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R Deep Learning Cookbook

You're reading from   R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Length 288 pages
Edition 1st Edition
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Authors (2):
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Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Author Profile Icon Achyutuni Sri Krishna Rao
Achyutuni Sri Krishna Rao
PKS Prakash PKS Prakash
Author Profile Icon PKS Prakash
PKS Prakash
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

Setting up denoising autoencoders


Denoising autoencoders are a special kind of autoencoder with a focus on extracting robust features from the input dataset. Denoising autoencoders are similar to the previous model except with a major difference that the data is corrupted before training the network. Different approaches for corruption can be used such as masking, which induces random error into the data.

Getting ready

Let's use the CIFAR-10 image data to set up a denoising dataset:

  • Download the CIFAR-10 dataset using the download_cifar_data function (covered in Chapter 3, Convolution Neural Network)
  • TensorFlow installation in R and Python

How to do it...

We first need to read the dataset.

Reading the dataset

  1. Load the CIFAR dataset using the steps explained in Chapter 3, Convolution Neural Network. The data files data_batch_1 and data_batch_2 are used to train. The data_batch_5 and test_batch files are used for validation and testing, respectively. The data can be flattened using the flat_data function...
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