神经网络预测为所有预测返回相同的值 [英] neuralnet prediction returns the same values for all predictions

问题描述

我正在尝试使用Neuronet软件包构建神经网络，但遇到了一些麻烦.我已经成功使用nnet软件包，但是对于neuralnet软件包却没有运气.我已经阅读了整个文档包，找不到解决方案，或者也许我找不到它.

I'm trying to build a neural net with the neuralnet package and I'm having some trouble with it. I've been successful with the nnet package but no luck with the neuralnet one. I have read the whole documentation package and can't find the solution, or maybe I'm not able to spot it.

我正在使用的训练命令是

The training command I'm using is

nn<-neuralnet(V15 ~ V1 + V2 + V3 + V4 + V5 + V6 + V7 + V8 + V9 + V10 + V11 + V12 + V13 + V14,data=test.matrix,lifesign="full",lifesign.step=100,hidden=8) 

并用于预测

result<- compute(nn,data.matrix)$net.result

培训花费的时间比nnet培训要长得多.我尝试使用与nnet相同的算法(反向传播而不是弹性反向传播)，并且什么也没有做，也更改了激活函数(和linear.output=F)以及几乎所有其他内容，结果没有得到改善.预测值都相同.我不明白为什么nnet对我有用，而neuralnet却不起作用.

The training takes a whole lot longer than the nnet training. I have tried using the same algorithm as nnet (backpropagation instead of resilent backpropagation) and nothing, changed the activation function too (and the linear.output=F) and pretty much everything else, and the result didn't improved. Predicted values are all the same. I don't understand why the nnet works for me, while the neuralnet one doesn't.

我真的可以使用一些帮助，我对(神经网络和R)这两种东西的(缺乏)理解可能是原因，但找不到原因.

I could really use some help, my (lack of) understanding of both things (neural nets and R) it's probably the cause, but can't find why.

我的数据集来自 UCI .我想使用神经网络进行二进制分类.数据样本为:

My dataset is from UCI. I want to use the neural network for a binary classification. A sample of the data would be:

25,Private,226802,11th,7,Never-married,Machine-op-inspct,Own-child,Black,Male,0,0,40,United-States,<=50K.
38,Private,89814,HS-grad,9,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,50,United-States,<=50K.
28,Local-gov,336951,Assoc-acdm,12,Married-civ-spouse,Protective-serv,Husband,White,Male,0,0,40,United-States,>50K.
44,Private,160323,Some-college,10,Married-civ-spouse,Machine-op-inspct,Husband,Black,Male,7688,0,40,United-States,>50K.
18,?,103497,Some-college,10,Never-married,NA,Own-child,White,Female,0,0,30,United-States,<=50K.
34,Private,198693,10th,6,Never-married,Other-service,Not-in-family,White,Male,0,0,30,United-States,<=50K.
29,?,227026,HS-grad,9,Never-married,?,Unmarried,Black,Male,0,0,40,United-States,<=50K.
63,Self-emp-not-inc,104626,Prof-school,15,Married-civ-spouse,Prof-specialty,Husband,White,Male,3103,0,32,United-States,>50K.
24,Private,369667,Some-college,10,Never-married,Other-service,Unmarried,White,Female,0,0,40,United-States,<=50K.
55,Private,104996,7th-8th,4,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,10,United-States,<=50K.
65,Private,184454,HS-grad,9,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,6418,0,40,United-States,>50K.
36,Federal-gov,212465,Bachelors,13,Married-civ-spouse,Adm-clerical,Husband,White,Male,0,0,40,United-States,<=50K.
26,Private,82091,HS-grad,9,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,39,United-States,<=50K.

转换为矩阵，因子为数值:

Converted into a matrix, with the factors as numerical values:

V1  V2  V3  V4  V5  V6  V7  V8  V9  V10 V11 V12 V13 V14 V15
39  7   77516   10  13  5   1   2   5   2   2174    0   40  39  0
50  6   83311   10  13  3   4   1   5   2   0   0   13  39  0
38  4   215646  12  9   1   6   2   5   2   0   0   40  39  0
53  4   234721  2   7   3   6   1   3   2   0   0   40  39  0
28  4   338409  10  13  3   10  6   3   1   0   0   40  5   0
37  4   284582  13  14  3   4   6   5   1   0   0   40  39  0
49  4   160187  7   5   4   8   2   3   1   0   0   16  23  0
52  6   209642  12  9   3   4   1   5   2   0   0   45  39  1
31  4   45781   13  14  5   10  2   5   1   14084   0   50  39  1
42  4   159449  10  13  3   4   1   5   2   5178    0   40  39  1
37  4   280464  16  10  3   4   1   3   2   0   0   80  39  1
30  7   141297  10  13  3   10  1   2   2   0   0   40  19  1
23  4   122272  10  13  5   1   4   5   1   0   0   30  39  0

预测值摘要:

      V1           
 Min.   :0.2446871  
 1st Qu.:0.2446871  
 Median :0.2446871  
 Mean   :0.2451587  
 3rd Qu.:0.2446871  
 Max.   :1.0000000  

Wilcoxon-Mann-Whitney检验(曲线下的面积)的值表明，预测性能与随机变量几乎相同.

Value of the Wilcoxon-Mann-Whitney test (area under the curve) shows that the prediction performance is virtualy the same as a random.

performance(predneural,"auc")@y.values
[1] 0.5013319126

推荐答案

使用神经网络获得怪异结果时要考虑的第一个原因是归一化.您的数据必须进行标准化，否则，是的，训练将导致偏斜的NN始终产生相同的结果，这是常见症状.

The first reason to consider when you get weird results with neural networks is normalization. Your data must be normalized, otherwise, yes, the training will result in skewed NN which will produce the same outcome all the time, it is a common symptom.

看您的数据集，有>> 1值，这意味着它们被NN对待的方式基本上相同.其原因是，传统上使用的响应函数在(大约)0附近的某个范围内(几乎)是常数.

Looking at your data set, there are values >>1 which means they are all treated by NN essentially the same. The reason for it is that the traditionally used response functions are (almost) constant outside some range around 0.

在将数据输入神经网络之前，总是对其进行标准化.

Always normalize your data before feeding it into a neural network.

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