% 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}

}