For a certain combination of parameters in the deeplearning function of h2o, I get different results each time I run it.
args <- list(list(hidden = c(200,200,200),
loss = "CrossEntropy",
hidden_dropout_ratio = c(0.1, 0.1,0.1),
activation = "RectifierWithDropout",
epochs = EPOCHS))
run <- function(extra_params) {
model <- do.call(h2o.deeplearning,
modifyList(list(x = columns, y = c("Response"),
validation_frame = validation, distribution = "multinomial",
l1 = 1e-5,balance_classes = TRUE,
training_frame = training), extra_params))
}
model <- lapply(args, run)
What would I need to do in order to get consistent results for the model each time I run this?