神经网络经过训练并保存到文件后如何进行测试？
我使用互联网上的一些伪代码训练了一个深度信念神经网络（由堆叠的受限玻尔兹曼机组成）。问题是在训练它之后，即调整它的权重之后，我不清楚如何测试它。
我有一个输入图像和一个经过训练的神经网络。必须如何分类？我已将经过训练的网络保存到文件中。问题是我还没有彻底研究它背后的数学，因为我需要尽快完成这个项目。此外，谷歌搜索也没有提供任何明确的信息。
I've trained a deep belief neural network (formed by stacked restricted boltzmann machines) using some pseudo-code from the internet. The problem is after training it, i.e. after adjusting its weights, I have no clear idea how to test it.
I have an input image and a trained neural network. How must the classification be done? I've saved the trained network to a file. The problem is I haven't thoroughly studied the math behind it as I need this project done ASAP. Also, Googling didn't provide any clear information.
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我使用来自网络的一些伪代码训练了一个深度置信神经网络（由堆叠的受限玻尔兹曼机组成）。
这意味着您已经向神经网络“喂入”了由图像组成的对以及与之相关的值，对吗？如果是分类，则该值可能是
0/1；如果是回归，则该值可能是任何实数。测试它意味着您必须仅用图像“喂养”您的神经网络。在您的伪代码中，应该有两个函数：
void train(Image input, float trainValue)和另一个float Predict(Image input)。 （将Image更改为与您的情况相关的任何内容：vector、Matrix等...）您能给我们您的代码吗（或者在最少的伪代码）？
I've trained a deep belief neural network (formed by stacked restricted boltzmann machines) using some pseudo-code from net.
This means that you've "fed" your neural networks with pairs consisting of an image and a value associated with it, right? This value might be
0/1in case of classification or any real number in the case of regression.Testing it means that you've got to "feed" your neural network only with the image. In your pseudo-code, there's supposed to be two functions :
void train(Image input, float trainValue)and another onefloat predict(Image input). (ChangeImagewith whatever is relevant in your case :vector,Matrix, etc...)Can you give us your code (or at least pseudo code)?
一种常见的方法是使用三分之二的可用训练数据来训练神经网络。剩下的三分之一用于测试训练后的网络。可以更改训练/测试数据的比例以满足您的应用程序，但训练和测试组不要有偏见，这一点至关重要。您可以考虑将数据随机划分为两组，以确保不会无意中引入偏差。
One common approach is to train your NN on two-thirds of your available training data. The remaining third is then used to test the trained network. The ratio of training/testing data can be changed to meet your application, but it is critical that the training and test groups be free of bias. You might consider randomly partitioning your data into the two sets to ensure you don't inadvertently introduce bias.