tfmot.sparsity.keras.strip_pruning

Strip pruning wrappers from the model.

tfmot.sparsity.keras.strip_pruning(
    model
)

Used in the guide
Sparsity and cluster preserving quantization aware training (PCQAT) Keras example Pruning preserving quantization aware training (PQAT) Keras example Sparsity preserving clustering Keras example Pruning comprehensive guide Pruning for on-device inference w/ XNNPACK

Once a model has been pruned to required sparsity, this method can be used to restore the original model with the sparse weights.

Only sequential and functional models are supported for now.

Arguments
`model`	A `tf.keras.Model` instance with pruned layers.

Returns
A keras model with pruning wrappers removed.

Raises
`ValueError`	if the model is not a `tf.keras.Model` instance.
`NotImplementedError`	if the model is a subclass model.

orig_model = tf.keras.Model(inputs, outputs)
pruned_model = prune_low_magnitude(orig_model)
exported_model = strip_pruning(pruned_model)

The exported_model and the orig_model share the same structure.

Arguments