R - 神经网络 - 传统的反向传播似乎很奇怪

Question

我正在尝试 neuralnet 包中的不同算法，但是当我尝试传统的 backprop 算法时，结果非常 strange/disappointing。几乎所有的计算结果都是~.33？？？我假设我一定是错误地使用了算法，就好像我运行它与默认 rprop+ 它确实区分样本一样。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有所不同吗？

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归一化。如果你这样做了，那么你就可以开始了：

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

关于 SO 处理这个问题实际上有几个问题。 Why do we have to normalize the input for an artificial neural network? 似乎有一些最详细的内容。

R - 神经网络 - 传统的反向传播似乎很奇怪

R - neuralnet - Traditional backprop seems strange

r

backpropagation

neural-network