Classification Multilayer Perceptron Learner

Calls RSNNS::mlp() from RSNNS.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.mlp")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, RSNNS

Parameters

Id	Type	Default	Levels	Range
size	untyped	5L		-
maxit	integer	100		\([1, \infty)\)
initFunc	untyped	"Randomize_Weights"		-
initFuncParams	untyped	-		-
learnFunc	untyped	"Std_Backpropagation"		-
learnFuncParams	untyped	-		-
updateFunc	untyped	"Topological_Order"		-
updateFuncParams	untyped	-		-
hiddenActFunc	untyped	"Act_Logistic"		-
shufflePatterns	logical	TRUE	TRUE, FALSE	-
linOut	logical	FALSE	TRUE, FALSE	-
inputsTest	untyped	-		-
targetsTest	untyped	-		-
pruneFunc	untyped	-		-
pruneFuncParams	untyped	-		-

References

r format_bib("rsnns2023")

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

awinterstetter

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifMLP

Methods

Public methods

• LearnerClassifMLP$new()

• LearnerClassifMLP$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifMLP$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifMLP$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.mlp")
print(learner)
#> 
#> ── <LearnerClassifMLP> (classif.mlp): Multi-Layer Perceptron ───────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3 and RSNNS
#> • Predict Types: [response] and prob
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass and twoclass
#> • Other settings: use_weights = 'error'

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

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

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

print(learner$model)
#> Class: mlp->rsnns
#> Number of inputs: 60 
#> Number of outputs: 2 
#> Maximal iterations: 100 
#> Initialization function: Randomize_Weights 
#> Initialization function parameters: -0.3 0.3 
#> Learning function: Std_Backpropagation 
#> Learning function parameters: 0.2 0 
#> Update function:Topological_Order
#> Update function parameters: 0 
#> Patterns are shuffled internally: TRUE 
#> Compute error in every iteration: TRUE 
#> Architecture Parameters:
#> $size
#> [1] 5
#> 
#> All members of model:
#>  [1] "nInputs"               "maxit"                 "initFunc"             
#>  [4] "initFuncParams"        "learnFunc"             "learnFuncParams"      
#>  [7] "updateFunc"            "updateFuncParams"      "shufflePatterns"      
#> [10] "computeIterativeError" "snnsObject"            "archParams"           
#> [13] "IterativeFitError"     "fitted.values"         "nOutputs"             


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2608696