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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
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TF Mobile in Android apps

The TensorFlow ecosystem enables it to be used in Android apps through the interface class TensorFlowInferenceInterface, and the TensorFlow Java API in the jar file libandroid_tensorflow_inference_java.jar. You can either use the jar file from the JCenter, download a precompiled jar from ci.tensorflow.org, or build it yourself.

The inference interface has been made available as a JCenter package and can be included in the Android project by adding the following code to the build.gradle file:

allprojects {
repositories {
jcenter()
}
}
dependencies {
compile 'org.tensorflow:tensorflow-android:+'
}
Instead of using the pre-built binaries from the JCenter, you can also build them yourself using Bazel or Cmake by following the instructions at this link: https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/contrib/android/README...
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