mlr_callback_set.history: History Callback

In mlr3torch: Deep Learning with 'mlr3'

History Callback

Description

Saves the training and validation history during training. The history is saved as a data.table where the validation measures are prefixed with "valid." and the training measures are prefixed with "train.".

Super class

mlr3torch::CallbackSet -> CallbackSetHistory

Methods

Public methods

• CallbackSetHistory$on_begin()

• CallbackSetHistory$state_dict()

• CallbackSetHistory$load_state_dict()

• CallbackSetHistory$on_before_valid()

• CallbackSetHistory$on_epoch_end()

• CallbackSetHistory$clone()

Inherited methods

• mlr3torch::CallbackSet$print()

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Method on_begin()

Initializes lists where the train and validation metrics are stored.

Usage

CallbackSetHistory$on_begin()

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Method state_dict()

Converts the lists to data.tables.

Usage

CallbackSetHistory$state_dict()

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Method load_state_dict()

Sets the field ⁠$train⁠ and ⁠$valid⁠ to those contained in the state dict.

Usage

CallbackSetHistory$load_state_dict(state_dict)

Arguments

state_dict

(callback_state_history)
The state dict as retrieved via ⁠$state_dict()⁠.

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Method on_before_valid()

Add the latest training scores to the history.

Usage

CallbackSetHistory$on_before_valid()

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Method on_epoch_end()

Add the latest validation scores to the history.

Usage

CallbackSetHistory$on_epoch_end()

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Method clone()

The objects of this class are cloneable with this method.

Usage

CallbackSetHistory$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

cb = t_clbk("history")
task = tsk("iris")

learner = lrn("classif.mlp", epochs = 3, batch_size = 1,
  callbacks = t_clbk("history"), validate = 0.3)
learner$param_set$set_values(
  measures_train = msrs(c("classif.acc", "classif.ce")),
  measures_valid = msr("classif.ce")
)
learner$train(task)

print(learner$model$callbacks$history)

mlr3torch documentation built on April 4, 2025, 3:03 a.m.