Performance evaluation metrics for autoencoders

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

Value

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

See also

Examples

library(purrr) x <- as.matrix(sample(iris[, 1:4])) x_train <- x[1:100, ] x_test <- x[101:150, ]
autoencoder(2) %>% train(x_train) %>% evaluate_mean_squared_error(x_test)
#> $loss #> [1] 5.88162 #> #> [[2]] #> [1] 5.88162 #>