Script in h2o in R to get you into top 30 percentile for the Digit Recognizer competition

@shuvayan wrote:

Hello,

I have been trying to break the 96% accuracy barrier in the Digit Recognizer problem for a long time but nothing seemed to work until I finally laid my hands on the deep learning module in h2o library in R.It helped me break into the 97% accuracy slab.The code is very small and sweet(compared to the other things I had tried before).I am sharing it here on AV in case anyone wants to get a headstart.

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library(h2o)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE,min_mem_size = "3g")
## Import MNIST CSV as H2O:
mnistPath = '/home/shuvayan/Downloads/Kaggle/DGr/train.csv'
mnist.hex = h2o.importFile(path = mnistPath, destination_frame = "mnist.hex")
train <- as.data.frame(mnist.hex)
train$label <- as.factor(train$label)
train_h2o <- as.h2o(train)

#Training a deep learning model:---------------------------------------------------------#
model <- h2o.deeplearning(x = 2:785,
                   y = 1,
                   training_frame = train_h2o,
                   activation = "RectifierWithDropout", 
                   input_dropout_ratio = 0.2,
                   hidden_dropout_ratios = c(0.5,0.5),
                   balance_classes = TRUE, 
                   hidden = c(800,800),
                   epochs = 500)

#Predict on test data:
test_h2o <- h2o.importFile(path = '/home/shuvayan/Downloads/Kaggle/DGr/test.csv', destination_frame = "test_h2o")
yhat <- h2o.predict(model, test_h2o)
ImageId <- as.numeric(seq(1,28000))
names(ImageId)[1] <- "ImageId"
predictions <- cbind(as.data.frame(ImageId),as.data.frame(yhat[,1]))
names(predictions)[2] <- "Label"
write.table(as.matrix(predictions), file="DNN_pred.csv", row.names=FALSE, sep=",")

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