Book Image

Learn TensorFlow Enterprise

By : KC Tung
Book Image

Learn TensorFlow Enterprise

By: KC Tung

Overview of this book

TensorFlow as a machine learning (ML) library has matured into a production-ready ecosystem. This beginner’s book uses practical examples to enable you to build and deploy TensorFlow models using optimal settings that ensure long-term support without having to worry about library deprecation or being left behind when it comes to bug fixes or workarounds. The book begins by showing you how to refine your TensorFlow project and set it up for enterprise-level deployment. You’ll then learn how to choose a future-proof version of TensorFlow. As you advance, you’ll find out how to build and deploy models in a robust and stable environment by following recommended practices made available in TensorFlow Enterprise. This book also teaches you how to manage your services better and enhance the performance and reliability of your artificial intelligence (AI) applications. You’ll discover how to use various enterprise-ready services to accelerate your ML and AI workflows on Google Cloud Platform (GCP). Finally, you’ll scale your ML models and handle heavy workloads across CPUs, GPUs, and Cloud TPUs. By the end of this TensorFlow book, you’ll have learned the patterns needed for TensorFlow Enterprise model development, data pipelines, training, and deployment.
Table of Contents (15 chapters)
1
Section 1 – TensorFlow Enterprise Services and Features
4
Section 2 – Data Preprocessing and Modeling
7
Section 3 – Scaling and Tuning ML Works
10
Section 4 – Model Optimization and Deployment

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...