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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 FREE CHAPTER 2. Data Extraction, Transformation, and Loading 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

Saving and loading trained neural network models

Training the neural network over and over to perform an evaluation is not a good idea since training is a very costly operation. This is why model persistence is important in distributed systems as well.

In this recipe, we will persist the distributed neural network models to disk and load them for further use.

How to do it...

  1. Save the distributed neural network model using ModelSerializer:
MultiLayerNetwork model = sparkModel.getNetwork();
File file = new File("MySparkMultiLayerNetwork.bin");
ModelSerializer.writeModel(model,file, saveUpdater);
  1. Save the distributed neural network model using save():
MultiLayerNetwork model = sparkModel.getNetwork();
File locationToSave...
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