我正在尝试使用neuralnet包中的不同算法，但是当我尝试传统的backprop算法时，结果却很奇怪/令人失望。几乎所有计算结果都是〜.33 ???我假设我一定使用了错误的算法，就像我使用默认的rprop+来运行该算法一样，它确实区分了样本。当然，正常的反向传播也不错，特别是如果它能够迅速收敛到提供的阈值时。
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")])$net.result
summary(preds)
V1
Min. :0.3347060
1st Qu.:0.3347158
Median :0.3347161
Mean :0.3347158
3rd Qu.:0.3347162
Max. :0.3347286
这里有些设置应该不同吗？
示例默认神经网络
set.seed(123)
fit <- neuralnet::neuralnet(formula = case~age+parity+induced+spontaneous,
data = infert, hidden = 3,
err.fct = "ce",
linear.output = FALSE,
lifesign = 'full',
lifesign.step = 100)
preds <- neuralnet::compute(fit, infert[,c("age","parity","induced","spontaneous")])$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
最佳答案
建议您在馈入神经网络之前将数据标准化。如果您这样做，那么就很好了：
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的处理实际上存在一些问题。 Why do we have to normalize the input for an artificial neural network?似乎有一些最详细的信息。