|View source on GitHub|
The callback that should be used with optimizers that extend
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 )
AverageWrapper, i.e., MovingAverage and StochasticAverage optimizers. It saves and, optionally, assigns the averaged weights.
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.
tf.keras.callbacks.ModelCheckpoint for the other args.
set_model( model )
set_params( params )