--- a
+++ b/man/compile_model.Rd
@@ -0,0 +1,64 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/create_model_utils.R
+\name{compile_model}
+\alias{compile_model}
+\title{Compile model}
+\usage{
+compile_model(
+  model,
+  solver,
+  learning_rate,
+  loss_fn,
+  label_smoothing = 0,
+  num_output_layers = 1,
+  label_noise_matrix = NULL,
+  bal_acc = FALSE,
+  f1_metric = FALSE,
+  auc_metric = FALSE,
+  layer_dense = NULL
+)
+}
+\arguments{
+\item{model}{A keras model.}
+
+\item{solver}{Optimization method, options are \verb{"adam", "adagrad", "rmsprop"} or \code{"sgd"}.}
+
+\item{learning_rate}{Learning rate for optimizer.}
+
+\item{loss_fn}{Either \code{"categorical_crossentropy"} or \code{"binary_crossentropy"}. If \code{label_noise_matrix} given, will use custom \code{"noisy_loss"}.}
+
+\item{label_smoothing}{Float in [0, 1]. If 0, no smoothing is applied. If > 0, loss between the predicted
+labels and a smoothed version of the true labels, where the smoothing squeezes the labels towards 0.5.
+The closer the argument is to 1 the more the labels get smoothed.}
+
+\item{num_output_layers}{Number of output layers.}
+
+\item{label_noise_matrix}{Matrix of label noises. Every row stands for one class and columns for percentage of labels in that class.
+If first label contains 5 percent wrong labels and second label no noise, then
+
+\code{label_noise_matrix <- matrix(c(0.95, 0.05, 0, 1), nrow = 2, byrow = TRUE )}}
+
+\item{bal_acc}{Whether to add balanced accuracy.}
+
+\item{f1_metric}{Whether to add F1 metric.}
+
+\item{auc_metric}{Whether to add AUC metric.}
+
+\item{layer_dense}{Vector specifying number of neurons per dense layer after last LSTM or CNN layer (if no LSTM used).}
+}
+\value{
+A compiled keras model.
+}
+\description{
+Compile model
+}
+\examples{
+\dontshow{if (reticulate::py_module_available("tensorflow")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+
+model <- create_model_lstm_cnn(layer_lstm = 8, compile = FALSE)
+model <- compile_model(model = model,
+                       solver = 'adam',
+                      learning_rate = 0.01,
+                       loss_fn = 'categorical_crossentropy')
+\dontshow{\}) # examplesIf}
+}