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: mlr3, mlr3torch, 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 :: integer(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.

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

• LearnerTorchTabResNet$new()

• LearnerTorchTabResNet$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$reset()
• mlr3::Learner$train()
• mlr3torch::LearnerTorch$dataset()
• mlr3torch::LearnerTorch$format()
• mlr3torch::LearnerTorch$marshal()
• mlr3torch::LearnerTorch$print()
• mlr3torch::LearnerTorch$unmarshal()

---

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.

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()
#> classif.ce 
#>        0.9