神经网络对输出数据进行归一化 [英] Neural Networks normalizing output data

问题描述

我有一个关于NN的训练数据以及预期的输出.每个输入都是10维向量，并具有1个预期输出.我已经使用高斯对训练数据进行了归一化，但是我不知道如何对输出进行归一化，因为它只有一维.有什么想法吗?

I have a training data for NN along with expected outputs. Each input is 10 dimensional vector and has 1 expected output.I have normalised the training data using Gaussian but I don't know how to normalise the outputs since it only has single dimension. Any ideas?

示例:

原始输入向量: -128.91，71.076，-100.75,4.2475，-98.811，77.219，4.4096，-15.382，-6.1477，-361.18

Raw Input Vector:-128.91, 71.076, -100.75,4.2475, -98.811, 77.219, 4.4096, -15.382, -6.1477, -361.18

归一化输入向量: -0.6049、1.0412，-0.3731、0.4912，-0.3571、1.0918、0.4925、0.3296、0.4056，-2.5168

Normalised Input Vector: -0.6049, 1.0412, -0.3731, 0.4912, -0.3571, 1.0918, 0.4925, 0.3296, 0.4056, -2.5168

以上输入的原始预期输出为1183.6，但我不知道如何将其标准化.我应该将期望的输出标准化为输入向量的一部分吗?

The raw expected output for the above input is 1183.6 but I don't know how to normalise that. Should I normalise the expected output as part of the input vector?

推荐答案

从问题的外观来看，您正在尝试实现某种回归算法.对于回归问题，通常不对输出进行归一化.对于您为回归系统提供的训练数据，预期输出应在预期范围内，或者仅是预期输出具有的任何数据.

From the looks of your problem, you are trying to implement some sort of regression algorithm. For regression problems you don't normally normalize the outputs. For the training data you provide for a regression system, the expected output should be within the range you're expecting, or simply whatever data you have for the expected outputs.

因此，您可以对训练进行归一化 输入，以使训练更快，但是您通常不对目标输出进行标准化.在测试时间或提供新输入时，请确保以与培训期间相同的方式对数据进行标准化.具体来说，在训练过程中使用完全相同的参数进行归一化，以训练对网络的任何测试输入.

Therefore, you can normalize the training inputs to allow the training to go faster, but you typically don't normalize the target outputs. When it comes to testing time or providing new inputs, make sure you normalize the data in the same way that you did during training. Specifically, use exactly the same parameters for normalization during training for any test inputs into the network.

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