Implementation using nnet() library

######################################################################## 
##Chapter 1 - Introduction to Neural Networks - using R ################
###Simple R program to build, train and test neural networks ###########
### Classification based on 3 inputs and 1 categorical output ##########
########################################################################
###Choose the libraries to use
library(NeuralNetTools)
library(nnet)
###Set working directory for the training data
setwd("C:/R")
getwd()
###Read the input file
mydata=read.csv('RestaurantTips.csv',sep=",",header=TRUE)
mydata
attach(mydata)
names(mydata)
##Train the model based on output from input
model=nnet(CustomerWillTip~Service+Ambience+Food,
data=mydata,
size =5,
rang=0.1,
decay=5e-2,
maxit=5000)
print(model)
plotnet(model)
garson(model)
########################################################################

Let us go through the code line-by-line

To understand all the steps in the code just proposed, we will look at them in detail. First, the code snippet will be shown, and the explanation will follow.

library(NeuralNetTools)
library(nnet)
###Set working directory for the training data
setwd("C:/R")
getwd()
###Read the input file
mydata=read.csv('RestaurantTips.csv',sep=",",header=TRUE)
mydata
attach(mydata)
names(mydata)
##Train the model based on output from input
model=nnet(CustomerWillTip~Service+Ambience+Food,
data=mydata,
size =5,
rang=0.1,
decay=5e-2,
maxit=5000)
print(model)
> model=nnet(CustomerWillTip~Service+Ambience+Food,data=mydata, size =5, rang=0.1, decay=5e-2, maxit=5000)
# weights: 26
initial value 7.571002
iter 10 value 5.927044
iter 20 value 5.267425
iter 30 value 5.238099
iter 40 value 5.217199
iter 50 value 5.216688
final value 5.216665
converged
print(model)
> print(model)
a 3-5-1 network with 26 weights
inputs: Service Ambience Food
output(s): CustomerWillTip
options were - decay=0.05
plotnet(model)
garson(model)

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