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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

  1. Why is it possible to assess that the model suffers from overfitting only by looking at the graph?
  2. Extend the baseline example to place the matrix multiplication operation on a remote device at IP 192.168.1.12; visualize the result on TensorBoard.
  3. Is it necessary to have a remote device to place an operation on?
  4. Extend the CNN architecture defined in the define_cnn method: add a batch normalization layer (from tf.layers) between the output of the convolutional layer and its activation function.
  5. Try to train the model with the extended CNN architecture: the batch normalization layer adds two update operations that must be executed before running the training operation. Become familiar with the tf.control_dependencies method to force the execution of the operations contained inside the collection tf.GraphKeys.UPDATE_OPS, to be executed before the train operation (look...
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