Instantly share code, notes, and snippets.

Embed
What would you like to do?
MNIST machine learning example in R.
library(caret)
library(doParallel)
# Enable parallel processing.
cl <- makeCluster(detectCores())
registerDoParallel(cl)
# Load the MNIST digit recognition dataset into R
# http://yann.lecun.com/exdb/mnist/
# assume you have all 4 files and gunzip'd them
# creates train$n, train$x, train$y and test$n, test$x, test$y
# e.g. train$x is a 60000 x 784 matrix, each row is one digit (28x28)
# call: show_digit(train$x[5,]) to see a digit.
# brendan o'connor - gist.github.com/39760 - anyall.org
load_mnist <- function() {
load_image_file <- function(filename) {
ret = list()
f = file(filename,'rb')
readBin(f,'integer',n=1,size=4,endian='big')
ret$n = readBin(f,'integer',n=1,size=4,endian='big')
nrow = readBin(f,'integer',n=1,size=4,endian='big')
ncol = readBin(f,'integer',n=1,size=4,endian='big')
x = readBin(f,'integer',n=ret$n*nrow*ncol,size=1,signed=F)
ret$x = matrix(x, ncol=nrow*ncol, byrow=T)
close(f)
ret
}
load_label_file <- function(filename) {
f = file(filename,'rb')
readBin(f,'integer',n=1,size=4,endian='big')
n = readBin(f,'integer',n=1,size=4,endian='big')
y = readBin(f,'integer',n=n,size=1,signed=F)
close(f)
y
}
train <<- load_image_file('train-images-idx3-ubyte')
test <<- load_image_file('t10k-images-idx3-ubyte')
train$y <<- load_label_file('train-labels-idx1-ubyte')
test$y <<- load_label_file('t10k-labels-idx1-ubyte')
}
show_digit <- function(arr784, col=gray(12:1/12), ...) {
image(matrix(arr784, nrow=28)[,28:1], col=col, ...)
}
train <- data.frame()
test <- data.frame()
# Load data.
load_mnist()
# Normalize: X = (X - min) / (max - min) => X = (X - 0) / (255 - 0) => X = X / 255.
train$x <- train$x / 255
# Setup training data with digit and pixel values with 60/40 split for train/cv.
inTrain = data.frame(y=train$y, train$x)
inTrain$y <- as.factor(inTrain$y)
trainIndex = createDataPartition(inTrain$y, p = 0.60,list=FALSE)
training = inTrain[trainIndex,]
cv = inTrain[-trainIndex,]
# SVM. 95/94.
fit <- train(y ~ ., data = head(training, 1000), method = 'svmRadial', tuneGrid = data.frame(sigma=0.0107249, C=1))
results <- predict(fit, newdata = head(cv, 1000))
confusionMatrix(results, head(cv$y, 1000))
# Draw the digit.
show_digit(as.matrix(training[5,2:785]))
# Predict the digit.
predict(fit, newdata = training[5,])
# Check the actual answer for the digit.
training[5,1]
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment