mlr_learners.tab_resnet function

Tabular ResNet

Tabular resnet.

Dictionary

This Learner can be instantiated using the sugar function lrn():

lrn("classif.tab_resnet", ...)
lrn("regr.tab_resnet", ...)

Properties

• Supported task types: 'classif', 'regr'

• Predict Types:

  • classif: 'response', 'prob'
  • regr: 'response'

• Feature Types: integer , numeric

• Required Packages: list("mlr3"), list("mlr3torch"), list("torch")

Parameters

Parameters from LearnerTorch, as well as:

• n_blocks :: integer(1)

  The number of blocks.

• d_block :: integer(1)

  The input and output dimension of a block.

• d_hidden :: integer(1)

  The latent dimension of a block.

• d_hidden_multiplier :: numeric(1)

  Alternative way to specify the latent dimension as d_block * d_hidden_multiplier.

• dropout1 :: numeric(1)

  First dropout ratio.

• dropout2 :: numeric(1)

  Second dropout ratio.

Examples

# Define the Learner and set parameter values
learner = lrn("classif.tab_resnet")
learner$param_set$set_values(
  epochs = 1, batch_size = 16, device = "cpu",
  n_blocks = 2, d_block = 10, d_hidden = 20, dropout1 = 0.3, dropout2 = 0.3
)

# Define a Task
task = tsk("iris")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

References

Gorishniy Y, Rubachev I, Khrulkov V, Babenko A (2021). Revisiting Deep Learning for Tabular Data.

arXiv, 2106.11959 .

See Also

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

Super classes

mlr3::Learner -> mlr3torch::LearnerTorch -> LearnerTorchTabResNet

Methods

Public methods

• list("LearnerTorchTabResNet$new()")

• list("LearnerTorchTabResNet$clone()")

Method new()

Creates a new instance of this R6 class.

Usage

LearnerTorchTabResNet$new(
  task_type,
  optimizer = NULL,
  loss = NULL,
  callbacks = list()
)

Arguments

• task_type: (character(1))

   The task type, either `"classif`" or `"regr"`.
  

• optimizer: (TorchOptimizer)

   The optimizer to use for training. Per default, **adam** is used.
  

• loss: (TorchLoss)

   The loss used to train the network. Per default, **mse** is used for regression and **cross_entropy** for classification.
  

• callbacks: (list() of TorchCallbacks)

   The callbacks. Must have unique ids.
  

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerTorchTabResNet$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.