View source on GitHub |
The callback that saves average model weights.
tfa.callbacks.AverageModelCheckpoint(
update_weights: bool,
filepath: str,
monitor: str = 'val_loss',
verbose: int = 0,
save_best_only: bool = False,
save_weights_only: bool = False,
mode: str = 'auto',
save_freq: str = 'epoch',
**kwargs
)
Used in the notebooks
Used in the tutorials |
---|
The callback that should be used with optimizers that extend
tfa.optimizers.AveragedOptimizerWrapper
, i.e.,
tfa.optimizers.MovingAverage
and
tfa.optimizers.StochasticAverage
optimizers.
It saves and, optionally, assigns the averaged weights.
Args | |
---|---|
update_weights
|
If True , assign the moving average weights
to the model, and save them. If False, keep the old
non-averaged weights, but the saved model uses the
average weights.
See |
Methods
set_model
set_model(
model
)
set_params
set_params(
params
)