最佳的数据集和软件，用于比较自制和专业人工神经网络/前馈神经网络之间的准确性 [英] Best available data sets and software to compare accuracy between homemade and professional ANNs / feedfoward neural networks
问题描述
对于前馈神经网络，我有一些稍微修改/非传统的设置，我希望将它们与当今专业使用的精度进行比较.是否有可以用作基准的特定数据集或数据集类型? IE. 通常用于某某任务的ANN样式相对于该数据集的准确性为98％".拥有多种功能非常好，一对用于统计分析，一对用于图像和语音识别，等等.
I have a couple slightly modified / non-traditional setups for feedforward neural networks which I'd like to compare for accuracy against the ones used professionally today. Are there specific data sets, or types of data sets, which can be used as a benchmark for this? I.e. "the style of ANN typically used for such-and-such a task is 98% accurate against this data set." It would be great to have a variety of these, a couple for statistical analysis, a couple for image and voice recognition, etc.
基本上，是否有一种方法可以将我整理的ANN与专业使用的ANN进行比较，以完成各种任务?我可以为数据或软件付费，但是当然更喜欢免费.
Basically, is there a way to compare an ANN I've put together against ANNs used professionally, across a variety of tasks? I could pay for data or software, but would prefer free of course.
推荐答案
CMU has some benchmarks for neural networks: Neural Networks Benchmarks
快速人工神经网络库(FANN)具有一些被广泛使用的基准: FANN .下载源代码(版本2.2.0)并查看目录数据集，格式非常简单.总会有一个训练集(x.train)和一个测试集(x.test).文件的开头是实例数，输入数和输出数.接下来的几行是第一个实例的输入和第一个实例的输出，依此类推.您可以在目录examples中找到带有FANN的示例程序.我认为他们甚至可以与以前版本中的其他库进行详细比较.
The Fast Artificial Neural Networks library (FANN) has some benchmarks that are widely used: FANN. Download the source code (version 2.2.0) and look at the directory datasets, the format is very simple. There is always a training set (x.train) and a test set (x.test). At the beginning of the file is the number of instances, the number of inputs and the number of outputs. The next lines are the input of the first instance and the output of the first instance and so on. You can find example programs with FANN in the directory examples. I think they even had detailed comparisons to other libraries in previous versions.
我认为FANN的大多数基准测试(如果不是全部)都来自Proben1. Google为此，Lutz Prechelt发表了一篇论文，其中有详细的描述和比较.
I think most of FANN's benchmarks if not all are from Proben1. Google for it, there is a paper from Lutz Prechelt with detailed descriptions and comparisons.
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