Random search with h2o
Next, you will use random search. The h2o
library and seeds_train_data
have already been loaded for you and the following code has been run:
h2o.init()
seeds_train_data_hf <- as.h2o(seeds_train_data)
y <- "seed_type"
x <- setdiff(colnames(seeds_train_data_hf), y)
seeds_train_data_hf[, y] <- as.factor(seeds_train_data_hf[, y])
sframe <- h2o.splitFrame(seeds_train_data_hf, seed = 42)
train <- sframe[[1]]
valid <- sframe[[2]]
dl_params <- list(hidden = list(c(50, 50), c(100, 100)),
epochs = c(5, 10, 15),
rate = c(0.001, 0.005, 0.01))
This exercise is part of the course
Hyperparameter Tuning in R
Exercise instructions
- Define a search criteria object that defines random search with a maximum runtime of 10 seconds.
- Add this search criteria object at the appropriate place in the
h2o.grid
function to train the random models.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Define search criteria
search_criteria <- list(strategy = ___,
___ = 10, # this is way too short & only used to keep runtime short!
seed = 42)
# Train with random search
dl_grid <- h2o.grid("deeplearning",
grid_id = "dl_grid",
x = x,
y = y,
training_frame = train,
validation_frame = valid,
seed = 42,
hyper_params = dl_params,
___ = ___)