callback_average_model_checkpoint: Average Model Checkpoint

In tfaddons: Interface to 'TensorFlow SIG Addons'

Description

Save the model after every epoch.

Usage

callback_average_model_checkpoint(
  filepath,
  update_weights,
  monitor = "val_loss",
  verbose = 0,
  save_best_only = FALSE,
  save_weights_only = FALSE,
  mode = "auto",
  save_freq = "epoch",
  ...
)

Arguments

filepath	string, path to save the model file.
update_weights	bool, wheteher to update weights or not
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. If ‘filepath' doesn’t contain formatting options like 'epoch' then 'filepath' will be overwritten by each new better model.
save_weights_only	if TRUE, then only the model's weights will be saved ('model$save_weights(filepath)'), else the full model is saved ('model$save(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.
save_freq	''epoch'‘ or integer. When using '’epoch'', the callback saves the model after each epoch. When using integer, the callback saves the model at end of a batch at which this many samples have been seen since last saving. Note that if the saving isn't aligned to epochs, the monitored metric may potentially be less reliable (it could reflect as little as 1 batch, since the metrics get reset every epoch). Defaults to ''epoch''
...	Additional arguments for backwards compatibility. Possible key is 'period'.

Details

The callback that should be used with optimizers that extend AverageWrapper, i.e., MovingAverage and StochasticAverage optimizers. It saves and, optionally, assigns the averaged weights.

Value

None

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.

tfaddons documentation built on July 2, 2020, 2:12 a.m.