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Hands-On Neural Networks with TensorFlow 2.0

You're reading from   Hands-On Neural Networks with TensorFlow 2.0 Understand TensorFlow, from static graph to eager execution, and design neural networks

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Product type Paperback
Published in Sep 2019
Publisher Packt
ISBN-13 9781789615555
Length 358 pages
Edition 1st Edition
Languages
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Author (1):
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Paolo Galeone Paolo Galeone
Author Profile Icon Paolo Galeone
Paolo Galeone
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Neural Network Fundamentals
2. What is Machine Learning? FREE CHAPTER 3. Neural Networks and Deep Learning 4. Section 2: TensorFlow Fundamentals
5. TensorFlow Graph Architecture 6. TensorFlow 2.0 Architecture 7. Efficient Data Input Pipelines and Estimator API 8. Section 3: The Application of Neural Networks
9. Image Classification Using TensorFlow Hub 10. Introduction to Object Detection 11. Semantic Segmentation and Custom Dataset Builder 12. Generative Adversarial Networks 13. Bringing a Model to Production 14. Other Books You May Enjoy

Exercises

Please go through the following exercises and answer all questions carefully. This is the only way (by making exercises, via trial and error, and with a lot of struggle) you will be able to master the framework and become an expert:

  1. Define a classifier using the Sequential, Functional, and Subclassing APIs so that you can classify the fashion-MNIST dataset.
  2. Train the model using the Keras model's built-in methods and measure the prediction accuracy.
  3. Write a class that accepts a Keras model in its constructor and that it trains and evaluates.
    The API should work as follows:
# Define your model
trainer = Trainer(model)
# Get features and labels as numpy arrays (explore the dataset available in the keras module)
trainer.train(features, labels)
# measure the accuracy
trainer.evaluate(test_features, test_labels)
  1. Accelerate the training method using the @tf.function annotation...
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