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Java Deep Learning Cookbook

You're reading from   Java Deep Learning Cookbook Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781788995207
Length 304 pages
Edition 1st Edition
Languages
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Author (1):
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Rahul Raj Rahul Raj
Author Profile Icon Rahul Raj
Rahul Raj
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Deep Learning in Java 2. Data Extraction, Transformation, and Loading FREE CHAPTER 3. Building Deep Neural Networks for Binary Classification 4. Building Convolutional Neural Networks 5. Implementing Natural Language Processing 6. Constructing an LSTM Network for Time Series 7. Constructing an LSTM Neural Network for Sequence Classification 8. Performing Anomaly Detection on Unsupervised Data 9. Using RL4J for Reinforcement Learning 10. Developing Applications in a Distributed Environment 11. Applying Transfer Learning to Network Models 12. Benchmarking and Neural Network Optimization 13. Other Books You May Enjoy

Creating an API endpoint for the image classifier

We want to leverage the image classifier as an API to use them in external applications. An API can be accessed externally, and prediction results can be obtained without setting up anything. In this recipe, we will create an API endpoint for the image classifier.

How to do it...

  1. Persist the model using ModelSerializer:
File file = new File("cnntrainedmodel.zip");
ModelSerializer.writeModel(model,file,true);
ModelSerializer.addNormalizerToModel(file,scaler);
  1. Restore the trained model using ModelSerializer to perform predictions:
MultiLayerNetwork network = ModelSerializer.restoreMultiLayerNetwork(modelFile);
NormalizerStandardize normalizerStandardize = ModelSerializer...
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