Creates a new optimizer context for training.
ggml_opt_init(
sched,
loss_type,
optimizer = ggml_opt_optimizer_type_adamw(),
opt_period = 1L,
ctx_compute = NULL,
inputs = NULL,
outputs = NULL
)External pointer to optimizer context
Backend scheduler
Loss type (use ggml_opt_loss_type_* functions)
Optimizer type (use ggml_opt_optimizer_type_* functions)
Gradient accumulation steps before optimizer step
Compute context for static graph mode (or NULL)
Input tensor for static graph mode (or NULL)
Output tensor for static graph mode (or NULL)
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_epoch(),
ggml_opt_eval(),
ggml_opt_fit(),
ggml_opt_free(),
ggml_opt_get_lr(),
ggml_opt_grad_acc(),
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()