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Specifies when to prune layer and the sparsity(%) at each training step.
PruningSchedule controls pruning during training by notifying at each step whether the layer's weights should be pruned or not, and the sparsity(%) at which they should be pruned.
It can be invoked as a
callable by providing the training
step Tensor. It
returns a tuple of bool and float tensors.
should_prune, sparsity = pruning_schedule(step)
You can inherit this class to write your own custom pruning schedule.
from_config( config )
PruningSchedule from its config.
__call__( step )
Returns the sparsity(%) to be applied.
If the returned sparsity(%) is 0, pruning is ignored for the step.
||Current step in graph execution.|
|Sparsity (%) that should be applied to the weights for the step.|