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You're reading from  Java Deep Learning Cookbook

Product typeBook
Published inNov 2019
Reading LevelIntermediate
PublisherPackt
ISBN-139781788995207
Edition1st Edition
Languages
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Author (1)
Rahul Raj
Rahul Raj
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Rahul Raj

Rahul Raj has more than 7 years of IT industry experience in software development, business analysis, client communication, and consulting on medium-/large-scale projects in multiple domains. Currently, he works as a lead software engineer in a top software development firm. He has extensive experience in development activities comprising requirement analysis, design, coding, implementation, code review, testing, user training, and enhancements. He has written a number of articles about neural networks in Java and they are featured by DL4J/ official Java community channels. He is also a certified machine learning professional, certified by Vskills, the largest government certification body in India.
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Implementing frozen layers

We might want to keep the training instance limited to certain layers, which means some layers can be kept frozen for the training instance, so we can focus on optimizing other layers while frozen layers are kept unchanged. We saw two ways of implementing frozen layers earlier: using the regular transfer learning builder and using the transfer learning helper. In this recipe, we will implement frozen layers for transfer layers.

How to do it...

  1. Define frozen layers by calling setFeatureExtractor():
MultiLayerNetwork newModel = new TransferLearning.Builder(oldModel)
.setFeatureExtractor(featurizeExtractionLayer)
.build();
  1. Call fit() to start the training instance:
newModel.fit(numOfEpochs);
...
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Java Deep Learning Cookbook
Published in: Nov 2019Publisher: PacktISBN-13: 9781788995207

Author (1)

author image
Rahul Raj

Rahul Raj has more than 7 years of IT industry experience in software development, business analysis, client communication, and consulting on medium-/large-scale projects in multiple domains. Currently, he works as a lead software engineer in a top software development firm. He has extensive experience in development activities comprising requirement analysis, design, coding, implementation, code review, testing, user training, and enhancements. He has written a number of articles about neural networks in Java and they are featured by DL4J/ official Java community channels. He is also a certified machine learning professional, certified by Vskills, the largest government certification body in India.
Read more about Rahul Raj