rnn_kindling: Recurrent Neural Network via kindling

In kindling: Higher-Level Interface of 'torch' Package to Auto-Train Neural Networks

Recurrent Neural Network via kindling

Description

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,
  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,
  validation_split = NULL,
  rnn_type = NULL,
  optimizer_args = NULL,
  loss = NULL,
  early_stopping = NULL,
  device = NULL,
  verbose = NULL,
  cache_weights = 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.
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.
validation_split	A number between 0 and 1 for the proportion of data used for validation. Can be tuned.
rnn_type	A character string for the type of RNN cell ("rnn", "lstm", "gru"). Cannot be tuned — pass via set_engine().
optimizer_args	A named list of additional arguments passed to the optimizer. Cannot be tuned — pass via set_engine().
loss	A character string for the loss function ("mse", "mae", "cross_entropy", "bce"). Cannot be tuned — pass via set_engine().
early_stopping	An early_stop() object or NULL. Cannot be tuned — pass via set_engine().
device	A character string for the device ("cpu", "cuda", "mps"). Cannot be tuned — pass via set_engine().
verbose	Logical for whether to print training progress. Cannot be tuned — pass via set_engine().
cache_weights	Logical. If TRUE, stores trained weight matrices in the returned object. Cannot be tuned — pass via set_engine().

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

Value

A model specification object with class rnn_kindling.

Examples

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")
}

kindling documentation built on March 3, 2026, 9:07 a.m.