I am trying to figure out how to make the neuralnet package to work. I did some tests with data I created and with their outcomes (about 50 rows of data and three columns with the fourth being the outcome I wanted and it was made from simple mathematical executions like summing the other three columns) and so far so good. Then I decided to apply the package on real data. I downloaded the mpg dataset from here http://vincentarelbundock.github.io/Rdatasets/datasets.html
I was running the code bellow:
net<- neuralnet(cty~displ+year+cyl+hwy,
datain, hidden=3)
Even if I have 3 hidden layers, or 8 or 18 the error is the same and the time that the package processes the data is relatively small from this amount of data (234 lines):
Error Reached Threshold Steps
1 2110.173077 0.006277805853 54
Any good advice for this?
It's a scale problem i guess, you can normalize or scale it.
There are differences between scaling
and normalizing
, it will affect your results and worths a separate question on SO:
norm.fun = function(x){
(x - min(x))/(max(x) - min(x))
}
require(ggplot2) # load mpg dataset
require(neuralnet)
data = mpg[, c('cty', 'displ', 'year', 'cyl', 'hwy')]
data.norm = apply(data, 2, norm.fun)
net = neuralnet(cty ~ displ + year + cyl + hwy, data.norm, hidden = 2)
Then you can denormalize the data
# restore data
y.net = min(data[, 'cty']) + net$net.result[[1]] * range(data[, 'cty'])
plot(data[, 'cty'], col = 'red')
points(y.net)
data.scaled = scale(data)
net = neuralnet(cty ~ displ + year + cyl + hwy, data.scaled, hidden = 2)
# restore data
y.sd = sd(data[, 'cty'])
y.mean = mean(data[, 'cty'])
y.net = net$net.result[[1]] * y.sd + y.mean
plot(data[, 'cty'], col = 'red')
points(y.net)
You can also try the nnet package, it's very fast:
require(nnet)
data2 = mpg
data2$year = scale(data2$year)
fit = nnet(cty ~ displ + year + cyl + hwy, size = 10, data = data2, linout = TRUE)
plot(mpg$cty)
points(fit$fitted.values, col = 'red')
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