callback_swap_ema_weights | R Documentation |
This callbacks replaces the model's weight values with the values of the optimizer's EMA weights (the exponential moving average of the past model weights values, implementing "Polyak averaging") before model evaluation, and restores the previous weights after evaluation.
The SwapEMAWeights
callback is to be used in conjunction with
an optimizer that sets use_ema = TRUE
.
Note that the weights are swapped in-place in order to save memory. The behavior is undefined if you modify the EMA weights or model weights in other callbacks.
callback_swap_ema_weights(swap_on_epoch = FALSE)
swap_on_epoch |
Whether to perform swapping at |
A Callback
instance that can be passed to fit.keras.src.models.model.Model()
.
# Remember to set `use_ema=TRUE` in the optimizer optimizer <- optimizer_sgd(use_ema = TRUE) model |> compile(optimizer = optimizer, loss = ..., metrics = ...) # Metrics will be computed with EMA weights model |> fit(X_train, Y_train, callbacks = c(callback_swap_ema_weights())) # If you want to save model checkpoint with EMA weights, you can set # `swap_on_epoch=TRUE` and place ModelCheckpoint after SwapEMAWeights. model |> fit( X_train, Y_train, callbacks = c( callback_swap_ema_weights(swap_on_epoch = TRUE), callback_model_checkpoint(...) ) )
Other callbacks:
Callback()
callback_backup_and_restore()
callback_csv_logger()
callback_early_stopping()
callback_lambda()
callback_learning_rate_scheduler()
callback_model_checkpoint()
callback_reduce_lr_on_plateau()
callback_remote_monitor()
callback_tensorboard()
callback_terminate_on_nan()
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