Torch Callback

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 callback, where the parameters are then of type ParamUty.

Super class

TorchDescriptor -> TorchCallback

Active bindings

weight: (numeric(1) or NULL)
Overwrites the $weight of the generated CallbackSet, see its section Ordering.

Methods

Inherited methods

`TorchCallback$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,
  additional_args = NULL,
  weight = 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().
additional_args: (any)
Additional arguments if necessary. For learning rate schedulers, this is the torch::LRScheduler.
weight: (numeric(1) or NULL)
Overwrites the $weight of the generated CallbackSet, see its section Ordering. If NULL (default), the callback's own weight is kept.

`TorchCallback$generate()`

Generates the CallbackSet, applying $weight if it is set.

Usage

TorchCallback$generate()

`TorchCallback$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(2)>
#>        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]

# 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/RtmpTwMqNF/file1d6d2ff099e7, 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$generate()
callback
#> <CallbackSetCheckpoint>
#> * Stages: on_epoch_end, on_exit
# is the same as
CallbackSetCheckpoint$new(
  path = tempfile(), freq = 1
)
#> <CallbackSetCheckpoint>
#> * Stages: on_epoch_end, on_exit

# Use in a learner
learner = lrn("regr.mlp", callbacks = t_clbk("checkpoint"))
# the parameters of the callback are added to the learner's parameter set
learner$param_set
#> <ParamSetCollection(41)>
#>                       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:  num_interop_threads ParamInt 1e+00   Inf     Inf <NoDefault[0]>
#>  5:                 seed ParamInt  -Inf   Inf     Inf <NoDefault[0]>
#>  6:            eval_freq ParamInt 1e+00   Inf     Inf <NoDefault[0]>
#>  7:       measures_train ParamUty    NA    NA     Inf <NoDefault[0]>
#>  8:       measures_valid ParamUty    NA    NA     Inf <NoDefault[0]>
#>  9:             patience ParamInt 0e+00   Inf     Inf <NoDefault[0]>
#> 10:            min_delta ParamDbl 0e+00   Inf     Inf <NoDefault[0]>
#> 11: restore_best_weights ParamLgl    NA    NA       2 <NoDefault[0]>
#> 12:           batch_size ParamInt 1e+00   Inf     Inf <NoDefault[0]>
#> 13:   batch_size_predict ParamInt 1e+00   Inf     Inf <NoDefault[0]>
#> 14:              shuffle ParamLgl    NA    NA       2          FALSE
#> 15:              sampler ParamUty    NA    NA     Inf <NoDefault[0]>
#> 16:        batch_sampler ParamUty    NA    NA     Inf <NoDefault[0]>
#> 17:          num_workers ParamInt 0e+00   Inf     Inf              0
#> 18:           collate_fn ParamUty    NA    NA     Inf         [NULL]
#> 19:           pin_memory ParamLgl    NA    NA       2          FALSE
#> 20:            drop_last ParamLgl    NA    NA       2          FALSE
#> 21:              timeout ParamDbl  -Inf   Inf     Inf             -1
#> 22:       worker_init_fn ParamUty    NA    NA     Inf <NoDefault[0]>
#> 23:       worker_globals ParamUty    NA    NA     Inf <NoDefault[0]>
#> 24:      worker_packages ParamUty    NA    NA     Inf <NoDefault[0]>
#> 25:       tensor_dataset ParamFct    NA    NA       1 <NoDefault[0]>
#> 26:            jit_trace ParamLgl    NA    NA       2 <NoDefault[0]>
#> 27:              neurons ParamUty    NA    NA     Inf <NoDefault[0]>
#> 28:                    p ParamDbl 0e+00     1     Inf <NoDefault[0]>
#> 29:             n_layers ParamInt 1e+00   Inf     Inf <NoDefault[0]>
#> 30:           activation ParamUty    NA    NA     Inf <NoDefault[0]>
#> 31:      activation_args ParamUty    NA    NA     Inf <NoDefault[0]>
#> 32:                shape ParamUty    NA    NA     Inf <NoDefault[0]>
#> 33:               opt.lr ParamDbl 0e+00   Inf     Inf          0.001
#> 34:            opt.betas ParamUty    NA    NA     Inf    0.900,0.999
#> 35:              opt.eps ParamDbl 1e-16   Inf     Inf          1e-08
#> 36:     opt.weight_decay ParamDbl 0e+00   Inf     Inf              0
#> 37:          opt.amsgrad ParamLgl    NA    NA       2          FALSE
#> 38:     opt.param_groups ParamUty    NA    NA     Inf <NoDefault[0]>
#> 39:       loss.reduction ParamFct    NA    NA       2           mean
#> 40:   cb.checkpoint.path ParamUty    NA    NA     Inf <NoDefault[0]>
#> 41:   cb.checkpoint.freq ParamInt 1e+00   Inf     Inf <NoDefault[0]>
#>                       id    class lower upper nlevels        default
#>                   <char>   <char> <num> <num>   <num>         <list>
#>            value
#>           <list>
#>  1:       [NULL]
#>  2:         auto
#>  3:            1
#>  4:       [NULL]
#>  5:       random
#>  6:            1
#>  7:    <list[0]>
#>  8:    <list[0]>
#>  9:            0
#> 10:            0
#> 11:        FALSE
#> 12:       [NULL]
#> 13:       [NULL]
#> 14:         TRUE
#> 15:       [NULL]
#> 16:       [NULL]
#> 17:       [NULL]
#> 18:       [NULL]
#> 19:       [NULL]
#> 20:       [NULL]
#> 21:       [NULL]
#> 22:       [NULL]
#> 23:       [NULL]
#> 24:       [NULL]
#> 25:        FALSE
#> 26:        FALSE
#> 27:             
#> 28:          0.1
#> 29:       [NULL]
#> 30: <nn_relu[1]>
#> 31:    <list[0]>
#> 32:       [NULL]
#> 33:       [NULL]
#> 34:       [NULL]
#> 35:       [NULL]
#> 36:       [NULL]
#> 37:       [NULL]
#> 38:       [NULL]
#> 39:       [NULL]
#> 40:       [NULL]
#> 41:       [NULL]
#>            value
#>           <list>

Parameters

See also

Super class

Active bindings

Methods

Public methods

TorchCallback$new()

Usage

Arguments

TorchCallback$generate()

Usage

TorchCallback$clone()

Usage

Arguments

Examples

`TorchCallback$new()`

`TorchCallback$generate()`

`TorchCallback$clone()`