Nothing
test_that("The function works well over a regular Neural Network.", {
skip_if_not_installed("keras")
skip_if_not_installed("tensorflow")
skip_on_cran()
nn <- keras_test_model()
# Save the parameters into a list
params <- get_parameters(nn)
expect_equal(params$p, 2)
expect_equal(params$af_string_list[[1]], "tanh")
expect_equal(params$af_string_list[[2]], "softplus")
expect_equal(params$n_neurons[[1]], 2)
expect_equal(params$n_neurons[[2]], 3)
expect_equal(params$weights[[1]][[2]], 0.8773805)
expect_equal(params$weights[[2]][[3]], 0.53210747)
expect_equal(params$p, 2)
})
test_that("The get_parameters functions returns the right list of activation
functions for a neural network with custom constraints.", {
skip_if_not_installed("keras")
skip_if_not_installed("tensorflow")
skip_on_cran()
testing_data <- testing_helper_2()
nn <- keras_test_model()
keras::compile(nn,
loss = "mse",
optimizer = keras::optimizer_adam(),
metrics = "mse")
constrained_nn <- add_constraints(nn)
fit(constrained_nn,
testing_data$train_x,
testing_data$train_y,
verbose = 0,
epochs = 3,
validation_split = 0.2
)
params <- get_parameters(constrained_nn)
expect_equal(params$af_string_list, list("tanh", "softplus", "linear"))
})
test_that("The get_parameters function works for a torch model (nn_module)
and the list of activation functions is the expected one.", {
skip_if_not_installed("luz")
skip_if_not_installed("torch")
skip_on_cran()
nn <- luz_test_model()
params <- get_parameters(nn)
expect_equal(params$af_string_list, list("softplus", "softplus", "linear"))
})
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