Assuming the goal of a training is to minimize the loss. With this, the
metric to be monitored would be 'loss', and mode would be 'min'. A
model$fit() training loop will check at end of every epoch whether
the loss is no longer decreasing, considering the min_delta and
patience if applicable. Once it's found no longer decreasing,
model$stop_training is marked TRUE and the training terminates.
The quantity to be monitored needs to be available in logs list.
To make it so, pass the loss or metrics at model$compile().
callback_early_stopping(
monitor = "val_loss",
min_delta = 0L,
patience = 0L,
verbose = 0L,
mode = "auto",
baseline = NULL,
restore_best_weights = FALSE,
start_from_epoch = 0L
)A Callback instance that can be passed to fit.keras.src.models.model.Model().
Quantity to be monitored. Defaults to "val_loss".
Minimum change in the monitored quantity to qualify as an
improvement, i.e. an absolute change of less than min_delta, will
count as no improvement. Defaults to 0.
Number of epochs with no improvement after which training will
be stopped. Defaults to 0.
Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1 displays
messages when the callback takes an action. Defaults to 0.
One of {"auto", "min", "max"}. In min mode, training will stop
when the quantity monitored has stopped decreasing; in "max" mode
it will stop when the quantity monitored has stopped increasing; in
"auto" mode, the direction is automatically inferred from the name
of the monitored quantity. Defaults to "auto".
Baseline value for the monitored quantity. If not NULL,
training will stop if the model doesn't show improvement over the
baseline. Defaults to NULL.
Whether to restore model weights from the epoch
with the best value of the monitored quantity. If FALSE, the model
weights obtained at the last step of training are used. An epoch
will be restored regardless of the performance relative to the
baseline. If no epoch improves on baseline, training will run
for patience epochs and restore weights from the best epoch in
that set. Defaults to FALSE.
Number of epochs to wait before starting to monitor
improvement. This allows for a warm-up period in which no
improvement is expected and thus training will not be stopped.
Defaults to 0.
callback <- callback_early_stopping(monitor = 'loss',
patience = 3)
# This callback will stop the training when there is no improvement in
# the loss for three consecutive epochs.
model <- keras_model_sequential() %>%
layer_dense(10)
model %>% compile(optimizer = optimizer_sgd(), loss = 'mse')
history <- model %>% fit(x = op_ones(c(5, 20)),
y = op_zeros(5),
epochs = 10, batch_size = 1,
callbacks = list(callback),
verbose = 0)
nrow(as.data.frame(history)) # Only 4 epochs are run.
## [1] 10Other callbacks:
Callback()
callback_backup_and_restore()
callback_csv_logger()
callback_lambda()
callback_learning_rate_scheduler()
callback_model_checkpoint()
callback_reduce_lr_on_plateau()
callback_remote_monitor()
callback_swap_ema_weights()
callback_tensorboard()
callback_terminate_on_nan()