帮助Neuroph神经网络 [英] Help with Neuroph neural network

问题描述

对于我的研究生研究，我正在创建一个训练识别图像的神经网络。我要做的不仅仅是采用RGB值网格，下采样，并将它们发送到网络的输入，就像许多例子一样。我实际上使用了超过100个独立训练的神经网络来检测线条，阴影图案等特征。更像人眼，它到目前为止工作得非常好！问题是我有相当多的训练数据。我展示了超过100个汽车外观的例子。然后是100个人的样子。然后超过100个狗的样子，等等。这是相当多的训练数据！目前我在大约一周的时间内开始训练网络。由于我需要调整和重新训练，这有点像我的进步。

For my graduate research I am creating a neural network that trains to recognize images. I am going much more complex than just taking a grid of RGB values, downsampling, and and sending them to the input of the network, like many examples do. I actually use over 100 independently trained neural networks that detect features, such as lines, shading patterns, etc. Much more like the human eye, and it works really well so far! The problem is I have quite a bit of training data. I show it over 100 examples of what a car looks like. Then 100 examples of what a person looks like. Then over 100 of what a dog looks like, etc. This is quite a bit of training data! Currently I am running at about one week to train the network. This is kind of killing my progress, as I need to adjust and retrain.

我正在使用 Neuroph ，作为低级神经网络API。我正在运行双四核机器（16核超线程），所以这应该很快。我的处理器百分比仅为5％。 Neuroph表现有什么技巧吗？还是Java性能一般？建议？我是一名认知心理学博士生，作为一名程序员，我态度不错，但对性能编程知之甚少。

I am using Neuroph, as the low-level neural network API. I am running a dual-quadcore machine(16 cores with hyperthreading), so this should be fast. My processor percent is at only 5%. Are there any tricks on Neuroph performance? Or Java performance in general? Suggestions? I am a cognitive psych doctoral student, and I am decent as a programmer, but do not know a great deal about performance programming.

推荐答案

是的，几个月前我去了那条路。也适用于大学项目。第一个问题是Neuroph。它致命的慢。 Neuroph很清楚主要的架构和性能问题，上周只有一篇关于代码项目的文章。

Yeah I went down that road a few months ago. Also for a university project. First problem is Neuroph. Its deadly slow. Neuroph has well know major architectural and performance issues, there was just an article about that last week on code project.

http://www.codeproject.com/KB/recipes/benchmark-neuroph-encog.aspx

我遵循了与本文作者类似的路径。从Neuroph切换到Encog是一个非常简单的端口。上面这篇文章的作者甚至还有另一篇文章比较了Encog，JOONE和Neuroph的语法，所以你可以比较一下。有关Encog的更多信息，

I followed a similar path as the author of this article. Switching from Neuroph to Encog is a real easy port. The author of the above article even has another that compares the syntax of Encog, JOONE and Neuroph, so you can compare that. For more info on Encog,

http：// www。 heatonresearch.com/encog

Encog将更多地利用您的核心。只需看看上面文章中的图表。

Encog will take more advantage of your cores too. Just look at the chart in the above article.

祝你好运！你的研究听起来真棒，我很想看到结果。

Good luck! Your research sounds really awesome, I would love to see the results.

这篇关于帮助Neuroph神经网络的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！