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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

Performing hyperparameter tuning

Once search spaces are defined using ParameterSpace or OptimizationConfiguration, with a possible range of values, the next step is to complete network configuration using MultiLayerSpace or ComputationGraphSpace. After that, we start the training process. We perform multiple training sessions during the hyperparameter tuning process.

In this recipe, we will perform and visualize the hyperparameter tuning process. We will be using MultiLayerSpace for the demonstration.

How to do it...

  1. Add a search space for the layer size using IntegerParameterSpace:
ParameterSpace<Integer> layerSizeParam = new IntegerParameterSpace(startLimit,endLimit);
  1. Add a search space for the learning rate using...
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