ggmlR (version 0.6.1)

ggml_opt_epoch: Run one training epoch

Description

Performs training on the front portion of the dataset and evaluation on the back portion. This gives more control than ggml_opt_fit.

Usage

ggml_opt_epoch(
  opt_ctx,
  dataset,
  result_train = NULL,
  result_eval = NULL,
  idata_split,
  callback_train = TRUE,
  callback_eval = TRUE
)

Value

NULL invisibly

Arguments

opt_ctx

External pointer to optimizer context

dataset

External pointer to dataset

result_train

Result object to accumulate training stats (or NULL)

result_eval

Result object to accumulate evaluation stats (or NULL)

idata_split

Data index at which to split training and evaluation

callback_train

Callback for training: TRUE for progress bar, FALSE for none, or a function(train, ibatch, ibatch_max, t_start_us, result)

callback_eval

Callback for evaluation: TRUE for progress bar, FALSE for none, or a function(train, ibatch, ibatch_max, t_start_us, result)

See Also

Other optimization: ggml_fit_opt(), ggml_opt_alloc(), ggml_opt_context_optimizer_type(), ggml_opt_dataset_data(), ggml_opt_dataset_free(), ggml_opt_dataset_get_batch(), ggml_opt_dataset_init(), ggml_opt_dataset_labels(), ggml_opt_dataset_ndata(), ggml_opt_dataset_shuffle(), ggml_opt_default_params(), ggml_opt_eval(), ggml_opt_fit(), ggml_opt_free(), ggml_opt_get_lr(), ggml_opt_grad_acc(), ggml_opt_init(), ggml_opt_init_for_fit(), ggml_opt_inputs(), ggml_opt_labels(), ggml_opt_loss(), ggml_opt_loss_type_cross_entropy(), ggml_opt_loss_type_mean(), ggml_opt_loss_type_mse(), ggml_opt_loss_type_sum(), ggml_opt_ncorrect(), ggml_opt_optimizer_name(), ggml_opt_optimizer_type_adamw(), ggml_opt_optimizer_type_sgd(), ggml_opt_outputs(), ggml_opt_pred(), ggml_opt_prepare_alloc(), ggml_opt_reset(), ggml_opt_result_accuracy(), ggml_opt_result_free(), ggml_opt_result_init(), ggml_opt_result_loss(), ggml_opt_result_ndata(), ggml_opt_result_pred(), ggml_opt_result_reset(), ggml_opt_set_lr(), ggml_opt_static_graphs()

Examples

# Requires full optimizer setup - see ggml_opt_fit() for simpler API
if (FALSE) {
result_train <- ggml_opt_result_init()
result_eval <- ggml_opt_result_init()
ggml_opt_epoch(opt_ctx, dataset, result_train, result_eval,
               idata_split = 900, callback_train = TRUE)
ggml_opt_result_free(result_train)
ggml_opt_result_free(result_eval)
}