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Deep Learning with TensorFlow 2 and Keras

You're reading from   Deep Learning with TensorFlow 2 and Keras Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838823412
Length 646 pages
Edition 2nd Edition
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Authors (3):
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Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
Sujit Pal Sujit Pal
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Sujit Pal
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
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Table of Contents (19) Chapters Close

Preface 1. Neural Network Foundations with TensorFlow 2.0 2. TensorFlow 1.x and 2.x FREE CHAPTER 3. Regression 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Generative Adversarial Networks 7. Word Embeddings 8. Recurrent Neural Networks 9. Autoencoders 10. Unsupervised Learning 11. Reinforcement Learning 12. TensorFlow and Cloud 13. TensorFlow for Mobile and IoT and TensorFlow.js 14. An introduction to AutoML 15. The Math Behind Deep Learning 16. Tensor Processing Unit 17. Other Books You May Enjoy
18. Index

CycleGAN in TensorFlow 2.0

In the last section of this chapter we will implement a CycleGAN in TensorFlow 2.0. The CycleGAN requires a special dataset, a paired dataset, from one domain of images to another domain. So, besides the necessary modules, we will use tensorflow_datasets as well:

import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.losses import mean_squared_error, mean_absolute_error
import os
import time
import matplotlib.pyplot as plt
import numpy as np
import tensorflow_datasets as tfds

TensorFlow's Dataset API contains a list of datasets. It has many paired datasets for CycleGANs, such as horse to zebra, apples to oranges, and so on. You can access the complete list here: https://www.tensorflow.org/datasets/catalog/cycle_gan. For our code we will be using summer2winter_yosemite, which contains images of Yosemite (USA) in summer (Dataset A) and winter (Dataset B). We will train the CycleGAN to convert...

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