kindling-basemodels: Base models for Neural Network Training in kindling

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

Base models for Neural Network Training in kindling

Description

Base models for Neural Network Training in kindling

Usage

ffnn(
  formula = NULL,
  data = NULL,
  hidden_neurons,
  activations = NULL,
  output_activation = NULL,
  bias = TRUE,
  epochs = 100,
  batch_size = 32,
  penalty = 0,
  mixture = 0,
  learn_rate = 0.001,
  optimizer = "adam",
  optimizer_args = list(),
  loss = "mse",
  validation_split = 0,
  device = NULL,
  verbose = FALSE,
  cache_weights = FALSE,
  ...,
  x = NULL,
  y = NULL
)

rnn(
  formula = NULL,
  data = NULL,
  hidden_neurons,
  rnn_type = "lstm",
  activations = NULL,
  output_activation = NULL,
  bias = TRUE,
  bidirectional = TRUE,
  dropout = 0,
  epochs = 100,
  batch_size = 32,
  penalty = 0,
  mixture = 0,
  learn_rate = 0.001,
  optimizer = "adam",
  optimizer_args = list(),
  loss = "mse",
  validation_split = 0,
  device = NULL,
  verbose = FALSE,
  cache_weights = FALSE,
  ...,
  x = NULL,
  y = NULL
)

Arguments

formula	Formula. Model formula (e.g., y ~ x1 + x2).
data	Data frame. Training data.
hidden_neurons	Integer vector. Number of neurons in each hidden layer.
activations	Activation function specifications. See act_funs().
output_activation	Optional. Activation for output layer.
bias	Logical. Use bias weights. Default TRUE.
epochs	Integer. Number of training epochs. Default 100.
batch_size	Integer. Batch size for training. Default 32.
penalty	Numeric. Regularization penalty (lambda). Default 0 (no regularization).
mixture	Numeric. Elastic net mixing parameter (0-1). Default 0.
learn_rate	Numeric. Learning rate for optimizer. Default 0.001.
optimizer	Character. Optimizer type ("adam", "sgd", "rmsprop"). Default "adam".
optimizer_args	Named list. Additional arguments passed to the optimizer. Default list().
loss	Character. Loss function ("mse", "mae", "cross_entropy", "bce"). Default "mse".
validation_split	Numeric. Proportion of data for validation (0-1). Default 0.
device	Character. Device to use ("cpu", "cuda", "mps"). Default NULL (auto-detect).
verbose	Logical. Print training progress. Default FALSE.
cache_weights	Logical. Cache weight matrices for faster variable importance. Default FALSE.
...	Additional arguments. Can be used to pass x and y for direct interface.
x	When not using formula: predictor data (data.frame or matrix).
y	When not using formula: outcome data (vector, factor, or matrix).
rnn_type	Character. Type of RNN ("rnn", "lstm", "gru"). Default "lstm".
bidirectional	Logical. Use bidirectional RNN. Default TRUE.
dropout	Numeric. Dropout rate between layers. Default 0.

Value

An object of class "ffnn_fit" containing the trained model and metadata.

FFNN

Train a feed-forward neural network using the torch package.

RNN

Train a recurrent neural network using the torch package.

Examples

if (torch::torch_is_installed()) {
    # Formula interface (original)
    model_reg = ffnn(
        Sepal.Length ~ .,
        data = iris[, 1:4],
        hidden_neurons = c(64, 32),
        activations = "relu",
        epochs = 50
    )

    # XY interface (new)
    model_xy = ffnn(
        hidden_neurons = c(64, 32),
        activations = "relu",
        epochs = 50,
        x = iris[, 2:4],
        y = iris$Sepal.Length
    )
}



if (torch::torch_is_installed()) {
    # Formula interface (original)
    model_rnn = rnn(
        Sepal.Length ~ .,
        data = iris[, 1:4],
        hidden_neurons = c(64, 32),
        rnn_type = "lstm",
        activations = "relu",
        epochs = 50
    )

    # XY interface (new)
    model_xy = rnn(
        hidden_neurons = c(64, 32),
        rnn_type = "gru",
        epochs = 50,
        x = iris[, 2:4],
        y = iris$Sepal.Length
    )
}

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