% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/train.R
\name{resume_training_from_model_card}
\alias{resume_training_from_model_card}
\title{Continue training from model card}
\usage{
resume_training_from_model_card(
  path_model_card,
  seed = NULL,
  epoch = NULL,
  new_run_name = NULL,
  new_args = NULL,
  new_compile = NULL,
  use_mirrored_strategy = NULL,
  unfreeze = FALSE,
  verbose = FALSE
)
}
\arguments{
\item{path_model_card}{Path to model card to resume training from.}

\item{seed}{Seed for reproducible results. If \code{NULL}, set random seed.}

\item{epoch}{Epoch to resume from. If \code{NULL}, use last epoch.}

\item{new_run_name}{New run name. If \code{NULL}, new run name is old run name + '_cont'.}

\item{new_args}{Named list of arguments to overwrite. Will use previous arguments from model card otherwise.
For example, if you want to change the batch size and padding option:
\code{new_args = list(batch_size = 6, padding = TRUE)}.}

\item{new_compile}{List of arguments to compile the model again. If \code{NULL}, use compiled model from checkpoint.
Example: \code{new_compile = list(loss = 'binary_crossentropy', metrics = 'acc', optimizer = keras::optimizer_adam())}}

\item{use_mirrored_strategy}{Whether to use distributed mirrored strategy.
If NULL, will use distributed mirrored strategy only if >1 GPU available.}

\item{unfreeze}{If \code{TRUE}, set trainable attribute of model to \code{TRUE} (unfreeze weights).}

\item{verbose}{Whether to print all training arguments.}
}
\value{
A list of training metrics.
}
\description{
Use information from model card to resume from the corresponding checkpoint using the same training arguments.
}
\examples{
\donttest{
library(keras)
# create dummy data and temp directories
path_train_1 <- tempfile()
path_train_2 <- tempfile()
path_val_1 <- tempfile()
path_val_2 <- tempfile()
path_checkpoint <- tempfile()
dir.create(path_checkpoint)
path_model_card <- tempfile()
dir.create(path_model_card)

for (current_path in c(path_train_1, path_train_2,
                       path_val_1, path_val_2)) {
  dir.create(current_path)
  create_dummy_data(file_path = current_path,
                    num_files = 3,
                    seq_length = 10,
                    num_seq = 5,
                    vocabulary = c("a", "c", "g", "t"))
}

# create model
model <- create_model_lstm_cnn(layer_lstm = 8, layer_dense = 2, maxlen = 5)

# train model
run_name <- 'test_run_1'
hist <- train_model(train_type = "label_folder",
                    run_name = run_name,
                    path_checkpoint = path_checkpoint,
                    model_card = list(path_model_card = path_model_card, description = 'test run'),
                    model = model,
                    path = c(path_train_1, path_train_2),
                    path_val = c(path_val_1, path_val_2),
                    batch_size = 8,
                    epochs = 3,
                    steps_per_epoch = 6,
                    vocabulary_label = c("label_1", "label_2"))

# resume training
resume_training_from_model_card(path_model_card = file.path(path_model_card, run_name))
}
}