First Neural networks in R

This is so exciting. I am going to reward myself somthing nice if I could do it. Like a headphone or keyboard.

getwd()

[1] "C:/Users/Maxwell/Desktop/Learn R"

setwd("C:/Users/Maxwell/Desktop/Learn R")
library(tidyverse)

Loading tidyverse: ggplot2
Loading tidyverse: tibble
Loading tidyverse: tidyr
Loading tidyverse: readr
Loading tidyverse: purrr
Loading tidyverse: dplyr
Conflicts with tidy packages ------------------------------------------------------------------------------------
filter(): dplyr, stats
lag():    dplyr, stats

concrete=read.csv("concrete.csv")
str(concrete)

'data.frame':   1030 obs. of  9 variables:
 $ cement      : num  540 540 332 332 199 ...
 $ slag        : num  0 0 142 142 132 ...
 $ ash         : num  0 0 0 0 0 0 0 0 0 0 ...
 $ water       : num  162 162 228 228 192 228 228 228 228 228 ...
 $ superplastic: num  2.5 2.5 0 0 0 0 0 0 0 0 ...
 $ coarseagg   : num  1040 1055 932 932 978 ...
 $ fineagg     : num  676 676 594 594 826 ...
 $ age         : int  28 28 270 365 360 90 365 28 28 28 ...
 $ strength    : num  80 61.9 40.3 41 44.3 ...

summary(concrete)

     cement           slag            ash             water        superplastic      coarseagg     
 Min.   :102.0   Min.   :  0.0   Min.   :  0.00   Min.   :121.8   Min.   : 0.000   Min.   : 801.0  
 1st Qu.:192.4   1st Qu.:  0.0   1st Qu.:  0.00   1st Qu.:164.9   1st Qu.: 0.000   1st Qu.: 932.0  
 Median :272.9   Median : 22.0   Median :  0.00   Median :185.0   Median : 6.400   Median : 968.0  
 Mean   :281.2   Mean   : 73.9   Mean   : 54.19   Mean   :181.6   Mean   : 6.205   Mean   : 972.9  
 3rd Qu.:350.0   3rd Qu.:142.9   3rd Qu.:118.30   3rd Qu.:192.0   3rd Qu.:10.200   3rd Qu.:1029.4  
 Max.   :540.0   Max.   :359.4   Max.   :200.10   Max.   :247.0   Max.   :32.200   Max.   :1145.0  
    fineagg           age            strength    
 Min.   :594.0   Min.   :  1.00   Min.   : 2.33  
 1st Qu.:731.0   1st Qu.:  7.00   1st Qu.:23.71  
 Median :779.5   Median : 28.00   Median :34.45  
 Mean   :773.6   Mean   : 45.66   Mean   :35.82  
 3rd Qu.:824.0   3rd Qu.: 56.00   3rd Qu.:46.13  
 Max.   :992.6   Max.   :365.00   Max.   :82.60  

important to normalize the values

normalize=function(data){
  return(
    (data-min(data) )/(max(data)-min(data) )
  )
}
concrete_n=sapply(concrete,normalize)
summary(concrete_n)

     cement            slag              ash             water         superplastic      coarseagg     
 Min.   :0.0000   Min.   :0.00000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.2063   1st Qu.:0.00000   1st Qu.:0.0000   1st Qu.:0.3442   1st Qu.:0.0000   1st Qu.:0.3808  
 Median :0.3902   Median :0.06121   Median :0.0000   Median :0.5048   Median :0.1988   Median :0.4855  
 Mean   :0.4091   Mean   :0.20561   Mean   :0.2708   Mean   :0.4774   Mean   :0.1927   Mean   :0.4998  
 3rd Qu.:0.5662   3rd Qu.:0.39775   3rd Qu.:0.5912   3rd Qu.:0.5607   3rd Qu.:0.3168   3rd Qu.:0.6640  
 Max.   :1.0000   Max.   :1.00000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
    fineagg            age             strength     
 Min.   :0.0000   Min.   :0.00000   Min.   :0.0000  
 1st Qu.:0.3436   1st Qu.:0.01648   1st Qu.:0.2664  
 Median :0.4654   Median :0.07418   Median :0.4001  
 Mean   :0.4505   Mean   :0.12270   Mean   :0.4172  
 3rd Qu.:0.5770   3rd Qu.:0.15110   3rd Qu.:0.5457  
 Max.   :1.0000   Max.   :1.00000   Max.   :1.0000  

split the data

library(caret)

Loading required package: lattice

Attaching package: <U+393C><U+3E31>caret<U+393C><U+3E32>

The following object is masked from <U+393C><U+3E31>package:purrr<U+393C><U+3E32>:

    lift

index=createDataPartition(concrete$strength,p=0.8,list=FALSE)
train_con=concrete_n[index,]
test_con=concrete_n[-index,]

multilayer feedforward neural network

#install.packages("neuralnet")
library(neuralnet)

Attaching package: <U+393C><U+3E31>neuralnet<U+393C><U+3E32>

The following object is masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:

    compute

I have successfully load the library. So exciting. No bugs need to be fixed.

Call the function

library(neuralnet)
# could not use . to represent all features in the dataset
nns=neuralnet(strength~cement + slag
 + ash + water + superplastic + coarseagg + fineagg + age,data=train_con,hidden=1)
plot(nns)

# you need to take out the target value out of the data frame for predicting
nn_pred=compute(nns, test_con[,1:8])
std_nn=(nn_pred$net.result-test_con[,9])^2%>%mean%>%sqrt
std_nn

[1] 0.1248886747

cor(nn_pred$net.result,test_con[,9])

             [,1]
[1,] 0.8047687632

let us use more hidden nodes

library(neuralnet)
# Need to change the stepmax for algorithm to converge
nns5=neuralnet(strength~cement + slag
 + ash + water + superplastic + coarseagg + fineagg + age,data=train_con,hidden=5,stepmax = 10^9)
plot(nns5)

nn5_pred=compute(nns5,test_con[,1:8])
std_nn5=(nn5_pred$net.result-test_con[,9])^2%>%mean
std_nn5

[1] 0.005594889873

cor(nn5_pred$net.result,test_con[,9])

             [,1]
[1,] 0.9345892787

This is excellent result

what if we use more hidden layers

library(neuralnet)
# Need to change the stepmax for algorithm to converge
nns10=neuralnet(strength~cement + slag
               + ash + water + superplastic + coarseagg + fineagg + age,data=train_con,hidden=10,stepmax = 10^9)
plot(nns10)

nn10_pred=compute(nns10,test_con[,1:8])
std_nn10=(nn10_pred$net.result-test_con[,9])^2%>%mean
std_nn10

[1] 0.00543611784

cor(nn10_pred$net.result,test_con[,9])

             [,1]
[1,] 0.9369204562