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What's New in TensorFlow 2.0

You're reading from   What's New in TensorFlow 2.0 Use the new and improved features of TensorFlow to enhance machine learning and deep learning

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781838823856
Length 202 pages
Edition 1st Edition
Languages
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Authors (3):
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Tanish Baranwal Tanish Baranwal
Author Profile Icon Tanish Baranwal
Tanish Baranwal
Alizishaan Khatri Alizishaan Khatri
Author Profile Icon Alizishaan Khatri
Alizishaan Khatri
Ajay Baranwal Ajay Baranwal
Author Profile Icon Ajay Baranwal
Ajay Baranwal
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Table of Contents (13) Chapters Close

Preface 1. Section 1: TensorFlow 2.0 - Architecture and API Changes
2. Getting Started with TensorFlow 2.0 FREE CHAPTER 3. Keras Default Integration and Eager Execution 4. Section 2: TensorFlow 2.0 - Data and Model Training Pipelines
5. Designing and Constructing Input Data Pipelines 6. Model Training and Use of TensorBoard 7. Section 3: TensorFlow 2.0 - Model Inference and Deployment and AIY
8. Model Inference Pipelines - Multi-platform Deployments 9. AIY Projects and TensorFlow Lite 10. Section 4: TensorFlow 2.0 - Migration, Summary
11. Migrating From TensorFlow 1.x to 2.0 12. Other Books You May Enjoy

Model artifact – the SavedModel format

The SavedModel format is the default model serialization and deserialization format used by TensorFlow. In layman's terms, this can be understood as a container that holds everything there is to reproduce a model from scratch elsewhere without access to the original code that created it. We can use SavedModel to transfer trained models from the training to the inference phase or even to transfer state between different parts of the training process. In a nutshell, it can be said that SavedModel contains a complete TensorFlow program along with model weights and descriptions of the various compute operations described. While working with the Python API of TF 2.0, it is now possible to export certain native Python operations along with the model. This is facilitated largely by the tf.function and tf.autograph APIs. In the following...

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