TorchLoss {mlr3torch}

Torch Loss

Description

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

This class is usually used to configure the loss function of a torch learner, e.g. when construcing a learner or in a ModelDescriptor.

For a list of available losses, see mlr3torch_losses. Items from this dictionary can be retrieved using t_loss().

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 -> TorchLoss

Public fields

task_types

(character())
The task types this loss supports.

Methods

Public methods

• TorchLoss$new()

• TorchLoss$print()

• TorchLoss$clone()

Inherited methods

• mlr3torch::TorchDescriptor$generate()
• mlr3torch::TorchDescriptor$help()

Method new()

Creates a new instance of this R6 class.

Usage

TorchLoss$new(
  torch_loss,
  task_types = NULL,
  param_set = NULL,
  id = NULL,
  label = NULL,
  packages = NULL,
  man = NULL
)

Arguments

torch_loss

(nn_loss)
The loss module.

task_types

(character())
The task types supported by this loss.

param_set

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

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 print()

Prints the object

Usage

TorchLoss$print(...)

Arguments

...

any

Method clone()

The objects of this class are cloneable with this method.

Usage

TorchLoss$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other Torch Descriptor: TorchCallback, TorchDescriptor, TorchOptimizer, as_torch_callbacks(), as_torch_loss(), as_torch_optimizer(), mlr3torch_losses, mlr3torch_optimizers, t_clbk(), t_loss(), t_opt()

Examples

# Create a new torch loss
torch_loss = TorchLoss$new(torch_loss = nn_mse_loss, task_types = "regr")
torch_loss
# the parameters are inferred
torch_loss$param_set

# Retrieve a loss from the dictionary:
torch_loss = t_loss("mse", reduction = "mean")
# is the same as
torch_loss
torch_loss$param_set
torch_loss$label
torch_loss$task_types
torch_loss$id

# Create the loss function
loss_fn = torch_loss$generate()
loss_fn
# Is the same as
nn_mse_loss(reduction = "mean")

# open the help page of the wrapped loss function
# torch_loss$help()

# Use in a learner
learner = lrn("regr.mlp", loss = t_loss("mse"))
# The parameters of the loss are added to the learner's parameter set
learner$param_set