Neural Network#

teaching: 20 exercises: 0 questions:

  • “How to use Neural Network in Machine Learning model” objectives:

  • “Learn how to use ANN in ML model” keypoints:

  • “ANN”

11 Neural Network#

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  • Formulation of Neural Network

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  • Here, x1,x2….xn are input variables. w1,w2….wn are weights of respective inputs. b is the bias, which is summed with the weighted inputs to form the net inputs.

  • Bias and weights are both adjustable parameters of the neuron.

  • Parameters are adjusted using some learning rules.

  • The output of a neuron can range from -inf to +inf.

  • The neuron doesn’t know the boundary. So we need a mapping mechanism between the input and output of the neuron.

    • This mechanism of mapping inputs to output is known as Activation Function.

  • Activation functions: image

  • Neural Network formulation

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  • Basic Type of Neural Network:

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11.1 Implementation#

install.packages("neuralnet")

Split the data

library(caret)
library(neuralnet)

datain <- mtcars
set.seed(123)
#Split training/testing
indT <- createDataPartition(y=datain$mpg,p=0.6,list=FALSE)
training <- datain[indT,]
testing  <- datain[-indT,]
#scale the data set
smax <- apply(training,2,max)
smin <- apply(training,2,min)
trainNN <- as.data.frame(scale(training,center=smin,scale=smax-smin))
testNN <- as.data.frame(scale(testing,center=smin,scale=smax-smin))

  • Fit the Neural Network using 1 hidden layer with 10 neurons using backpropagation:

set.seed(123)
ModNN <- neuralnet(mpg~cyl+disp+hp+drat+wt+qsec+carb,trainNN, hidden=10,linear.output = T)
plot(ModNN)

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#Predict using Neural Network
predictNN <- compute(ModNN,testNN[,c(2:7,11)])
predictmpg<- predictNN$net.result*(smax-smin)[1]+smin[1]
postResample(testing$mpg,predictmpg)