+33
-24
lines changedFilter options
+33
-24
lines changed Original file line number Diff line number Diff line change
@@ -309,7 +309,7 @@ set_model_arg(
309 309
model = "boost_tree",
310 310
eng = "spark",
311 311
parsnip = "min_info_gain",
312 -
original = "gamma",
312 +
original = "loss_reduction",
313 313
func = list(pkg = "dials", fun = "loss_reduction"),
314 314
has_submodel = FALSE
315 315
)
@@ -342,6 +342,15 @@ set_model_engine("linear_reg", "regression", "keras")
342 342
set_dependency("linear_reg", "keras", "keras")
343 343
set_dependency("linear_reg", "keras", "magrittr")
344 344 345 +
set_model_arg(
346 +
model = "linear_reg",
347 +
eng = "keras",
348 +
parsnip = "penalty",
349 +
original = "penalty",
350 +
func = list(pkg = "dials", fun = "penalty"),
351 +
has_submodel = FALSE
352 +
)
353 + 345 354
set_fit(
346 355
model = "linear_reg",
347 356
eng = "keras",
@@ -288,9 +288,9 @@ set_dependency("logistic_reg", "keras", "magrittr")
288 288
set_model_arg(
289 289
model = "logistic_reg",
290 290
eng = "keras",
291 -
parsnip = "decay",
292 -
original = "decay",
293 -
func = list(pkg = "dials", fun = "weight_decay"),
291 +
parsnip = "penalty",
292 +
original = "penalty",
293 +
func = list(pkg = "dials", fun = "penalty"),
294 294
has_submodel = FALSE
295 295
)
296 296
@@ -265,12 +265,12 @@ class2ind <- function (x, drop2nd = FALSE) {
265 265
#' @param x A data frame or matrix of predictors
266 266
#' @param y A vector (factor or numeric) or matrix (numeric) of outcome data.
267 267
#' @param hidden_units An integer for the number of hidden units.
268 -
#' @param decay A non-negative real number for the amount of weight decay. Either
268 +
#' @param penalty A non-negative real number for the amount of weight decay. Either
269 269
#' this parameter _or_ `dropout` can specified.
270 270
#' @param dropout The proportion of parameters to set to zero. Either
271 -
#' this parameter _or_ `decay` can specified.
271 +
#' this parameter _or_ `penalty` can specified.
272 272
#' @param epochs An integer for the number of passes through the data.
273 -
#' @param act A character string for the type of activation function between layers.
273 +
#' @param activation A character string for the type of activation function between layers.
274 274
#' @param seeds A vector of three positive integers to control randomness of the
275 275
#' calculations.
276 276
#' @param ... Currently ignored.
@@ -279,11 +279,11 @@ class2ind <- function (x, drop2nd = FALSE) {
279 279
#' @export
280 280
keras_mlp <-
281 281
function(x, y,
282 -
hidden_units = 5, decay = 0, dropout = 0, epochs = 20, act = "softmax",
282 +
hidden_units = 5, penalty = 0, dropout = 0, epochs = 20, activation = "softmax",
283 283
seeds = sample.int(10^5, size = 3),
284 284
...) {
285 285 286 -
if (decay > 0 & dropout > 0) {
286 +
if (penalty > 0 & dropout > 0) {
287 287
stop("Please use either dropoput or weight decay.", call. = FALSE)
288 288
}
289 289
if (!is.matrix(x)) {
@@ -307,20 +307,20 @@ keras_mlp <-
307 307 308 308
model <- keras::keras_model_sequential()
309 309 310 -
if (decay > 0) {
310 +
if (penalty > 0) {
311 311
model %>%
312 312
keras::layer_dense(
313 313
units = hidden_units,
314 -
activation = act,
314 +
activation = activation,
315 315
input_shape = ncol(x),
316 -
kernel_regularizer = keras::regularizer_l2(decay),
316 +
kernel_regularizer = keras::regularizer_l2(penalty),
317 317
kernel_initializer = keras::initializer_glorot_uniform(seed = seeds[1])
318 318
)
319 319
} else {
320 320
model %>%
321 321
keras::layer_dense(
322 322
units = hidden_units,
323 -
activation = act,
323 +
activation = activation,
324 324
input_shape = ncol(x),
325 325
kernel_initializer = keras::initializer_glorot_uniform(seed = seeds[1])
326 326
)
@@ -330,7 +330,7 @@ keras_mlp <-
330 330
model %>%
331 331
keras::layer_dense(
332 332
units = hidden_units,
333 -
activation = act,
333 +
activation = activation,
334 334
input_shape = ncol(x),
335 335
kernel_initializer = keras::initializer_glorot_uniform(seed = seeds[1])
336 336
) %>%
@@ -24,7 +24,7 @@ set_model_arg(
24 24
eng = "keras",
25 25
parsnip = "penalty",
26 26
original = "penalty",
27 -
func = list(pkg = "dials", fun = "weight_decay"),
27 +
func = list(pkg = "dials", fun = "penalty"),
28 28
has_submodel = FALSE
29 29
)
30 30
set_model_arg(
@@ -188,7 +188,7 @@ set_model_arg(
188 188
eng = "nnet",
189 189
parsnip = "penalty",
190 190
original = "decay",
191 -
func = list(pkg = "dials", fun = "weight_decay"),
191 +
func = list(pkg = "dials", fun = "penalty"),
192 192
has_submodel = FALSE
193 193
)
194 194
set_model_arg(
@@ -172,9 +172,9 @@ set_dependency("multinom_reg", "keras", "magrittr")
172 172
set_model_arg(
173 173
model = "multinom_reg",
174 174
eng = "keras",
175 -
parsnip = "decay",
176 -
original = "decay",
177 -
func = list(pkg = "dials", fun = "weight_decay"),
175 +
parsnip = "penalty",
176 +
original = "penalty",
177 +
func = list(pkg = "dials", fun = "penalty"),
178 178
has_submodel = FALSE
179 179
)
180 180 You can’t perform that action at this time.
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