Save the model after every epoch.

filepath can contain named formatting options, which will be filled the value of epoch and keys in logs (passed in on_epoch_end). For example: if filepath is weights.{epoch:02d}-{val_loss:.2f}.hdf5, then the model checkpoints will be saved with the epoch number and the validation loss in the filename.

callback_model_checkpoint(filepath, monitor = "val_loss", verbose = 0,
  save_best_only = FALSE, save_weights_only = FALSE, mode = c("auto",
  "min", "max"), period = 1)

Arguments

filepath

string, path to save the model file.
monitor

quantity to monitor.
verbose

verbosity mode, 0 or 1.
save_best_only

if save_best_only=TRUE, the latest best model according to the quantity monitored will not be overwritten.
save_weights_only

if TRUE, then only the model's weights will be saved (save_model_weights_hdf5(filepath)), else the full model is saved (save_model_hdf5(filepath)).
mode

one of "auto", "min", "max". If save_best_only=TRUE, the decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity. For val_acc, this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically inferred from the name of the monitored quantity.
period

Interval (number of epochs) between checkpoints.

For example

if filepath is weights.{epoch:02d}-{val_loss:.2f}.hdf5,: then the model checkpoints will be saved with the epoch number and the validation loss in the filename.

See also

Other callbacks: callback_csv_logger, callback_early_stopping, callback_lambda, callback_learning_rate_scheduler, callback_progbar_logger, callback_reduce_lr_on_plateau, callback_remote_monitor, callback_tensorboard, callback_terminate_on_naan