tfmot.sparsity.keras.strip_pruning
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Strip pruning wrappers from the model.
tfmot.sparsity.keras.strip_pruning(
model
)
Used in the notebooks
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.
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.
|
Usage:
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.
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Last updated 2023-05-26 UTC.
[null,null,["Last updated 2023-05-26 UTC."],[],[],null,["# tfmot.sparsity.keras.strip_pruning\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/sparsity/keras/prune.py#L222-L270) |\n\nStrip pruning wrappers from the model. \n\n tfmot.sparsity.keras.strip_pruning(\n model\n )\n\n### Used in the notebooks\n\n| Used in the guide |\n|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Sparsity and cluster preserving quantization aware training (PCQAT) Keras example](https://www.tensorflow.org/model_optimization/guide/combine/pcqat_example) - [Pruning preserving quantization aware training (PQAT) Keras example](https://www.tensorflow.org/model_optimization/guide/combine/pqat_example) - [Sparsity preserving clustering Keras example](https://www.tensorflow.org/model_optimization/guide/combine/sparse_clustering_example) - [Pruning comprehensive guide](https://www.tensorflow.org/model_optimization/guide/pruning/comprehensive_guide) - [Pruning for on-device inference w/ XNNPACK](https://www.tensorflow.org/model_optimization/guide/pruning/pruning_for_on_device_inference) |\n\nOnce a model has been pruned to required sparsity, this method can be used\nto restore the original model with the sparse 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 pruned 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 pruning 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 pruned_model = prune_low_magnitude(orig_model)\n exported_model = strip_pruning(pruned_model)\n\nThe exported_model and the orig_model share the same structure."]]
View source on GitHub