tfmot.sparsity.keras.PruningSchedule

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.