Nothing
test_that('updating', {
expect_snapshot(
mlp(mode = "classification", hidden_units = 2) %>%
set_engine("nnet", Hess = FALSE) %>%
update(hidden_units = tune(), Hess = tune())
)
})
test_that('bad input', {
expect_error(mlp(mode = "time series"))
expect_error(translate(mlp(mode = "classification") %>% set_engine("wat?")))
expect_warning(translate(mlp(mode = "regression") %>% set_engine("nnet", formula = y ~ x)))
expect_error(translate(mlp(mode = "classification", x = x, y = y) %>% set_engine("keras")))
expect_error(translate(mlp(mode = "regression", formula = y ~ x) %>% set_engine()))
})
test_that("nnet_softmax", {
obj <- mlp(mode = 'classification')
obj$lvls <- c("a", "b")
res <- nnet_softmax(matrix(c(.8, .2)), obj)
expect_equal(names(res), obj$lvls)
expect_equal(res$b, 1 - res$a)
})
test_that("more activations for brulee", {
skip_if_not_installed("brulee", minimum_version = "0.3.0")
skip_on_cran()
data(ames, package = "modeldata")
ames$Sale_Price <- log10(ames$Sale_Price)
set.seed(122)
in_train <- sample(1:nrow(ames), 2000)
ames_train <- ames[ in_train,]
ames_test <- ames[-in_train,]
set.seed(1)
fit <-
try(
mlp(penalty = 0.10, activation = "softplus") %>%
set_mode("regression") %>%
set_engine("brulee") %>%
fit_xy(x = as.matrix(ames_train[, c("Longitude", "Latitude")]),
y = ames_train$Sale_Price),
silent = TRUE)
expect_true(inherits(fit$fit, "brulee_mlp"))
})
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