小编Rwa*_*wak的帖子

我正在尝试用神经网络包构建一个神经网络,我遇到了一些问题.我已经成功使用了这个nnet包,但没有运气neuralnet.我已阅读整个文档包,无法找到解决方案,或者我可能无法发现它.

我正在使用的训练命令是

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

培训比nnet培训需要更长的时间.我尝试使用相同的算法nnet(backpropagation而不是resilent backpropagation),什么都没有,改变了激活函数(和linear.output=F)以及其他几乎所有,结果没有改进.预测值都是相同的.我不明白为什么nnet对我有用,而那个neuralnet没有.

我真的可以使用一些帮助,我(缺乏)理解这两件事(神经网络和R)这可能是原因,但找不到原因.

我的数据集来自UCI.我想使用神经网络进行二进制分类.数据样本将是:

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:

V1  V2  V3  V4  V5  V6  V7  V8  V9  V10 V11 V12 V13 V14 V15
39 …