Depth-Aware Grid Generation for Neural Networks

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 class '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 class '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 class '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 class '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 class '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
)

# Default S3 method
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
)

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). 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.

Value

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

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.

Examples

if (FALSE) { # \dontrun{
library(dials)

# Method 1: Using parameters()
params = parameters(
    hidden_neurons(c(32L, 128L)),
    activations(c("relu", "elu", "selu")),
    epochs(c(50L, 200L))
)
grid = grid_depth(params, n_hlayer = 2:3, type = "regular", levels = 3)

# Method 2: Direct param objects
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 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)
} # }