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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
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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

How to use TPUs with Colab

In this section, we show how to use TPUs with Colabs. Just point your browser to https://colab.research.google.com/ and change the runtime from the runtime menu as shown in Figure 9:

Figure 9: Setting TPU as runtime in Colab

Checking whether TPUs are available

First of all, let's check if there is a TPU available by using this simple code fragment that returns the IP address assigned to the TPU. Communication between CPU and TPU happens via grpc:

import os
try:
    device_name = os.environ['COLAB_TPU_ADDR']
    TPU_ADDRESS = 'grpc://' + device_name
    print('Found TPU at: {}'.format(TPU_ADDRESS))
except KeyError:
    print('TPU not found')
Found TPU at: grpc://10.91.166.82:8470

We've confirmed that a TPU is available! Now, we'll continue to explore how we can make use of it.

Loading data with tf.data

Our goal is to implement a simple CNN on MNIST...

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