mlr_callback_set.progress | R Documentation |
Prints a progress bar and the metrics for training and validation.
mlr3torch::CallbackSet
-> CallbackSetProgress
on_epoch_begin()
Initializes the progress bar for training.
CallbackSetProgress$on_epoch_begin()
on_batch_end()
Increments the training progress bar.
CallbackSetProgress$on_batch_end()
on_before_valid()
Creates the progress bar for validation.
CallbackSetProgress$on_before_valid()
on_batch_valid_end()
Increments the validation progress bar.
CallbackSetProgress$on_batch_valid_end()
on_epoch_end()
Prints a summary of the training and validation process.
CallbackSetProgress$on_epoch_end()
on_end()
Prints the time at the end of training.
CallbackSetProgress$on_end()
clone()
The objects of this class are cloneable with this method.
CallbackSetProgress$clone(deep = FALSE)
deep
Whether to make a deep clone.
Other Callback:
TorchCallback
,
as_torch_callback()
,
as_torch_callbacks()
,
callback_set()
,
mlr3torch_callbacks
,
mlr_callback_set
,
mlr_callback_set.checkpoint
,
mlr_callback_set.tb
,
mlr_callback_set.unfreeze
,
mlr_context_torch
,
t_clbk()
,
torch_callback()
task = tsk("iris")
learner = lrn("classif.mlp", epochs = 5, batch_size = 1,
callbacks = t_clbk("progress"), validate = 0.3)
learner$param_set$set_values(
measures_train = msrs(c("classif.acc", "classif.ce")),
measures_valid = msr("classif.ce")
)
learner$train(task)
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