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

# \donttest{
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
    )
}
# }

# \donttest{
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
    )
}
# }