kindling (version 0.2.0)

grid_depth: Depth-Aware Grid Generation for Neural Networks

Description

grid_depth() extends standard grid generation to support multi-layer neural network architectures. It creates heterogeneous layer configurations by generating list columns for hidden_neurons and activations.

Usage

grid_depth(
  x,
  ...,
  n_hlayer = 2L,
  size = 5L,
  type = c("regular", "random", "latin_hypercube", "max_entropy", "audze_eglais"),
  original = TRUE,
  levels = 3L,
  variogram_range = 0.5,
  iter = 1000L
)
# S3 method for parameters
grid_depth(
  x,
  ...,
  n_hlayer = 2L,
  size = 5L,
  type = c("regular", "random", "latin_hypercube", "max_entropy", "audze_eglais"),
  original = TRUE,
  levels = 3L,
  variogram_range = 0.5,
  iter = 1000L
)
# S3 method for list
grid_depth(
  x,
  ...,
  n_hlayer = 2L,
  size = 5L,
  type = c("regular", "random", "latin_hypercube", "max_entropy", "audze_eglais"),
  original = TRUE,
  levels = 3L,
  variogram_range = 0.5,
  iter = 1000L
)
# S3 method for workflow
grid_depth(
  x,
  ...,
  n_hlayer = 2L,
  size = 5L,
  type = c("regular", "random", "latin_hypercube", "max_entropy", "audze_eglais"),
  original = TRUE,
  levels = 3L,
  variogram_range = 0.5,
  iter = 1000L
)
# S3 method for model_spec
grid_depth(
  x,
  ...,
  n_hlayer = 2L,
  size = 5L,
  type = c("regular", "random", "latin_hypercube", "max_entropy", "audze_eglais"),
  original = TRUE,
  levels = 3L,
  variogram_range = 0.5,
  iter = 1000L
)
# S3 method for param
grid_depth(
  x,
  ...,
  n_hlayer = 2L,
  size = 5L,
  type = c("regular", "random", "latin_hypercube", "max_entropy", "audze_eglais"),
  original = TRUE,
  levels = 3L,
  variogram_range = 0.5,
  iter = 1000L
)
# S3 method for default
grid_depth(
  x,
  ...,
  n_hlayer = 2L,
  size = 5L,
  type = c("regular", "random", "latin_hypercube", "max_entropy", "audze_eglais"),
  original = TRUE,
  levels = 3L,
  variogram_range = 0.5,
  iter = 1000L
)

Value

A tibble with list columns for hidden_neurons and activations, where each element is a vector of length n_hlayer.

Arguments

x

A parameters object, list, workflow, or model spec. Can also be a single param object if ... contains additional param objects.

...

One or more param objects (e.g., hidden_neurons(), epochs()). If x is a parameters object, ... is ignored. None of the objects can have unknown() values.

n_hlayer

Integer vector specifying number of hidden layers to generate (e.g., 2:4 for 2, 3, or 4 layers), or a param object created with n_hlayers(). Default is 2.

size

Integer. Number of parameter combinations to generate.

type

Character. Type of grid: "regular", "random", "latin_hypercube", "max_entropy", or "audze_eglais".

original

Logical. Should original parameter ranges be used?

levels

Integer. Levels per parameter for regular grids.

variogram_range

Numeric. Range for audze_eglais design.

iter

Integer. Iterations for max_entropy optimization.

Details

This function is specifically for {kindling} models. The n_hlayer parameter determines network depth and creates list columns for hidden_neurons and activations, where each element is a vector of length matching the sampled depth.

When n_hlayer is a parameter object (created with n_hlayers()), it will be treated as a tunable parameter and sampled according to its defined range.

Examples

# \donttest{
if (FALSE) {
library(dials)
library(workflows)
library(tune)

# Method 1: Fixed depth
grid = grid_depth(
    hidden_neurons(c(32L, 128L)),
    activations(c("relu", "elu")),
    epochs(c(50L, 200L)),
    n_hlayer = 2:3,
    type = "random",
    size = 20
)

# Method 2: Tunable depth using parameter object
grid = grid_depth(
    hidden_neurons(c(32L, 128L)),
    activations(c("relu", "elu")),
    epochs(c(50L, 200L)),
    n_hlayer = n_hlayers(range = c(2L, 4L)),
    type = "random",
    size = 20
)

# Method 3: From workflow
wf = workflow() |>
    add_model(mlp_kindling(hidden_neurons = tune(), activations = tune())) |>
    add_formula(y ~ .)
grid = grid_depth(wf, n_hlayer = 2:4, type = "latin_hypercube", size = 15)
}
# }