lr_finder: Learning Rate Finder

In mlverse/luz: Higher Level 'API' for 'torch'

Learning Rate Finder

Description

Learning Rate Finder

Usage

lr_finder(
  object,
  data,
  steps = 100,
  start_lr = 1e-07,
  end_lr = 0.1,
  log_spaced_intervals = TRUE,
  ...,
  verbose = NULL
)

Arguments

object	An nn_module that has been setup().
data	(dataloader) A dataloader created with torch::dataloader() used for learning rate finding.
steps	(integer) The number of steps to iterate over in the learning rate finder. Default: 100.
start_lr	(float) The smallest learning rate. Default: 1e-7.
end_lr	(float) The highest learning rate. Default: 1e-1.
log_spaced_intervals	(logical) Whether to divide the range between start_lr and end_lr into log-spaced intervals (alternative: uniform intervals). Default: TRUE
...	Other arguments passed to fit.
verbose	Wether to show a progress bar during the process.

Value

A dataframe with two columns: learning rate and loss

Examples

if (torch::torch_is_installed()) {
library(torch)
ds <- torch::tensor_dataset(x = torch_randn(100, 10), y = torch_randn(100, 1))
dl <- torch::dataloader(ds, batch_size = 32)
model <- torch::nn_linear
model <- model %>% setup(
  loss = torch::nn_mse_loss(),
  optimizer = torch::optim_adam
) %>%
  set_hparams(in_features = 10, out_features = 1)
records <- lr_finder(model, dl, verbose = FALSE)
plot(records)
}

mlverse/luz documentation built on Sept. 19, 2024, 11:20 p.m.