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Recurrent Neural Network via kindling

Source: R/rnn_kindling.R
rnn_kindling.Rd

rnn_kindling() defines a recurrent neural network model that can be used for classification or regression on sequential data. It integrates with the tidymodels ecosystem and uses the torch backend via kindling.

Usage

rnn_kindling(
  mode = "unknown",
  engine = "kindling",
  hidden_neurons = NULL,
  rnn_type = NULL,
  activations = NULL,
  output_activation = NULL,
  bias = NULL,
  bidirectional = NULL,
  dropout = NULL,
  epochs = NULL,
  batch_size = NULL,
  penalty = NULL,
  mixture = NULL,
  learn_rate = NULL,
  optimizer = NULL,
  optimizer_args = NULL,
  loss = NULL,
  validation_split = NULL,
  device = NULL,
  verbose = NULL
)

Arguments

mode

A single character string for the type of model. Possible values are "unknown", "regression", or "classification".

engine

A single character string specifying what computational engine to use for fitting. Currently only "kindling" is supported.

hidden_neurons

An integer vector for the number of units in each hidden layer. Can be tuned.

rnn_type

A character string for the type of RNN cell ("rnn", "lstm", "gru"). Cannot be tuned.

activations

A character vector of activation function names for each hidden layer (e.g., "relu", "tanh", "sigmoid"). Can be tuned.

output_activation

A character string for the output activation function. Can be tuned.

bias

Logical for whether to include bias terms. Can be tuned.

bidirectional

A logical indicating whether to use bidirectional RNN. Can be tuned.

dropout

A number between 0 and 1 for dropout rate between layers. Can be tuned.

epochs

An integer for the number of training iterations. Can be tuned.

batch_size

An integer for the batch size during training. Can be tuned.

penalty

A number for the regularization penalty (lambda). Default 0 (no regularization). Higher values increase regularization strength. Can be tuned.

mixture

A number between 0 and 1 for the elastic net mixing parameter. Default 0 (pure L2/Ridge regularization).

  • 0: Pure L2 regularization (Ridge)

  • 1: Pure L1 regularization (Lasso)

  • 0 < mixture < 1: Elastic net (combination of L1 and L2) Only relevant when penalty > 0. Can be tuned.

learn_rate

A number for the learning rate. Can be tuned.

optimizer

A character string for the optimizer type ("adam", "sgd", "rmsprop"). Can be tuned.

optimizer_args

A named list of additional arguments passed to the optimizer. Cannot be tuned.

loss

A character string for the loss function ("mse", "mae", "cross_entropy", "bce"). Cannot be tuned.

validation_split

A number between 0 and 1 for the proportion of data used for validation. Can be tuned.

device

A character string for the device to use ("cpu", "cuda", "mps"). If NULL, auto-detects available GPU. Cannot be tuned.

verbose

Logical for whether to print training progress. Default FALSE. Cannot be tuned.

Value

A model specification object with class rnn_kindling.

Details

This function creates a model specification for a recurrent neural network that can be used within tidymodels workflows. The model supports:

  • Multiple RNN types: basic RNN, LSTM, and GRU

  • Bidirectional processing

  • Dropout regularization

  • GPU acceleration (CUDA, MPS, or CPU)

  • Hyperparameter tuning integration

  • Both regression and classification tasks

The device parameter controls where computation occurs:

  • NULL (default): Auto-detect best available device (CUDA > MPS > CPU)

  • "cuda": Use NVIDIA GPU

  • "mps": Use Apple Silicon GPU

  • "cpu": Use CPU only

Examples

# \donttest{
if (torch::torch_is_installed()) {
    box::use(
        recipes[recipe],
        workflows[workflow, add_recipe, add_model],
        parsnip[fit]
    )

    # Model specs
    rnn_spec = rnn_kindling(
        mode = "classification",
        hidden_neurons = c(64, 32),
        rnn_type = "lstm",
        activation = c("relu", "elu"),
        epochs = 100,
        bidirectional = TRUE
    )

    wf = workflow() |>
        add_recipe(recipe(Species ~ ., data = iris)) |>
        add_model(rnn_spec)

    fit_wf = fit(wf, data = iris)
    fit_wf
} else {
    message("Torch not fully installed — skipping example")
}
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: rnn_kindling()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 0 Recipe Steps
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Warning: running command 'tput cols' had status 2
#> Warning: running command 'tput cols' had status 2
#> 
#> ========================= Long Short-Term Memory (RNN) =========================
#> 
#> 
#> -- RNN Model Summary -----------------------------------------------------------
#> 
#> 
#> Warning: running command 'tput cols' had status 2
#> -----------------------------------------------------------------------
#>   NN Model Type           :              RNN    n_predictors :      4
#>   RNN Type                :             LSTM    n_response   :      3
#>   Bidirectional           :              Yes    reg.         :   None
#>   Number of Epochs        :              100    Device       :    cpu
#>   Hidden Layer Units      :           64, 32                 :       
#>   Number of Hidden Layers :                2                 :       
#>   Pred. Type              :   classification                 :       
#> -----------------------------------------------------------------------
#> 
#> 
#> 
#> -- Activation function ---------------------------------------------------------
#> 
#> 
#> Warning: running command 'tput cols' had status 2
#> -------------------------------------------------
#>   1st Layer {64}    :                      relu
#>   2nd Layer {32}    :                       elu
#>   Output Activation :   No act function applied
#> -------------------------------------------------
#> 
# }