r - R 中的神经网络 - 为所有输入值获取相同的输出

Question

我正在尝试准备一个神经网络，根据“否”和“年龄”两个参数来预测产品的 claim 数量。 以下数据集是神经网络的输入。

structure(list(no = c(25305.4104099149, 49282.7650363303, 71596.161588407, 
93100.2399492689, 120575.89348652, 138907.168168798, 152853.150129645, 
164658.048266216, 203323.951054253, 217964.514231364, 232098.010631853, 
245528.300551639, 257729.677107825, 273017.858354583, 289943.942081732, 
307253.529762711, 322779.210756104, 338484.424561413, 354509.62945598, 
376508.167449508, 392559.686167136, 406403.704572922, 418237.95321136, 
428306.956736623, 443032.309329306, 462815.029777392, 483057.035564531, 
501119.337852308, 516468.28989971, 529231.965438745, 546230.529378035), 
age = c(63.5793740593707, 102.316649334314, 139.062062015527, 
159.908188195329, 221.139098010716, 243.371632144127, 255.656705912817, 
321.979062244126, 302.183543005839, 354.719062375634, 369.989444935937, 
415.562730056562, 445.18103403067, 487.443822982359, 522.664771025013, 
531.055799381952, 588.227179384567, 627.155320232965, 631.325866647729, 
656.228738193787, 674.252217317525, 717.171080443709, 741.672049752712, 
788.251261134812, 798.113504685438, 831.731613476353, 834.814816968948, 
868.754851062387, 891.362029551517, 902.022293484355, 940.795814337874), 
claims = c(430.964844652385, 732.996578820216, 1702.3121722574,
2251.25233558302, 2197.47809502525, 2567.04757960458, 3031.86042202782, 
3156.90611199034, 3863.87816105778, 4111.89975688297, 3775.93067659216, 
4012.49766196774, 4312.44312947351, 4180.22855748422, 5089.44484309535, 
4257.88997259059, 4880.90586497903, 4463.20376379347, 4240.41527392955, 
4784.76670484109, 5402.00394657619, 4599.18095060565, 4003.91468429224, 
4029.72081951048, 3774.73142127963, 3920.30299815048, 5640.00980484863, 
5609.58082520698, 4689.03553448074, 5021.68591677232, 6583.74468086371), 
expense = c(152020.866139235, 435514.001634924, 752077.230564814, 
1206688.79158373, 1291739.60434588, 1421308.36224772, 2050740.38970347, 
1975198.4497045, 2274222.98020964, 2579595.43870509, 2129258.22735162, 
2135819.30924201, 2670328.44657756, 2908678.20678848, 2647633.44523976, 
2416617.98013342, 2312104.28655066, 2603487.56885879, 2598480.12097434, 
2747610.29007465, 2856983.01477582, 2453661.76656217, 2557917.28443019, 
2952529.81656875, 2177766.2760928, 2077444.9802322, 3542576.76934085, 
4050503.17869956, 3737028.1474149, 3497074.2505681, 3541174.73116362)),
.Names = c("no", "age", "claims", "expense"), row.names = c(NA, -31L), 
class = "data.frame")

我正在尝试的神经网络是

claimnet = neuralnet(claims~no+age,data=claimdata,hidden=10,threshold=0.01,err.fct='sse')

我从 claimnet$net.result 获得的所有 31 条记录的输出/拟合结果为 3913.491497。当我尝试使用这个神经网络进行计算时，结果也是如此。我认为必须传递一些参数才能获得正确的输出。

请让我知道我哪里出错了。

Answer 1

我可能是错的，但我很确定您需要在训练人工神经网络之前对数据进行标准化。您可以使用标准评分方法将数据从 -1 标准化为 1。另外，请确保您的数据呈正态分布。但是，我不确定神经网络是否强制这样做。

使用您的数据，这就是我所做的。

plot(density(claimdata$no));shapiro.test(claimdata$no)
plot(density(claimdata$age));shapiro.test(claimdata$age)
plot(density((claimdata$claims)^2));shapiro.test(claimdata$claims^2)
claimdata$claimsSQ<- claimdata$claims^2

fml<- as.formula("claimsSQ ~ no + age");
data_Train<- claimdata[complete.cases(claimdata),];
scMeans<- apply(data_Train,2,mean);
scSTDEV<- apply(data_Train,2,sd);
sc_Train<- scale(data_Train);

#Select training samples
inTrain <- sample(1:nrow(sc_Train), floor(.9*nrow(sc_Train)));
# Get the predictor data 
trainingPredictors <- sc_Train[inTrain, ];
# Get Data not used in Training set
testPredictors <- sc_Train[-inTrain,];

train.nnet<- nnet(fml,data=trainingPredictors,linout=T,
                  size = 2, rang = 0.1,decay = 5e-4, maxit = 200);

res.nnet<- predict(train.nnet,testPredictors);

results<-  cbind(claimdata$claims[-inTrain],
                 sqrt(res.nnet*scSTDEV[5]+scMeans[5]));results;

另外，请看一下， “第 3 章 - 神经网络数据中的数据准备 人工神经网络外汇汇率预测分析 国际运筹学与管理科学丛书，第 107 卷，2007 年，第 39-62 页。

希望这有帮助，

干杯。