# IRIS example using RPROP fine-tuning and autosaving
example.iris <- function(...)
{
data(iris)
darch <- darch(Species ~ ., iris,
preProc.params = list("method" = c("scale", "center")),
normalizeWeights = T,
normalizeWeightsBound = 1,
layers = 20, # one hidden layer with 20 neurons
darch.batchSize = 30,
darch.fineTuneFunction = "rpropagation",
darch.unitFunction = c("tanhUnit", "softmaxUnit"),
darch.stopValidClassErr = 0,
darch.stopValidErr = .15,
bootstrap = T,
bootstrap.unique = F,
rprop.incFact = 1.3,
rprop.decFact = .7,
rprop.initDelta = .1,
rprop.maxDelta = 5,
rprop.method = "iRprop-",
rprop.minDelta = 1e-5,
autosave = T,
autosave.dir = "darch.autosave",
autosave.epochs = 10,
autosave.trim = T,
...
)
# The predict function can be used to get the network output for a new set of
# data, it will even convert the output back to the original class labels
predictions <- predict(darch, newdata = iris, type = "class")
# And these labels can then easily be compared to the correct ones
numIncorrect <- sum(predictions != iris[,5])
cat(paste0("Incorrect classifications on all examples: ", numIncorrect, " (",
round(numIncorrect/nrow(iris)*100, 2), "%)\n"))
darch
}
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