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presentation's Introduction

2019/6/25 교육

A) Rstudio를 통한 cpu버젼의 keras설치

R은 https://cran.r-project.org/bin/windows/base/에서 R-3.6를 다운로드하여 설치합니다. R Studio는0 https://download1.rstudio.org/RStudio-1.2.1335.exe 에서 Windows용을 다운/설치합니다. RTools는 https://cran.r-project.org/bin/windows/Rtools/Rtools34.exe 에서 3.4를 다운/설치합니다. [R Studio에서 연동되는 R 버젼확인 필요 ]

  1. Rstudio에서 다음을 입력

install.packages('devtools')

devtools::install_github("rstudio/keras")

  1. Anaconda를 설치 https://www.anaconda.com/download/ 에서 window 탭 클릭 python 3.7 version -64bit(632MB)와 32bit(546MB)에서 선택

  2. Rstudio에서 다음을 실행(cpu version)

library('keras')

install_keras()

  1. Rstudio에서 다음을 실행

mnist <- datastset_mnist()

############################### #python을 윈도우 앱에서 제거 #uninstall anaconda를 검색함 #Rstudio제거 #2. https://www.python.org/downloads/windows/ #에서 3.7.3설치 #Windows x86 executable installer

참고: https://belitino.tistory.com/257 [belitino]

##########################################

B) XOR 분류

XOR 분류(입력2개) 예제

Rstudio에서 다음을 입력

library(neuralnet)

AND <- c(rep(0,3),1)

OR <- c(0,rep(1,3))

xx <- matrix(0,4,2)

xx[,1] <- c(0,0,1,1)

xx[,2] <- c(0,1,0,1)

1.

binary.data <- data.frame(xx, AND, OR)

colnames(binary.data) <- c("Var1", "Var2")

print(net <- neuralnet(AND+OR~Var1+Var2, binary.data, hidden=0, rep=10,err.fct="ce", linear.output=FALSE))

plot(net)

2.

XOR <- c(0,1,1,0)

xor.data <- data.frame(expand.grid(c(0,1), c(0,1)), XOR)

print(net.xor <- neuralnet(XOR~Var1+Var2, xor.data, hidden=2, rep=5))

plot(net.xor, rep="best")

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