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Mastering TensorFlow 1.x

You're reading from   Mastering TensorFlow 1.x Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788292061
Length 474 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (21) Chapters Close

Preface 1. TensorFlow 101 2. High-Level Libraries for TensorFlow FREE CHAPTER 3. Keras 101 4. Classical Machine Learning with TensorFlow 5. Neural Networks and MLP with TensorFlow and Keras 6. RNN with TensorFlow and Keras 7. RNN for Time Series Data with TensorFlow and Keras 8. RNN for Text Data with TensorFlow and Keras 9. CNN with TensorFlow and Keras 10. Autoencoder with TensorFlow and Keras 11. TensorFlow Models in Production with TF Serving 12. Transfer Learning and Pre-Trained Models 13. Deep Reinforcement Learning 14. Generative Adversarial Networks 15. Distributed Models with TensorFlow Clusters 16. TensorFlow Models on Mobile and Embedded Platforms 17. TensorFlow and Keras in R 18. Debugging TensorFlow Models 19. Tensor Processing Units
20. Other Books You May Enjoy

TF Mobile in iOS apps

TensorFlow enables support for iOS apps by following these steps:

  1. Include TF Mobile in your app by adding a file named Profile in the root directory of your project. Add the following content to the Profile:
target 'Name-Of-Your-Project'
       pod 'TensorFlow-experimental'
  1. Run the pod install command to download and install the TensorFlow Experimental pod.
  2. Run the myproject.xcworkspace command to open the workspace so you can add the prediction code to your application logic.
To create your own TensorFlow binaries for iOS projects, follow the instructions at this link: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/ios

Once the TF library is configured in your iOS project, you can call the TF model with the following four steps:

  1. Load the model:
PortableReadFileToProto(file_path, &tensorflow_graph);
...
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