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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
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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

AutoKeras

AutoKeras [6] provides functions to automatically search for the architecture and hyperparameters of deep learning models. The framework uses Bayesian optimization for efficient neural architecture search. You can install the alpha version by using pip:

pip3 install autokeras # for 0.4 version
pip3 install git+git://github.com/keras-team/autokeras@master#egg=autokeras # for 1.0 version

The architecture is explained in Figure 3 (taken from [6]):

  1. The user calls the API
  2. The searcher generates neural architectures on CPU
  3. Real neural networks with parameters on RAM from the neural architectures
  4. The neural network is copied to GPU for training
  5. Trained neural networks are saved on storage devices
  6. The searcher is updated based on the training results

Steps 2 to 6 will repeat until a time limit is reached:

Figure 3: AutoKeras system overview

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