了解Matlab模式识别神经网络图

debugcn 发表于 Dev

Noobmaster69

我目前正在进行一个有关车辆分类的项目，该项目现已接近完成，但是我对从神经网络获得的图块感到有些困惑

我使用了230张图像[90=Hatchbacks,90=Sedans,50=SUVs]对80个特征点进行了分类。因此，我vInput是一个[80x230]矩阵，而我vTarget是一个[3x230]矩阵

分类器效果很好，但我不了解这些图或它们是否异常。

我的神经网络

然后，我在PLOT部分中单击了这4个图，并依次得到它们。

性能图

训练状态

混淆图

接收器工作特性图
ROC图

I know the images they are a lots of images but I know nothing about them. On the matlab documentation they just train the system and plot the graph
So please someone briefly explain them to me or show me some good links to learn them.

Leonid Beschastny

First two plots shows training statistscs.

Performance Plot shows you mean square error dynamics for all your datasets in logarithmic scale. Training MSE is always decreasing, so its validation and test MSE you should be interested in. Your plot shows a perfect training.

Training State shows you some other training statistics.

Gradient is a value of backpropagation gradient on each iteration in logarithmic scale. 5e-7 means that you reached the bottom of the local minimum of your goal function.

Validation fails are iterations when validation MSE increased its value. A lot of fails means owertrainig, but in you case its OK. Matlab automatically stops training after 6 fails in a row.

The other two plots shows you the results of your network simulation after training.

Confusion Plot. In your case its 100% accurate. Green cells represent correct answers and red cells represent all types of incorrect answers.

For example, you may read the first one (training set) as: "59 samples from the class 1 was corrctly classified as class 1, 13 samples from the class 2 was corrctly classified as class 2 and 6 samples from the class 3 was corrctly classified as class 3".

接收器工作特性图显示相同的内容，但方式不同-使用ROC曲线：

在此处输入图片说明

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编辑于2021-06-4

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