% 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}

}