R code for the example in the video:
Neural networks with continuous output | ANN vs Regression
rm(list=ls())
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x)))
}
Age=c(1,2,2,3,4,5,6,7,8,9,10,10,11)
Price=c(29,25,21,18,15,15,12,10,7,5,6,4,4)
df=data.frame(Age,Price,Price_n=normalize(Price),Age_n=normalize(Age))
plot(Age, Price, ylab="Price",xlab="Age",col="blue",cex=1.2,ylim=c(0,35),xlim=c(0,12))
set.seed(378) # To get the same numbers as in my example
library(neuralnet)
nn <- neuralnet(Price_n ~ Age_n, data=df, hidden=c(2), linear.output=TRUE,
threshold=1e-4,rep=20,act.fct = "logistic")
i=which.min(nn$result.matrix[1,]) # Select network with the lowest error
x=seq(0,1,0.01)
df2=data.frame(Age=x)
yy=predict(nn,df2, rep = i)* abs(diff(range(Price))) + min(Price)# Change back to original scale
tt=x*abs(diff(range(Age))) + min(Age)# Change back to original scale
lines(tt,yy)
yhat=predict(nn,data.frame(Age=normalize(Age)), rep = i)* abs(diff(range(Price))) + min(Price)
(SSE=sum((Price-yhat)^2))
plot(nn,rep=i)