mlr_callback_set.history {mlr3torch} | R Documentation |
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
Inherited methods
Method on_begin()
Initializes lists where the train and validation metrics are stored.
Usage
CallbackSetHistory$on_begin()
Method state_dict()
Converts the lists to data.tables.
Usage
CallbackSetHistory$state_dict()
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$state_dict()
.
Method on_before_valid()
Add the latest training scores to the history.
Usage
CallbackSetHistory$on_before_valid()
Method on_epoch_end()
Add the latest validation scores to the history.
Usage
CallbackSetHistory$on_epoch_end()
Method clone()
The objects of this class are cloneable with this method.
Usage
CallbackSetHistory$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)
[Package mlr3torch version 0.2.1 Index]