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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

Comparing performance using CPU and GPU


One of the questions with device change is why so much improvement is observed when the device is switched from CPU to GPU. As the deep learning architecture involves a lot of matrix computations, GPUs help expedite these computations using a lot of parallel cores, which are usually used for image rendering.

The power of GPU has been utilized by a lot of algorithms to accelerate the execution. The following recipe provides some benchmarks of matrix computation using the gpuR package. The gpuR package is a general-purpose package for GPU computing in R.

Getting ready

The section covers requirement to set-up a comparison between GPU Vs CPU.

  1. Use GPU hardware installed such as GTX 1070.
  2. CUDA toolkit installation using URL https://developer.nvidia.com/cuda-downloads.
  3. Install the gpuR package:
install.packages("gpuR") 
  1. Test gpuR:
library(gpuR) 
# verify you have valid GPUs 
detectGPUs()  

How to do it...

Let's get started by loading the packages:

  1. Load the package, and...
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