ggml_opt_fit_multi: Fit a multi-output model

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Fit a multi-output model

Description

Trains a model with several output heads. Each head has its own labels, loss type and weight; the optimized total is sum(loss_weights[i] * loss_i).

Usage

ggml_opt_fit_multi(
  sched,
  ctx_compute,
  inputs,
  outputs,
  dataset,
  loss_types,
  loss_weights = rep(1, length(outputs)),
  labels_offs = NULL,
  optimizer = ggml_opt_optimizer_type_adamw(),
  nepoch = 1,
  nbatch_logical = 32,
  val_split = 0,
  silent = FALSE
)

Arguments

sched	Backend scheduler
ctx_compute	Compute context holding the model graph
inputs	Input tensor
outputs	List of output tensors, one per head
dataset	Dataset with data and concatenated labels
loss_types	Integer vector of loss type constants, one per head
loss_weights	Numeric vector of head weights (default: all 1)
labels_offs	Numeric vector of label offsets, 0-based, one per head
optimizer	Optimizer type constant
nepoch	Number of epochs
nbatch_logical	Logical batch size
val_split	Fraction of the data used for validation
silent	Suppress progress output

Details

All heads share the dataset's datapoints. The dataset's labels hold every head's labels concatenated along the first dimension: head i occupies labels_offs[i] + seq_len(width_i) of each label row.

Value

Named list with train_loss, train_accuracy, val_loss, val_accuracy, head_loss (a nepoch x nhead matrix of unweighted per-head losses) and head_accuracy (same shape; NA for heads whose loss is not cross-entropy)

See Also

ggml_fit_opt_multi, which this wraps and which adds callbacks and shuffling control

Other optimization: ggml_fit_opt(), ggml_fit_opt_gen(), ggml_fit_opt_multi(), ggml_opt_alloc(), ggml_opt_context_optimizer_type(), ggml_opt_dataset_data(), ggml_opt_dataset_free(), ggml_opt_dataset_get_batch(), ggml_opt_dataset_get_batch_head(), ggml_opt_dataset_init(), ggml_opt_dataset_labels(), ggml_opt_dataset_ndata(), ggml_opt_dataset_shuffle(), ggml_opt_dataset_weights(), ggml_opt_default_params(), ggml_opt_epoch(), 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_init_for_fit_multi(), ggml_opt_inputs(), ggml_opt_labels(), ggml_opt_loss(), ggml_opt_loss_i(), ggml_opt_loss_type_binary_cross_entropy(), ggml_opt_loss_type_cross_entropy(), ggml_opt_loss_type_huber(), ggml_opt_loss_type_mae(), ggml_opt_loss_type_mean(), ggml_opt_loss_type_mse(), ggml_opt_loss_type_sum(), ggml_opt_loss_type_weighted_mse(), ggml_opt_n_loss(), ggml_opt_ncorrect(), ggml_opt_optimizer_name(), ggml_opt_optimizer_type_adamw(), ggml_opt_optimizer_type_sgd(), ggml_opt_outputs(), ggml_opt_outputs_i(), 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_loss_i(), ggml_opt_result_n_loss(), ggml_opt_result_ndata(), ggml_opt_result_pred(), ggml_opt_result_reset(), ggml_opt_set_lr(), ggml_opt_static_graphs(), ggml_snapshot_free(), ggml_trainer()

ggmlR documentation built on Oct. 23, 2026, 1:07 a.m.