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Deep Learning with PyTorch

You're reading from   Deep Learning with PyTorch A practical approach to building neural network models using PyTorch

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788624336
Length 262 pages
Edition 1st Edition
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Author (1):
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Vishnu Subramanian Vishnu Subramanian
Author Profile Icon Vishnu Subramanian
Vishnu Subramanian
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Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning Using PyTorch FREE CHAPTER 2. Building Blocks of Neural Networks 3. Diving Deep into Neural Networks 4. Fundamentals of Machine Learning 5. Deep Learning for Computer Vision 6. Deep Learning with Sequence Data and Text 7. Generative Networks 8. Modern Network Architectures 9. What Next? 10. Other Books You May Enjoy

Model ensembling

There could be times when we would need to try to combine multiple models to build a very powerful model. There are many techniques that can be used for building an ensemble model. In this section, we will learn how to combine outputs using the features generated by three different models (ResNet, Inception, and DenseNet) to build a powerful model. We will be using the same dataset that we used for other examples in this chapter.

The architecture for the ensemble model would look like this:

This image shows what we are going to do in the ensemble model, which can be summarized in the following steps:

  1. Create three models
  2. Extract the image features using the created models
  3. Create a custom dataset which returns features of all the three models along with the labels
  4. Create model similar to the architecture in the preceding figure
  5. Train and validate the model

Let...

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