tfl.pwl_calibration_layer.LaplacianRegularizer
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Laplacian regularizer for PWL calibration layer.
tfl.pwl_calibration_layer.LaplacianRegularizer(
l1=0.0, l2=0.0, is_cyclic=False
)
Calibrator Laplacian regularization penalizes the change in the calibration
output. It is defined to be:
l1 * ||delta||_1 + l2 * ||delta||_2^2
where delta is:
output_keypoints[1:end] - output_keypoints[0:end-1].
Args |
|---|
l1
|
l1 regularization amount as float.
|
l2
|
l2 regularization amount as float.
|
is_cyclic
|
Whether the first and last keypoints should take the same
output value.
|
Methods
from_config
@classmethod
from_config(
config
)
Creates a regularizer from its config.
This method is the reverse of get_config,
capable of instantiating the same regularizer from the config
dictionary.
This method is used by TF-Keras model_to_estimator, saving and
loading models to HDF5 formats, TF-Keras model cloning, some
visualization utilities, and exporting models to and from JSON.
| Args |
|---|
config
|
A Python dictionary, typically the output of get_config.
|
| Returns |
|---|
|
A regularizer instance.
|
get_config
View source
get_config()
Standard Keras config for serialization.
__call__
View source
__call__(
x
)
Returns regularization loss.
| Args |
|---|
x
|
Tensor of shape: (k, units) which represents weights of PWL
calibration layer. First row of weights is bias term. All remaining
represent delta in y-value compare to previous point (segment heights).
|
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Last updated 2024-08-02 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-08-02 UTC."],[],[],null,["# tfl.pwl_calibration_layer.LaplacianRegularizer\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/lattice/blob/v2.1.1/tensorflow_lattice/python/pwl_calibration_layer.py#L815-L879) |\n\nLaplacian regularizer for PWL calibration layer. \n\n tfl.pwl_calibration_layer.LaplacianRegularizer(\n l1=0.0, l2=0.0, is_cyclic=False\n )\n\nCalibrator Laplacian regularization penalizes the change in the calibration\noutput. It is defined to be:\n\n`l1 * ||delta||_1 + l2 * ||delta||_2^2`\n\nwhere `delta` is:\n\n`output_keypoints[1:end] - output_keypoints[0:end-1]`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------|-------------------------------------------------------------------------|\n| `l1` | l1 regularization amount as float. |\n| `l2` | l2 regularization amount as float. |\n| `is_cyclic` | Whether the first and last keypoints should take the same output value. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `from_config`\n\n @classmethod\n from_config(\n config\n )\n\nCreates a regularizer from its config.\n\nThis method is the reverse of `get_config`,\ncapable of instantiating the same regularizer from the config\ndictionary.\n\nThis method is used by TF-Keras `model_to_estimator`, saving and\nloading models to HDF5 formats, TF-Keras model cloning, some\nvisualization utilities, and exporting models to and from JSON.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|----------|----------------------------------------------------------|\n| `config` | A Python dictionary, typically the output of get_config. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A regularizer instance. ||\n\n\u003cbr /\u003e\n\n### `get_config`\n\n[View source](https://github.com/tensorflow/lattice/blob/v2.1.1/tensorflow_lattice/python/pwl_calibration_layer.py#L873-L879) \n\n get_config()\n\nStandard Keras config for serialization.\n\n### `__call__`\n\n[View source](https://github.com/tensorflow/lattice/blob/v2.1.1/tensorflow_lattice/python/pwl_calibration_layer.py#L845-L871) \n\n __call__(\n x\n )\n\nReturns regularization loss.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|-----|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `x` | Tensor of shape: `(k, units)` which represents weights of PWL calibration layer. First row of weights is bias term. All remaining represent delta in y-value compare to previous point (segment heights). |\n\n\u003cbr /\u003e"]]
View source on GitHub