Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
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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