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Hands-On Neural Network Programming with C#

You're reading from   Hands-On Neural Network Programming with C# Add powerful neural network capabilities to your C# enterprise applications

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789612011
Length 328 pages
Edition 1st Edition
Languages
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Author (1):
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Matt Cole Matt Cole
Author Profile Icon Matt Cole
Matt Cole
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Toc

Table of Contents (16) Chapters Close

Preface 1. A Quick Refresher 2. Building Our First Neural Network Together FREE CHAPTER 3. Decision Trees and Random Forests 4. Face and Motion Detection 5. Training CNNs Using ConvNetSharp 6. Training Autoencoders Using RNNSharp 7. Replacing Back Propagation with PSO 8. Function Optimizations: How and Why 9. Finding Optimal Parameters 10. Object Detection with TensorFlowSharp 11. Time Series Prediction and LSTM Using CNTK 12. GRUs Compared to LSTMs, RNNs, and Feedforward networks 13. Activation Function Timings
14. Function Optimization Reference 15. Other Books You May Enjoy

Comparing LSTM, GRU, Feedforward, and RNN operations

In order to help you see the difference in both the creation and results of all the network objects we have been dealing with, I created the sample code that follows. This sample will allow you to see the difference in training times for all four of the network types we have here. As stated previously, the GRU is the easiest to train and therefore will complete faster (in less iterations) than the other networks. When executing the code, you will see that the GRU achieves the optimal error rate typically in under 10,000 iterations, while a conventional RNN and/or LSTM can take 50,000 or more iterations to converge properly.

Here is what our sample code looks like:

static void Main(string[] args)
{
Console.WriteLine("Running GRU sample", Color.Yellow);
Console.ReadKey();
ExampleGRU.Run();
Console.ReadKey();
Console.WriteLine...
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