Tune a deep learning model

Tune a deep learning model

Usage

tl_tune_deep(
  data,
  formula,
  is_classification = FALSE,
  hidden_layers_options = list(c(32), c(64, 32), c(128, 64, 32)),
  learning_rates = c(0.01, 0.001, 1e-04),
  batch_sizes = c(16, 32, 64),
  epochs = 30,
  validation_split = 0.2,
  ...
)

Arguments

data

A data frame containing the training data

formula

A formula specifying the model

is_classification

Logical indicating if this is a classification problem

hidden_layers_options

List of vectors defining hidden layer configurations to try

learning_rates

Learning rates to try (default: c(0.01, 0.001, 0.0001))

batch_sizes

Batch sizes to try (default: c(16, 32, 64))

epochs

Number of training epochs (default: 30)

validation_split

Proportion of data for validation (default: 0.2)

...

Additional arguments

Value

A list with elements model (the best fitted deep learning model), best_hidden_layers (optimal layer configuration), best_learning_rate, best_batch_size, and tuning_results (a data frame of all hyperparameter combinations and their validation losses).

Examples

if (FALSE) { # \dontrun{
if (requireNamespace("keras", quietly = TRUE)) {
  result <- tl_tune_deep(iris, Species ~ .,
    is_classification = TRUE,
    hidden_layers_options = list(c(10), c(10, 5)),
    learning_rates = c(0.01, 0.001), batch_sizes = c(32),
    epochs = 5)
}
} # }