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

...

Additional parameters passed to keras::evaluate.

Value

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

See also

Examples

library(purrr)
#> #> Attaching package: ‘purrr’
#> The following object is masked from ‘package:testthat’: #> #> is_null
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] 16.60996 #> #> [[2]] #> [1] 16.60996 #>