tfmot.clustering.keras.strip_clustering

Strips clustering wrappers from the model.

tfmot.clustering.keras.strip_clustering(
    model
)

Used in the guide
Weight clustering in Keras example Cluster preserving quantization aware training (CQAT) Keras example Sparsity and cluster preserving quantization aware training (PCQAT) Keras example Sparsity preserving clustering Keras example

Once a model has been clustered, this method can be used to restore the original model with the clustered weights.

Only sequential and functional models are supported for now.

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

Returns
A keras model with clustering 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)
clustered_model = cluster_weights(orig_model)
exported_model = strip_clustering(clustered_model)

The exported_model and the orig_model have the same structure.

tfmot.clustering.keras.strip_clusteringStay organized with collections Save and categorize content based on your preferences.