神经网络验证的概念 [英] Concept of validate for neural network

问题描述

我对NN验证的概念有疑问.假设我有100组输入变量(例如8个输入，X1，...，X8)，并且想要预测一个Target(Y).现在我有两种使用NN的方法: 1-使用70组数据来训练NN，然后使用经过训练的NN预测其他30套目标以进行验证，然后将这30套目标的输出VS Target绘制为验证图. 2-使用100组数据来训练NN，然后将所有输出分为两部分(70％和30％).绘制70％的输出VS对应的目标作为训练图.然后将其他30％的输出与对应的目标作图，作为验证图

I have a problem with concept of Validation for NN. suppose I have 100 set of input variables (for example 8 input, X1,...,X8) and want to predict one Target(Y). now I have two ways to use NN: 1- use 70 set of data for training NN and then use trained NN to predict other 30 sets of Target for validation and then plot output VS Target for this 30 sets as validation plot. 2- use 100 sets of data for training NN and then divide all outputs to two part (70% and 30%). plot 70% of outputs VS corresponding Targets as Training plot. then plot other 30% outputs VS their corresponding Targets as validation plot

哪个是正确的?

此外，用新数据集检查NN和验证数据集有什么区别?

Also, what the difference between checking NN with new data set and validation data set??

谢谢

推荐答案

如果数据已经用于训练，则不能使用数据进行验证，因为受过训练的NN将知道"您的验证示例.这种验证的结果将有很大的偏差.我肯定会使用第一种方式.

You cannot use data for validation, if it has been already used for the training, because the trained NN will already "know" your validation examples. The result of such validation will be very biased. I would for sure use the first way.

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