r - 使用 R 上的反向传播神经网络模型生成预测会为所有观察返回相同的值

Question

我正在尝试使用经过训练的反向传播神经网络在新数据集上使用神经网络包来生成预测。我使用了“计算”功能，但最终所有观察结果都得到相同的值。我做错了什么？

# the data
Var1 <- runif(50, 0, 100)
sqrt.data <- data.frame(Var1, Sqrt=sqrt(Var1))

# training the model
backnet = neuralnet(Sqrt~Var1, sqrt.data, hidden=2, err.fct="sse", linear.output=FALSE, algorithm="backprop", learningrate=0.01)

print (backnet)

Call: neuralnet(formula = Sqrt ~ Var1, data = sqrt.data, hidden = 2,     learningrate = 0.01, algorithm = "backprop", err.fct = "sse",     linear.output = FALSE)

1 repetition was calculated.

        Error Reached Threshold Steps
1 883.0038185    0.009998448226  5001

valnet = compute(backnet, (1:10)^2)

summary (valnet$net.result)

      V1           
Min.   :0.9998572  
1st Qu.:0.9999620  
Median :0.9999626  
Mean   :0.9999505  
3rd Qu.:0.9999626  
Max.   :0.9999626  

print (valnet$net.result)

         [,1]
[1,] 0.9998572272
[2,] 0.9999477241
[3,] 0.9999617930
[4,] 0.9999625684
[5,] 0.9999625831
[6,] 0.9999625831
[7,] 0.9999625831
[8,] 0.9999625831
[9,] 0.9999625831
[10,] 0.9999625831

Answer 1

我能够使以下内容发挥作用:

library(neuralnet)

# the data
Var1 <- runif(50, 0, 100)
sqrt.data <- data.frame(Var1, Sqrt=sqrt(Var1))

# training the model
backnet = neuralnet(Sqrt~Var1, sqrt.data, hidden=10, learningrate=0.01)

print (backnet)


Var2<-c(1:10)^2

valnet = compute(backnet, Var2)

print (valnet$net.result)

返回:

     [,1]
[1,] 0.9341689395
[2,] 1.9992711472
[3,] 3.0012823496
[4,] 3.9968226732
[5,] 5.0038316976
[6,] 5.9992936957
[7,] 6.9991576925
[8,] 7.9996871591
[9,] 9.0000849977
[10,] 9.9891334545

根据neuralnet reference manual ，该包的默认训练算法是反向传播:

neuralnet is used to train neural networks using backpropagation, resilient backpropagation (RPROP) with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version (GRPROP) by Anastasiadis et al. (2005). The function allows flexible settings through custom-choice of error and activation function. Furthermore the calculation of generalized weights (Intrator O. and Intrator N., 1993) is implemented.