mlr_learners_torch_model function

Learner Torch Model

Create a torch learner from an instantiated nn_module(). For classification, the output of the network must be the scores (before the softmax).

Parameters

See LearnerTorch

Examples

# We show the learner using a classification task

# The iris task has 4 features and 3 classes
network = nn_linear(4, 3)
task = tsk("iris")

# This defines the dataloader.
# It loads all 4 features, which are also numeric.
# The shape is (NA, 4) because the batch dimension is generally NA
ingress_tokens = list(
  input = TorchIngressToken(task$feature_names, batchgetter_num, c(NA, 4))
)

# Creating the learner and setting required parameters
learner = lrn("classif.torch_model",
  network = network,
  ingress_tokens = ingress_tokens,
  batch_size = 16,
  epochs = 1,
  device = "cpu"
)

# A simple train-predict
ids = partition(task)
learner$train(task, ids$train)
learner$predict(task, ids$test)

See Also

Other Learner: mlr_learners.mlp, mlr_learners.tab_resnet, mlr_learners.torch_featureless, mlr_learners_torch, mlr_learners_torch_image

Other Graph Network: ModelDescriptor(), TorchIngressToken(), mlr_pipeops_module, mlr_pipeops_torch, mlr_pipeops_torch_ingress, mlr_pipeops_torch_ingress_categ, mlr_pipeops_torch_ingress_ltnsr, mlr_pipeops_torch_ingress_num, model_descriptor_to_learner(), model_descriptor_to_module(), model_descriptor_union(), nn_graph()

Super classes

mlr3::Learner -> mlr3torch::LearnerTorch -> LearnerTorchModel

Active bindings

• ingress_tokens: (named list() with TorchIngressToken or NULL)

   The ingress tokens. Must be non-`NULL` when calling `$train()`.
  

Methods

Public methods

• list("LearnerTorchModel$new()")

• list("LearnerTorchModel$clone()")

Method new()

Creates a new instance of this R6 class.

Usage

LearnerTorchModel$new(
  network = NULL,
  ingress_tokens = NULL,
  task_type,
  properties = NULL,
  optimizer = NULL,
  loss = NULL,
  callbacks = list(),
  packages = character(0),
  feature_types = NULL
)

Arguments

• network: (nn_module)

   An instantiated `nn_module`. Is not cloned during construction. For classification, outputs must be the scores (before the softmax).
  

• ingress_tokens: (list of TorchIngressToken())

   A list with ingress tokens that defines how the dataloader will be defined.
  

• task_type: (character(1))

   The task type.
  

• properties: (NULL or character())

   The properties of the learner. Defaults to all available properties for the given task type.
  

• optimizer: (TorchOptimizer)

   The torch optimizer.
  

• loss: (TorchLoss)

   The loss to use for training.
  

• callbacks: (list() of TorchCallbacks)

   The callbacks used during training. Must have unique ids. They are executed in the order in which they are provided
  

• packages: (character())

   The R packages this object depends on.
  

• feature_types: (NULL or character())

   The feature types. Defaults to all available feature types.
  

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerTorchModel$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.