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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 FREE CHAPTER
2. Machine Learning for the Web 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

Converting models using tfjs-converter

Unfortunately, models such as SavedModel of HDF5 created by TensorFlow cannot be used in the world of TensorFlow.js directly. It is inevitable that you will have to convert the model into a format readable by the web platform.

Converting a TensorFlow SavedModel

Once the SavedModel is created, you can convert the SavedModel into TensorFlow.js format as follows:

$ tensorflowjs_converter \
--output_node_names=output \
--input_format=tf_saved_model \
./my_saved_model ./my_tfjs_model

The input path and output path are required as positional arguments. (my_saved_model and my_tfjs_model). The model will be generated in the my_tfjs_model directory. The options specified in the preceding...

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