tf.keras.utils.unpack_x_y_sample_weight
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Unpacks user-provided data tuple.
tf.keras.utils.unpack_x_y_sample_weight(
data
)
Used in the notebooks
This is a convenience utility to be used when overriding
Model.train_step, Model.test_step, or Model.predict_step.
This utility makes it easy to support data of the form (x,),
(x, y), or (x, y, sample_weight).
Example:
features_batch = ops.ones((10, 5))
labels_batch = ops.zeros((10, 5))
data = (features_batch, labels_batch)
# `y` and `sample_weight` will default to `None` if not provided.
x, y, sample_weight = unpack_x_y_sample_weight(data)
sample_weight is None
True
Args |
|---|
data
|
A tuple of the form (x,), (x, y), or (x, y, sample_weight).
|
Returns |
|---|
The unpacked tuple, with Nones for y and sample_weight if they are
not provided.
|
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Last updated 2024-06-07 UTC.
[null,null,["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.utils.unpack_x_y_sample_weight\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/trainers/data_adapters/data_adapter_utils.py#L10-L50) |\n\nUnpacks user-provided data tuple. \n\n tf.keras.utils.unpack_x_y_sample_weight(\n data\n )\n\n### Used in the notebooks\n\n| Used in the guide |\n|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Integrating MinDiff without MinDiffModel](https://www.tensorflow.org/responsible_ai/model_remediation/min_diff/guide/integrating_min_diff_without_min_diff_model) - [Creating a custom Counterfactual Logit Pairing Dataset](https://www.tensorflow.org/responsible_ai/model_remediation/counterfactual/guide/creating_a_custom_counterfactual_dataset) |\n\nThis is a convenience utility to be used when overriding\n[`Model.train_step`](../../../tf/keras/Model#train_step), [`Model.test_step`](../../../tf/keras/Model#test_step), or [`Model.predict_step`](../../../tf/keras/Model#predict_step).\nThis utility makes it easy to support data of the form `(x,)`,\n`(x, y)`, or `(x, y, sample_weight)`.\n\n#### Example:\n\n features_batch = ops.ones((10, 5))\n labels_batch = ops.zeros((10, 5))\n data = (features_batch, labels_batch)\n # `y` and `sample_weight` will default to `None` if not provided.\n x, y, sample_weight = unpack_x_y_sample_weight(data)\n sample_weight is None\n True\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------|-------------------------------------------------------------------|\n| `data` | A tuple of the form `(x,)`, `(x, y)`, or `(x, y, sample_weight)`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| The unpacked tuple, with `None`s for `y` and `sample_weight` if they are not provided. ||\n\n\u003cbr /\u003e"]]
View source on GitHub