多线程反向传播 [英] Multithreaded backpropagation

问题描述

我已经在VB.NET中编写了一个反向传播类-它很好用-并且正在C#人工智能项目中使用它.

I have written a back propagation class in VB.NET -it works well- and I'm using it in a C# artificial intelligence project.

但是我家里有AMD Phenom X3，学校有Intel i5.而且我的神经网络不是多线程的.

But I have a AMD Phenom X3 at home and a Intel i5 at school. and my neural network is not multi-threaded.

如何将反向传播类转换为多线程算法?或如何在其中使用GPGPU编程?还是应该使用具有多线程反向传播神经网络的任何第三方库?

How to convert that back propagation class to a multithreaded algorithm? or how to use GPGPU programming in it? or should I use any third party libraries that have a multithreaded back propagation neural network?

推荐答案

JeffHeaton has recommend that you use resilient propagation (RPROP) instead of backpropagation. There are examples on how to do multithreaded RPROP (MPROP):

• 有关C#多线程反向传播的文章(来自Jeff Heaton)
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• 第7.2.1章传播和多线程"(第94页， Encog 2.5 for C#简介)

• Article on C# multithreaded backpropagation (from Jeff heaton)
• Chapter 7.2.1- "Propagation and Multithreading" (p.94 of Introduction to Encog 2.5 for C#)

很难在这里讨论所有详细信息，因此我建议您要么阅读该文章，然后查看我所引用的书的相关章节.当然，这是假定您熟悉并发编程.

It's a difficult to discuss all of the details here, so I would recommend that you either read that article and take a look at the relevant chapters of the book I referenced. This, of course, is assuming you're familiar with concurrent programming.

弹性传播的性能通常会比反向传播高出 相当大的因素.此外，RPROP没有必须包含的参数 被设置.反向传播需要学习率和动力 值被指定.寻找最佳的学习速度和动力 反向传播的价值可能很难.这是没有必要的 弹性传播. (来源: Encog机器学习)

Resilient propagation will typically outperform backpropagation by a considerable factor. Additionally, RPROP has no parameters that must be set. Backpropagation requires that a learning rate and momentum value be specified. Finding an optimal learning rate and momentum value for backpropagation can be difficult. This is not necessary with resilient propagation. (source: Encog Machine Learning)

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