Construct a reference torch model

Builds compact EEGNet, CNN1D, causal TCN, or unidirectional/bidirectional LSTM modules. Classification models return logits; regression models return raw values. Input is always batch by channel by time.

Usage

peModel(
  architecture = c("eegnet", "cnn1d", "tcn", "lstm"),
  n_channels,
  n_time,
  n_outputs,
  task = c("classification", "regression"),
  sampling_rate = NULL,
  dropout = 0.5,
  hidden = 32L,
  seed = 1L,
  ...
)

Arguments

architecture

Exact architecture name.

n_channels, n_time, n_outputs

Positive whole dimensions.

task

Exact task name.

sampling_rate

Optional positive sampling rate.

dropout

Dropout probability in [0, 1).

hidden

Positive hidden width.

seed

CPU initialization seed.

...

Architecture-specific named settings.

Value

A physio_torch_module.

Examples

# Requires the optional torch backend.
if (requireNamespace("torch", quietly = TRUE)) {
  model <- peModel(
    architecture = "cnn1d",
    n_channels = 2, n_time = 50, n_outputs = 2,
    task = "classification"
  )
}
#> Error: the R package `torch` is installed but LibTorch is unavailable; install it explicitly with `torch::install_torch()`