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Deep Learning Quick Reference

You're reading from   Deep Learning Quick Reference Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Length 272 pages
Edition 1st Edition
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Author (1):
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Mike Bernico Mike Bernico
Author Profile Icon Mike Bernico
Mike Bernico
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Table of Contents (15) Chapters Close

Preface 1. The Building Blocks of Deep Learning FREE CHAPTER 2. Using Deep Learning to Solve Regression Problems 3. Monitoring Network Training Using TensorBoard 4. Using Deep Learning to Solve Binary Classification Problems 5. Using Keras to Solve Multiclass Classification Problems 6. Hyperparameter Optimization 7. Training a CNN from Scratch 8. Transfer Learning with Pretrained CNNs 9. Training an RNN from scratch 10. Training LSTMs with Word Embeddings from Scratch 11. Training Seq2Seq Models 12. Using Deep Reinforcement Learning 13. Generative Adversarial Networks 14. Other Books You May Enjoy

Generating CIFAR-10 images using a Keras GAN

While the network architecture remains for the most part unchanged I felt it necessary to show you an example that uses color images, as well as providing the example in Git, so that you had some place to start if you wanted to apply a GAN to your own data.

The CIFAR-10 is a famous dataset comprised of 60,000 32 x 32 x 3 RGB color images, distributed across 10 categories. Those categories are airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. Hopefully, when you see the generated images later, you might see something that you can imagine looks like those objects.

Loading CIFAR-10

Loading the dataset is almost exactly the same, as Keras also provides a loader...

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