luz_callback_metrics: Metrics callback

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

Metrics callback

Description

Tracks metrics passed to setup() during training and validation.

Usage

luz_callback_metrics()

Details

This callback takes care of 2 ctx attributes:

• ctx$metrics: stores the current metrics objects that are initialized once for epoch, and are further update()d and compute()d every batch. You will rarely need to work with these metrics.

• ctx$records$metrics: Stores metrics per training/validation and epoch. The structure is very similar to ctx$losses.

Value

A luz_callback

Note

In general you won't need to explicitly use the metrics callback as it's used by default in fit.luz_module_generator().

See Also

Other luz_callbacks: luz_callback_auto_resume(), luz_callback_csv_logger(), luz_callback_early_stopping(), luz_callback_interrupt(), luz_callback_keep_best_model(), luz_callback_lr_scheduler(), luz_callback_mixed_precision(), luz_callback_mixup(), luz_callback_model_checkpoint(), luz_callback_profile(), luz_callback_progress(), luz_callback_resume_from_checkpoint(), luz_callback_train_valid(), luz_callback()

mlverse/torchlight documentation built on Sept. 19, 2024, 11:22 p.m.