ruta (version 1.0.2)

evaluate_mean_squared_error: 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

Value

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

See Also

evaluation_metric

Examples

# NOT RUN {
library(purrr)

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

# }
# NOT RUN {
autoencoder(2) %>%
  train(x_train) %>%
  evaluate_mean_squared_error(x_test)
# }
# NOT RUN {
# }