evaluate_mean_squared_error | R Documentation |
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, ...)
learner |
A trained learner object |
data |
Test data for evaluation |
... |
Additional parameters passed to |
A named list with the autoencoder training loss and evaluation metric for the given data
\link{evaluation_metric}
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)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.