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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

Constructing output layers

As a final step, we need to decode the data back from the encoded state. Are we able to reconstruct the input just the way it is? If yes, then it's all good. Otherwise, we need to calculate an associated reconstruction error. Remember that the incoming connections to the output layer should be the same as the outgoing connections from the preceding layer.

How to do it...

  1. Create an output layer using OutputLayer:
OutputLayer outputLayer = new OutputLayer.Builder().nIn(250).nOut(784)
.lossFunction(LossFunctions.LossFunction.MSE)
.build();
  1. Add OutputLayer to the layer definitions:
builder.layer(new OutputLayer.Builder().nIn(250).nOut(784)
.lossFunction(LossFunctions.LossFunction.MSE)
.build...
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