KerasCallback | R Documentation |
New custom callbacks implemented as R6 classes are encouraged to inherit from
keras$callbacks$Callback
directly.
An R6Class generator object
The logs
named list that callback methods take as argument will
contain keys for quantities relevant to the current batch or epoch.
Currently, the fit.keras.engine.training.Model()
method for sequential
models will include the following quantities in the logs
that
it passes to its callbacks:
on_epoch_end
: logs include acc
and loss
, and optionally include val_loss
(if validation is enabled in fit
), and val_acc
(if validation and accuracy monitoring are enabled).
on_batch_begin
: logs include size
, the number of samples in the current batch.
on_batch_end
: logs include loss
, and optionally acc
(if accuracy monitoring is enabled).
KerasCallback.
params
Named list with training parameters (eg. verbosity, batch size, number of epochs...).
model
Reference to the Keras model being trained.
on_epoch_begin(epoch, logs)
Called at the beginning of each epoch.
on_epoch_end(epoch, logs)
Called at the end of each epoch.
on_batch_begin(batch, logs)
Called at the beginning of each batch.
on_batch_end(batch, logs)
Called at the end of each batch.
on_train_begin(logs)
Called at the beginning of training.
on_train_end(logs)
Called at the end of training.
## Not run:
library(keras)
LossHistory <- R6::R6Class("LossHistory",
inherit = KerasCallback,
public = list(
losses = NULL,
on_batch_end = function(batch, logs = list()) {
self$losses <- c(self$losses, logs[["loss"]])
}
)
)
## End(Not run)
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