tfmot.clustering.keras.strip_clustering
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Strips clustering wrappers from the model.
tfmot.clustering.keras.strip_clustering(
model
)
Used in the notebooks
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.
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.
|
Usage:
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.
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Last updated 2023-05-26 UTC.
[null,null,["Last updated 2023-05-26 UTC."],[],[],null,["# tfmot.clustering.keras.strip_clustering\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/model-optimization/blob/v0.7.5/tensorflow_model_optimization/python/core/clustering/keras/cluster.py#L300-L367) |\n\nStrips clustering wrappers from the model. \n\n tfmot.clustering.keras.strip_clustering(\n model\n )\n\n### Used in the notebooks\n\n| Used in the guide |\n|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Weight clustering in Keras example](https://www.tensorflow.org/model_optimization/guide/clustering/clustering_example) - [Cluster preserving quantization aware training (CQAT) Keras example](https://www.tensorflow.org/model_optimization/guide/combine/cqat_example) - [Sparsity and cluster preserving quantization aware training (PCQAT) Keras example](https://www.tensorflow.org/model_optimization/guide/combine/pcqat_example) - [Sparsity preserving clustering Keras example](https://www.tensorflow.org/model_optimization/guide/combine/sparse_clustering_example) |\n\nOnce a model has been clustered, this method can be used\nto restore the original model with the clustered weights.\n\nOnly sequential and functional models are supported for now.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Arguments --------- ||\n|---------|-----------------------------------------------------------------------------------------------------------------|\n| `model` | A [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) instance with clustered layers. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A keras model with clustering wrappers removed. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|-----------------------|---------------------------------------------------------------------------------------------------------------|\n| `ValueError` | if the model is not a [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) instance. |\n| `NotImplementedError` | if the model is a subclass model. |\n\n\u003cbr /\u003e\n\n#### Usage:\n\n orig_model = tf.keras.Model(inputs, outputs)\n clustered_model = cluster_weights(orig_model)\n exported_model = strip_clustering(clustered_model)\n\nThe exported_model and the orig_model have the same structure."]]
View source on GitHub