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Learn TensorFlow Enterprise

You're reading from   Learn TensorFlow Enterprise Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781800209145
Length 314 pages
Edition 1st Edition
Languages
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Author (1):
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KC Tung KC Tung
Author Profile Icon KC Tung
KC Tung
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Table of Contents (15) Chapters Close

Preface 1. Section 1 – TensorFlow Enterprise Services and Features
2. Chapter 1: Overview of TensorFlow Enterprise FREE CHAPTER 3. Chapter 2: Running TensorFlow Enterprise in Google AI Platform 4. Section 2 – Data Preprocessing and Modeling
5. Chapter 3: Data Preparation and Manipulation Techniques 6. Chapter 4: Reusable Models and Scalable Data Pipelines 7. Section 3 – Scaling and Tuning ML Works
8. Chapter 5: Training at Scale 9. Chapter 6: Hyperparameter Tuning 10. Section 4 – Model Optimization and Deployment
11. Chapter 7: Model Optimization 12. Chapter 8: Best Practices for Model Training and Performance 13. Chapter 9: Serving a TensorFlow Model 14. Other Books You May Enjoy

Decoding TFRecord and reconstructing the image

In the previous section, we learned how to write a .jpg image into a TFRecord dataset. Now we are going to see how to read it back and display it. An important requirement is that you must know the feature structure of the TFRecord protobuf as indicated by its keys. The feature structure is the same as the feature description used to build the TFRecord in the previous section. In other words, in the same way as a raw image was structured into a tf.Example protobuf with a defined feature description, we can use that feature description to parse or reconstruct the image using the same knowledge stored in the feature description:

  1. Read TFRecord back from the path where it is stored:
    read_back_tfrecord = tf.data.TFRecordDataset('tfrecords-collection/maldives-1.tfrecord')
  2. Create a dictionary to specify the keys and values in TFRecord, and use it to parse all elements in the TFRecord dataset:
    # Create a dictionary describing...
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