Skip to contents

This wraps a CallbackSet and annotates it with metadata, most importantly a ParamSet. The callback is created for the given parameter values by calling the $generate() method.

This class is usually used to configure the callback of a torch learner, e.g. when constructing a learner of in a ModelDescriptor.

For a list of available callbacks, see mlr3torch_callbacks. To conveniently retrieve a TorchCallback, use t_clbk().

Parameters

Defined by the constructor argument param_set. If no parameter set is provided during construction, the parameter set is constructed by creating a parameter for each argument of the wrapped loss function, where the parametes are then of type ParamUty.

Super class

mlr3torch::TorchDescriptor -> TorchCallback

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage

TorchCallback$new(
  callback_generator,
  param_set = NULL,
  id = NULL,
  label = NULL,
  packages = NULL,
  man = NULL
)

Arguments

callback_generator

(R6ClassGenerator)
The class generator for the callback that is being wrapped.

param_set

(ParamSet or NULL)
The parameter set. If NULL (default) it is inferred from callback_generator.

id

(character(1))
The id for of the new object.

label

(character(1))
Label for the new instance.

packages

(character())
The R packages this object depends on.

man

(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object. The referenced help package can be opened via method $help().


Method clone()

The objects of this class are cloneable with this method.

Usage

TorchCallback$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Create a new torch callback from an existing callback set
torch_callback = TorchCallback$new(CallbackSetCheckpoint)
# The parameters are inferred
torch_callback$param_set
#> <ParamSet(3)>
#>           id    class lower upper nlevels        default  value
#>       <char>   <char> <num> <num>   <num>         <list> <list>
#> 1:      path ParamUty    NA    NA     Inf <NoDefault[0]> [NULL]
#> 2:      freq ParamUty    NA    NA     Inf <NoDefault[0]> [NULL]
#> 3: freq_type ParamUty    NA    NA     Inf <NoDefault[0]> [NULL]

# Retrieve a torch callback from the dictionary
torch_callback = t_clbk("checkpoint",
  path = tempfile(), freq = 1
)
torch_callback
#> <TorchCallback:checkpoint> Checkpoint
#> * Generator: CallbackSetCheckpoint
#> * Parameters: path=/tmp/RtmpUMG6s7/file26bb6514a71c, freq=1
#> * Packages: mlr3torch,torch
torch_callback$label
#> [1] "Checkpoint"
torch_callback$id
#> [1] "checkpoint"

# open the help page of the wrapped callback set
# torch_callback$help()

# Create the callback set
callback = torch_callback$generate()
callback
#> <CallbackSetCheckpoint>
#> * Stages: on_batch_end, on_epoch_end, on_exit
# is the same as
CallbackSetCheckpoint$new(
  path = tempfile(), freq = 1
)
#> <CallbackSetCheckpoint>
#> * Stages: on_batch_end, on_epoch_end, on_exit

# Use in a learner
learner = lrn("regr.mlp", callbacks = t_clbk("checkpoint"))
#> Warning: Learner$initialize argument 'data_formats' is deprecated and will be removed in the future.
# the parameters of the callback are added to the learner's parameter set
learner$param_set
#> <ParamSetCollection(35)>
#>                          id    class lower upper nlevels        default
#>                      <char>   <char> <num> <num>   <num>         <list>
#>  1:                  epochs ParamInt 0e+00   Inf     Inf <NoDefault[0]>
#>  2:                  device ParamFct    NA    NA      12 <NoDefault[0]>
#>  3:             num_threads ParamInt 1e+00   Inf     Inf <NoDefault[0]>
#>  4:                    seed ParamInt  -Inf   Inf     Inf <NoDefault[0]>
#>  5:               eval_freq ParamInt 1e+00   Inf     Inf <NoDefault[0]>
#>  6:          measures_train ParamUty    NA    NA     Inf <NoDefault[0]>
#>  7:          measures_valid ParamUty    NA    NA     Inf <NoDefault[0]>
#>  8:                patience ParamInt 0e+00   Inf     Inf <NoDefault[0]>
#>  9:               min_delta ParamDbl 0e+00   Inf     Inf <NoDefault[0]>
#> 10:              batch_size ParamInt 1e+00   Inf     Inf <NoDefault[0]>
#> 11:                 shuffle ParamLgl    NA    NA       2          FALSE
#> 12:                 sampler ParamUty    NA    NA     Inf <NoDefault[0]>
#> 13:           batch_sampler ParamUty    NA    NA     Inf <NoDefault[0]>
#> 14:             num_workers ParamInt 0e+00   Inf     Inf              0
#> 15:              collate_fn ParamUty    NA    NA     Inf         [NULL]
#> 16:              pin_memory ParamLgl    NA    NA       2          FALSE
#> 17:               drop_last ParamLgl    NA    NA       2          FALSE
#> 18:                 timeout ParamDbl  -Inf   Inf     Inf             -1
#> 19:          worker_init_fn ParamUty    NA    NA     Inf <NoDefault[0]>
#> 20:          worker_globals ParamUty    NA    NA     Inf <NoDefault[0]>
#> 21:         worker_packages ParamUty    NA    NA     Inf <NoDefault[0]>
#> 22:                 neurons ParamUty    NA    NA     Inf <NoDefault[0]>
#> 23:                       p ParamDbl 0e+00 1e+00     Inf <NoDefault[0]>
#> 24:              activation ParamUty    NA    NA     Inf <NoDefault[0]>
#> 25:         activation_args ParamUty    NA    NA     Inf <NoDefault[0]>
#> 26:                   shape ParamUty    NA    NA     Inf <NoDefault[0]>
#> 27:                  opt.lr ParamDbl 0e+00   Inf     Inf          0.001
#> 28:               opt.betas ParamUty    NA    NA     Inf    0.900,0.999
#> 29:                 opt.eps ParamDbl 1e-16 1e-04     Inf          1e-08
#> 30:        opt.weight_decay ParamDbl 0e+00 1e+00     Inf              0
#> 31:             opt.amsgrad ParamLgl    NA    NA       2          FALSE
#> 32:          loss.reduction ParamFct    NA    NA       2           mean
#> 33:      cb.checkpoint.path ParamUty    NA    NA     Inf <NoDefault[0]>
#> 34:      cb.checkpoint.freq ParamInt 1e+00   Inf     Inf <NoDefault[0]>
#> 35: cb.checkpoint.freq_type ParamFct    NA    NA       2          epoch
#>                          id    class lower upper nlevels        default
#>            value
#>           <list>
#>  1:       [NULL]
#>  2:         auto
#>  3:            1
#>  4:       random
#>  5:            1
#>  6:    <list[0]>
#>  7:    <list[0]>
#>  8:            0
#>  9:            0
#> 10:       [NULL]
#> 11:       [NULL]
#> 12:       [NULL]
#> 13:       [NULL]
#> 14:       [NULL]
#> 15:       [NULL]
#> 16:       [NULL]
#> 17:       [NULL]
#> 18:       [NULL]
#> 19:       [NULL]
#> 20:       [NULL]
#> 21:       [NULL]
#> 22:             
#> 23:          0.5
#> 24: <nn_relu[1]>
#> 25:    <list[0]>
#> 26:       [NULL]
#> 27:       [NULL]
#> 28:       [NULL]
#> 29:       [NULL]
#> 30:       [NULL]
#> 31:       [NULL]
#> 32:       [NULL]
#> 33:       [NULL]
#> 34:       [NULL]
#> 35:       [NULL]
#>            value