% 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}

}