Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838821739
Length 296 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Kai Sasaki Kai Sasaki
Author Profile Icon Kai Sasaki
Kai Sasaki
Arrow right icon
View More author details
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

Exporting a model from TensorFlow

GraphDef, as you can see, only contains the minimum information to construct the model, which is not actually suitable for practical use cases. We may need a more comprehensive, platform-agnostic format to represent the machine learning model. SavedModel is the latest way to serialize a machine learning model in TensorFlow. Currently, using SavedModel is the recommended option to export a model trained by TensorFlow.js. This is because SavedModel contains not only the graph definition but also variables and graph metadata, so that higher-level systems or tools can consume the model and reuse it immediately.

Another major way to export the model is by using Keras. Keras is a high-level TensorFlow API that enables us to construct our model more intuitively. The usage of Keras is very similar to the TensorFlow.js Layers API. Many data scientists...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image