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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

Setting up heap spaces and garbage collection

Memory heap spaces and garbage collection are frequently discussed yet are often the most frequently ignored benchmarks. With DL4J/ND4J, you can configure two types of memory limit: on-heap memory and off-heap memory. Whenever an INDArray is collected by the JVM garbage collector, the off-heap memory will be de-allocated, assuming that it is not being used anywhere else. In this recipe, we will set up heap spaces and garbage collection for benchmarking.

How to do it...

  1. Add the required VM arguments to the Eclipse/IntelliJ IDE, as shown in the following example:
-Xms1G -Xmx6G -Dorg.bytedeco.javacpp.maxbytes=16G -Dorg.bytedeco.javacpp.maxphysicalbytes=20G

For example, in IntelliJ...

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