如何标准化神经网络的坐标输入 [英] How to normalize coordinates inputs for neural networks

问题描述

我正在使用神经网络进行项目分类签名识别.因此，我进行了所有特征提取活动.特征提取的结果是，有些特征作为一组坐标(x和y).所以我以前知道把那些作为神经网络输入应该对其进行归一化.所以我不知道在将坐标作为NN输入之前归一化的确切方法.是否有任何方法可以解决这个问题? 谢谢.

I am doing project regrading signature recognition using Neural Networks.So in there I did all feature extraction activities.In result of feature extraction,there are some features which are as set of coordinates(x and y).So I know before put those as neural network input it should be normalize.So I don't know the exact way to normalize coordinates before it give as NN input.Is there are any method to approach that problem? Thank you.

推荐答案

序列化它们！

Serialize 'em!

# output from        |output coordinates
# cluster algorithm  |
[_ X X _ _]           (0,0) (0,1) (0,2) (0,3) (0,4)
[_ X _ X _]           (1,0) (1,1) (1,2) (1,3) (1,4)
[_ X X _ _]           (2,0) (2,1) (2,2) (2,3) (2,4)
[_ X _ _ _]           (3,0) (3,1) (3,2) (3,3) (3,4)
[_ X _ _ _]           (4,0) (4,1) (4,2) (4,3) (4,4)

# output from cluster algorithm, serialized:
[_ X X _ _ _ X _ X _ _ X X _ _ _ X _ _ _ _ X _ _ _ ]

# the corresponding coordinates for instructional purposes:
[(0,0) (0,1) (0,2) (0,3) (0,4) (1,0) (1,1) (1,2) (1,3) (1,4) (2,0) (2,1) (2,2) (2,3) (2,4) (3,0) (3,1) (3,2) (3,3) (3,4) (4,0) (4,1) (4,2) (4,3) (4,4)]

并将其输入到您的网络中！

And feed this into your network!

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您的聚类算法应该对输入的图像进行足够的聚类"，以便输入的数量可以接受.

Your clustering algorithm should "cluster your input images enough", so that the number of inputs is acceptable.

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