% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/metrics.R
\name{f1_wrapper}
\alias{f1_wrapper}
\title{F1 metric}
\usage{
f1_wrapper(num_targets = 2, loss = "binary_crossentropy")
}
\arguments{
\item{num_targets}{Size of model output.}

\item{loss}{Loss function of model.}
}
\value{
A keras metric.
}
\description{
Compute F1 metric. If loss is \code{"categorical_crossentropy"}, number of targets must be 2. If
loss is \code{"binary_crossentropy"} and number of targets > 1, will flatten \code{y_true} and \code{y_pred} matrices
to a single vector (rather than computing separate F1 scores for each class).
}
\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)  
y_pred <-  c(0.9,0.05,0.05,0.9,0.05,0.05,0.9,0.05,0.05) 
\donttest{
library(keras)
f1_metric <- f1_wrapper(3L, "binary_crossentropy")
f1_metric$update_state(y_true, y_pred)
f1_metric$result()  


# add metric to a model

num_targets <- 1
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))

f1_metric <- f1_wrapper(num_targets, loss = model$loss)
model \%>\% keras::compile(loss = model$loss, 
                         optimizer = model$optimizer,
                         metrics = c(model$metrics, f1_metric))
}                    
\dontshow{\}) # examplesIf}
}