tfmot.sparsity.keras.PruningSchedule
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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.
Methods
from_config
View source
@classmethod
from_config(
config
)
Instantiates a PruningSchedule
from its config.
Args |
config
|
Output of get_config() .
|
Returns |
A PruningSchedule instance.
|
get_config
View source
@abc.abstractmethod
get_config()
__call__
View source
@abc.abstractmethod
__call__(
step
)
Returns the sparsity(%) to be applied.
If the returned sparsity(%) is 0, pruning is ignored for the step.
Args |
step
|
Current step in graph execution.
|
Returns |
Sparsity (%) that should be applied to the weights for the step.
|
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Last updated 2023-05-26 UTC.
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