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Hands-On Machine Learning with TensorFlow.js

You're reading from   Hands-On Machine Learning with TensorFlow.js A guide to building ML applications integrated with web technology using the TensorFlow.js library

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838821739
Length 296 pages
Edition 1st Edition
Languages
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Author (1):
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Kai Sasaki Kai Sasaki
Author Profile Icon Kai Sasaki
Kai Sasaki
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js
2. Machine Learning for the Web FREE CHAPTER 3. Importing Pretrained Models into TensorFlow.js 4. TensorFlow.js Ecosystem 5. Section 2: Real-World Applications of TensorFlow.js
6. Polynomial Regression 7. Classification with Logistic Regression 8. Unsupervised Learning 9. Sequential Data Analysis 10. Dimensionality Reduction 11. Solving the Markov Decision Process 12. Section 3: Productionizing Machine Learning Applications with TensorFlow.js
13. Deploying Machine Learning Applications 14. Tuning Applications to Achieve High Performance 15. Future Work Around TensorFlow.js 16. Other Books You May Enjoy

Questions

  1. What is the serialization format used by the model definition file of SavedModel?
  2. What kind of file format does the Keras API export?
  1. Let's assume we want to convert the following model. Please describe the options for converting it using tfjs-converter:
    1. SavedModel
    2. The model tag is my_mobilenet1
    3. The output node name is y
  2. Write code to import a pretrained MobileNet into TensorFlow.js. The model is uploaded to TensorFlow Hub: https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/3.
  3. What is the recommended model size to import a model into web browsers in general? Do you think you can optimize the memory footprint of the SavedModel or Keras model?
  4. In order to achieve the best performance when loading the model via HTTP, how big should each shard of the weight variable file be in the web format?
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