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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

Loading the model into TensorFlow.js

The model generated by tfjs-converter can be finally loaded by TensorFlow.js. TensorFlow.js provides us with some dedicated APIs to load the specific model format, loadGraphModel and loadLayersModel. If the model file is created from SavedModel, loadGraphModel can be used. On the other hand, if the original model is Keras, loadLayersModel should be used. These APIs are able to load the model via both HTTP and a local filesystem:

import * as tf from '@tensorflow/tfjs';

const MODEL_URL = 'https://path/to/model.json';
const model = await tf.loadGraphModel(MODEL_URL);

// Or

const MODEL_PATH = 'file://path/to/model.json';
const model = await tf.loadGraphModel(MODEL_PATH);

Model loading can be done asynchronously to prevent the loading of a large-size model from blocking the main thread. While browsers usually support...

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