R - 神经网络 - 传统的反向传播看起来很奇怪

Question

我正在试验 neuralnet 中的不同算法包但是当我尝试传统的backprop算法结果非常奇怪/令人失望。几乎所有的计算结果都是~.33？？？我假设我必须错误地使用算法，就像我使用默认值 rprop+ 运行它一样它确实区分了样本。当然，正常的反向传播并没有那么糟糕，特别是如果它能够如此迅速地收敛到提供的阈值。

library(neuralnet)
data(infert)

set.seed(123)
fit <- neuralnet::neuralnet(formula = case~age+parity+induced+spontaneous, 
                            data = infert, hidden = 3, 
                            learningrate = 0.01, 
                            algorithm =  "backprop", 
                            err.fct = "ce", 
                            linear.output = FALSE,
                            lifesign = 'full', 
                            lifesign.step = 100)

preds <- neuralnet::compute(fit, infert[,c("age","parity","induced","spontaneous")])$net.result

summary(preds)
       V1           
 Min.   :0.3347060  
 1st Qu.:0.3347158  
 Median :0.3347161  
 Mean   :0.3347158  
 3rd Qu.:0.3347162  
 Max.   :0.3347286  

这里的某些设置应该不同吗？

示例默认神经网络

set.seed(123)
fit <- neuralnet::neuralnet(formula = case~age+parity+induced+spontaneous, 
                            data = infert, hidden = 3, 
                            err.fct = "ce", 
                            linear.output = FALSE,
                            lifesign = 'full', 
                            lifesign.step = 100)

preds <- neuralnet::compute(fit, infert[,c("age","parity","induced","spontaneous")])$net.result

summary(preds)
       V1           
 Min.   :0.1360947  
 1st Qu.:0.1516387  
 Median :0.1984035  
 Mean   :0.3346734  
 3rd Qu.:0.4838288  
 Max.   :1.0000000 

Answer 1

建议您在输入神经网络之前对数据进行标准化。如果你这样做，那么你很高兴去:

library(neuralnet)
data(infert)

set.seed(123)
infert[,c('age','parity','induced','spontaneous')] <- scale(infert[,c('age','parity','induced','spontaneous')])
fit <- neuralnet::neuralnet(formula = case~age+parity+induced+spontaneous, 
                            data = infert, hidden = 3, 
                            learningrate = 0.01, 
                            algorithm =  "backprop", 
                            err.fct = "ce", 
                            linear.output = FALSE,
                            lifesign = 'full', 
                            lifesign.step = 100)

preds <- neuralnet::compute(fit, infert[,c("age","parity","induced","spontaneous")])$net.result
summary(preds)
       V1            
 Min.   :0.02138785  
 1st Qu.:0.21002456  
 Median :0.21463423  
 Mean   :0.33471568  
 3rd Qu.:0.47239818  
 Max.   :0.97874839  

实际上有一些关于 SO 处理这个的问题。 Why do we have to normalize the input for an artificial neural network? 似乎有一些最详细的信息。