mlr_callback_set.progress: Progress Callback

In mlr3torch: Deep Learning with 'mlr3'

Progress Callback

Description

Prints a progress bar and the metrics for training and validation.

Super class

mlr3torch::CallbackSet -> CallbackSetProgress

Methods

Public methods

• CallbackSetProgress$new()

• CallbackSetProgress$on_epoch_begin()

• CallbackSetProgress$on_batch_end()

• CallbackSetProgress$on_before_valid()

• CallbackSetProgress$on_batch_valid_end()

• CallbackSetProgress$on_epoch_end()

• CallbackSetProgress$on_end()

• CallbackSetProgress$clone()

Inherited methods

• mlr3torch::CallbackSet$load_state_dict()
• mlr3torch::CallbackSet$print()
• mlr3torch::CallbackSet$state_dict()

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Method new()

Creates a new instance of this R6 class.

Usage

CallbackSetProgress$new(digits = 2)

Arguments

digits

integer(1)
The number of digits to print for the measures.

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Method on_epoch_begin()

Initializes the progress bar for training.

Usage

CallbackSetProgress$on_epoch_begin()

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Method on_batch_end()

Increments the training progress bar.

Usage

CallbackSetProgress$on_batch_end()

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Method on_before_valid()

Creates the progress bar for validation.

Usage

CallbackSetProgress$on_before_valid()

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Method on_batch_valid_end()

Increments the validation progress bar.

Usage

CallbackSetProgress$on_batch_valid_end()

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Method on_epoch_end()

Prints a summary of the training and validation process.

Usage

CallbackSetProgress$on_epoch_end()

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Method on_end()

Prints the time at the end of training.

Usage

CallbackSetProgress$on_end()

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Method clone()

The objects of this class are cloneable with this method.

Usage

CallbackSetProgress$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other Callback: TorchCallback, as_torch_callback(), as_torch_callbacks(), callback_set(), mlr3torch_callbacks, mlr_callback_set, mlr_callback_set.checkpoint, mlr_callback_set.tb, mlr_callback_set.unfreeze, mlr_context_torch, t_clbk(), torch_callback()

Examples

task = tsk("iris")

learner = lrn("classif.mlp", epochs = 5, batch_size = 1,
  callbacks = t_clbk("progress"), validate = 0.3)
learner$param_set$set_values(
  measures_train = msrs(c("classif.acc", "classif.ce")),
  measures_valid = msr("classif.ce")
)

learner$train(task)

mlr3torch documentation built on Aug. 26, 2025, 5:09 p.m.