A neural network exaple in R. Full article at: http://datascienceplus.com/fitting-neural-network-in-r/

neuralnetR.R

# Set a seed
set.seed(500)

library(MASS)
data <- Boston

# Check that no data is missing
apply(data,2,function(x) sum(is.na(x)))

# Train-test random splitting for linear model
index <- sample(1:nrow(data),round(0.75*nrow(data)))
train <- data[index,]
test <- data[-index,]

# Fitting linear model
lm.fit <- glm(medv~., data=train)
summary(lm.fit)

# Predicted data from lm
pr.lm <- predict(lm.fit,test)

# Test MSE
MSE.lm <- sum((pr.lm - test$medv)^2)/nrow(test)

#-------------------------------------------------------------------------------
# Neural net fitting

# Scaling data for the NN
maxs <- apply(data, 2, max) 
mins <- apply(data, 2, min)
scaled <- as.data.frame(scale(data, center = mins, scale = maxs - mins))

# Train-test split
train_ <- scaled[index,]
test_ <- scaled[-index,]

# NN training
library(neuralnet)
n <- names(train_)
f <- as.formula(paste("medv ~", paste(n[!n %in% "medv"], collapse = " + ")))
nn <- neuralnet(f,data=train_,hidden=c(5,3),linear.output=T)

# Visual plot of the model
plot(nn)

# Predict
pr.nn <- compute(nn,test_[,1:13])

# Results from NN are normalized (scaled)
# Descaling for comparison
pr.nn_ <- pr.nn$net.result*(max(data$medv)-min(data$medv))+min(data$medv)
test.r <- (test_$medv)*(max(data$medv)-min(data$medv))+min(data$medv)

# Calculating MSE
MSE.nn <- sum((test.r - pr.nn_)^2)/nrow(test_)

# Compare the two MSEs
print(paste(MSE.lm,MSE.nn))

# Plot predictions
par(mfrow=c(1,2))

plot(test$medv,pr.nn_,col='red',main='Real vs predicted NN',pch=18,cex=0.7)
abline(0,1,lwd=2)
legend('bottomright',legend='NN',pch=18,col='red', bty='n')

plot(test$medv,pr.lm,col='blue',main='Real vs predicted lm',pch=18, cex=0.7)
abline(0,1,lwd=2)
legend('bottomright',legend='LM',pch=18,col='blue', bty='n', cex=.95)

# Compare predictions on the same plot
plot(test$medv,pr.nn_,col='red',main='Real vs predicted NN',pch=18,cex=0.7)
points(test$medv,pr.lm,col='blue',pch=18,cex=0.7)
abline(0,1,lwd=2)
legend('bottomright',legend=c('NN','LM'),pch=18,col=c('red','blue'))

#-------------------------------------------------------------------------------
# Cross validating

library(boot)
set.seed(200)

# Linear model cross validation
lm.fit <- glm(medv~.,data=data)
cv.glm(data,lm.fit,K=10)$delta[1]


# Neural net cross validation
set.seed(450)
cv.error <- NULL
k <- 10

# Initialize progress bar
library(plyr) 
pbar <- create_progress_bar('text')
pbar$init(k)

for(i in 1:k){
    index <- sample(1:nrow(data),round(0.9*nrow(data)))
    train.cv <- scaled[index,]
    test.cv <- scaled[-index,]
    
    nn <- neuralnet(f,data=train.cv,hidden=c(5,2),linear.output=T)
    
    pr.nn <- compute(nn,test.cv[,1:13])
    pr.nn <- pr.nn$net.result*(max(data$medv)-min(data$medv))+min(data$medv)
    
    test.cv.r <- (test.cv$medv)*(max(data$medv)-min(data$medv))+min(data$medv)
    
    cv.error[i] <- sum((test.cv.r - pr.nn)^2)/nrow(test.cv)
    
    pbar$step()
}

# Average MSE
mean(cv.error)

# MSE vector from CV
cv.error

# Visual plot of CV results
boxplot(cv.error,xlab='MSE CV',col='cyan',
        border='blue',names='CV error (MSE)',
        main='CV error (MSE) for NN',horizontal=TRUE)

thank you

Thank you for this contribution

Hi Mick, this is a very helpful example! Thank you very much. Do you have any tutorials on RNN with time series data? Example scenario: Dependent variable type continues numeric, and input observations which would have, year, month, week of the year, fiscal day od the week, hour and min intervals for each hour? Many thanks.

Thank You! - do you by any chance know why this error would be coming up when running nm <- neuralnet(...): Error in x - y : non-conformable arrays ?

Thanks!

Thank you for this contribution. A good code for beginner !