evaluate: Evaluation metrics

In ruta: Implementation of Unsupervised Neural Architectures

Evaluation metrics

Description

Performance evaluation metrics for autoencoders

Usage

evaluate_mean_squared_error(learner, data, ...)

evaluate_mean_absolute_error(learner, data, ...)

evaluate_binary_crossentropy(learner, data, ...)

evaluate_binary_accuracy(learner, data, ...)

evaluate_kullback_leibler_divergence(learner, data, ...)

Arguments

learner	A trained learner object
data	Test data for evaluation
...	Additional parameters passed to keras::\link[keras]{evaluate}.

Value

A named list with the autoencoder training loss and evaluation metric for the given data

See Also

\link{evaluation_metric}

Examples

x <- as.matrix(sample(iris[, 1:4]))
x_train <- x[1:100, ]
x_test <- x[101:150, ]


if (interactive() && keras::is_keras_available()) {
  autoencoder(2) |>
    train(x_train) |>
    evaluate_mean_squared_error(x_test)
}

ruta documentation built on Jan. 9, 2023, 1:20 a.m.