% 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}
}