 |
[MIS세미나]인공신경망 코드 예 |
## (1) nnet 버전
library(nnet)
m<-nnet(Species~., data=iris, size=3)
predict(m, newdata=iris)
## (2) neuralnet 버전
install.packages("neuralnet")
# creating training data set
TKS=c(20,10,30,20,80,30)
CSS=c(90,20,40,50,50,80)
Placed=c(1,0,0,0,1,1)
## 위의 세 벡터를 한 매트릭스로 합치기
df=data.frame(TKS,CSS,Placed)
# load library
require(neuralnet)
nn=neuralnet(Placed~TKS+CSS,data=df, hidden=3,act.fct = "logistic", linear.output = FALSE)
plot(nn)
# creating test set
TKS=c(30,40,85)
CSS=c(85,50,40)
test=data.frame(TKS,CSS)
# 판병하기
Predict=compute(nn,test)
Predict$net.result
## 판별을 binary로 하기
prob <- Predict$net.result
pred <- ifelse(prob>0.5, 1, 0)
pred
## iris데이터셋 가지고 한번 더 연습하기
nn=neuralnet(Species~.,data=iris, hidden=3,act.fct = "logistic", linear.output = FALSE)
nn
plot(nn)
## 알고리즘을 바꿀 수도 있음
nn=neuralnet(Placed~TKS+CSS,data=df, hidden=3,act.fct = "logistic", linear.output = FALSE, algorithm = "backprop", learningrate=0.2)
## 'backprop' - backpropagation,
## 'rprop+' and 'rprop-' - resilient backpropagation with and without weight backtracking
## 'sag' and 'slr' - modified globally convergent algorithm (grprop).
|
|
