Consider a simple dataset of a square of numbers, which will be used to train a neuralnet function in R and then test the accuracy of the built neural network:

Our objective is to set up the weights and bias so that the model can do what is being done here. The output needs to be modeled on a function of input and the function can be used in future to determine the output based on an input:

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###Chapter 1 - Introduction to Neural Networks - using R ################ 
###Simple R program to build, train and test neural Networks############# 
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#Choose the libraries to use
library("neuralnet")
 
#Set working directory for the training data
setwd("C:/R")
getwd()
 
#Read the input file
mydata=read.csv('Squares.csv',sep=",",header=TRUE)
mydata
attach(mydata)
names(mydata)
 
#Train the model based on output from input
model=neuralnet(formula = Output~Input, 
                data = mydata, 
                hidden=10, 
                threshold=0.01 )
print(model)
 
#Lets plot and see the layers
plot(model)
 
#Check the data - actual and predicted
final_output=cbind (Input, Output, 
                    as.data.frame(model$net.result) )
colnames(final_output) = c("Input", "Expected Output", 
                           "Neural Net Output" )
print(final_output)
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