13 9781788629416
Pages 368 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Rowel Atienza Rowel Atienza
Profile icon Rowel Atienza
LinkedIn
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).
See other products by Rowel Atienza

Table of Contents (13) 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 Other Books You May Enjoy Index

Generator outputs of StackedGAN

After training the StackedGAN for 10,000 steps, the Generator0 and Generator1 models are saved on files. Stacked together, Generator0 and Generator1 can synthesize fake images conditioned on label and noise codes, z0 and z1.

The StackedGAN generator can be qualitatively validated by:

  1. Varying the discrete labels from 0 to 9 with both noise codes, z0 and z1 sampled from a normal distribution with a mean of 0.5 and standard -deviation of 1.0. The results are shown in Figure 6.2.9. We're able to see that the StackedGAN discrete code can control the digits produced by the generator:
    python3 stackedgan-mnist-6.2.1.py 
--generator0=stackedgan_mnist-gen0.h5 
--generator1=stackedgan_mnist-gen1.h5 --digit=0
python3 stackedgan-mnist-6.2.1.py 
--generator0=stackedgan_mnist-gen0.h5 
--generator1=stackedgan_mnist-gen1.h5 --digit=9

    to

  2. Varying the first noise code, z0, as a constant vector from -4.0 to 4.0 for digits 0 to 9 as shown...
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Authors (1)

Profile icon Rowel Atienza
LinkedIn
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).
Read more
See other products by Rowel Atienza

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