% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/metrics.R
\name{auc_wrapper}
\alias{auc_wrapper}
\title{Mean AUC score}
\usage{
auc_wrapper(model_output_size, loss = "binary_crossentropy")
}
\arguments{
\item{model_output_size}{Number of neurons in model output layer.}

\item{loss}{Loss function of model, for which metric will be applied to; must be \code{"binary_crossentropy"}
or \code{"categorical_crossentropy"}.}
}
\value{
A keras metric.
}
\description{
Compute AUC score as additional metric. If model has several output neurons with binary crossentropy loss, will use the average score.
}
\examples{
\dontshow{if (reticulate::py_module_available("tensorflow")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}

y_true <- c(1,0,0,1,1,0,1,0,0) \%>\% matrix(ncol = 3)
y_pred <- c(0.9,0.05,0.05,0.9,0.05,0.05,0.9,0.05,0.05) \%>\% matrix(ncol = 3)

\donttest{
library(keras)
auc_metric <- auc_wrapper(3L, "binary_crossentropy")

auc_metric$update_state(y_true, y_pred)
auc_metric$result()  

# add metric to a model
num_targets <- 4
model <- create_model_lstm_cnn(maxlen = 20,
                               layer_lstm = 8,
                               bal_acc = FALSE,
                               last_layer_activation = "sigmoid",
                               loss_fn = "binary_crossentropy",
                               layer_dense = c(8, num_targets))

auc_metric <- auc_wrapper(num_targets, loss = model$loss)
model \%>\% keras::compile(loss = model$loss, 
                         optimizer = model$optimizer,
                         metrics = c(model$metrics, auc_metric))
}
\dontshow{\}) # examplesIf}
}