% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/evaluation.R

\name{evaluate_softmax}

\alias{evaluate_softmax}

\title{Evaluate matrices of true targets and predictions from layer with softmax activation.}

\usage{

evaluate_softmax(y, y_conf, auc = FALSE, auprc = FALSE, label_names = NULL)

}

\arguments{

\item{y}{Matrix of true target.}

\item{y_conf}{Matrix of predictions.}

\item{auc}{Whether to include AUC metric. Only possible for 2 targets.}

\item{auprc}{Whether to include AUPRC metric. Only possible for 2 targets.}

\item{label_names}{Names of corresponding labels. Length must be equal to number of columns of \code{y}.}

}

\value{

A list of evaluation results.

}

\description{

Compute confusion matrix, accuracy, categorical crossentropy and (optionally) AUC or AUPRC, given predictions and

true targets. AUC and AUPRC only possible for 2 targets.

}

\examples{

\dontshow{if (reticulate::py_module_available("tensorflow")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}

y <- matrix(c(1, 0, 0, 0, 1, 1), ncol = 2)

y_conf <- matrix(c(0.3, 0.5, 0.1, 0.7, 0.5, 0.9), ncol = 2)

evaluate_softmax(y, y_conf, auc = TRUE, auprc = TRUE, label_names = c("A", "B")) 

\dontshow{\}) # examplesIf}

}