tft.histogram
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Computes a histogram over x, given the bin boundaries or bin count.
tft.histogram(
x: common_types.TensorType,
boundaries: Optional[Union[tf.Tensor, int]] = None,
categorical: Optional[bool] = False,
name: Optional[str] = None
) -> Tuple[tf.Tensor, tf.Tensor]
Ex (1):
counts, boundaries = histogram([0, 1, 0, 1, 0, 3, 0, 1], range(5))
counts: [4, 3, 0, 1, 0]
boundaries: [0, 1, 2, 3, 4]
Ex (2):
Can be used to compute class weights.
counts, classes = histogram([0, 1, 0, 1, 0, 3, 0, 1], categorical=True)
probabilities = counts / tf.reduce_sum(counts)
class_weights = dict(map(lambda (a, b): (a.numpy(), 1.0 / b.numpy()),
zip(classes, probabilities)))
Args |
|---|
x
|
A Tensor, SparseTensor, or RaggedTensor.
|
boundaries
|
(Optional) A Tensor or int used to build the histogram;
ignored if categorical is True. If possible, provide boundaries as
multiple sorted values. Default to 10 intervals over the 0-1 range, or
find the min/max if an int is provided (not recommended because
multi-phase analysis is inefficient).
|
categorical
|
(Optional) A bool that treats x as discrete values if true.
|
name
|
(Optional) A name for this operation.
|
Returns |
|---|
counts
|
The histogram, as counts per bin.
|
boundaries
|
A Tensor used to build the histogram representing boundaries.
|
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Last updated 2024-11-01 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-11-01 UTC."],[],[],null,["# tft.histogram\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/transform/blob/v1.16.0/tensorflow_transform/analyzers.py#L677-L739) |\n\nComputes a histogram over x, given the bin boundaries or bin count. \n\n tft.histogram(\n x: common_types.TensorType,\n boundaries: Optional[Union[tf.Tensor, int]] = None,\n categorical: Optional[bool] = False,\n name: Optional[str] = None\n ) -\u003e Tuple[tf.Tensor, tf.Tensor]\n\nEx (1):\ncounts, boundaries = histogram(\\[0, 1, 0, 1, 0, 3, 0, 1\\], range(5))\ncounts: \\[4, 3, 0, 1, 0\\]\nboundaries: \\[0, 1, 2, 3, 4\\]\n\nEx (2):\nCan be used to compute class weights.\ncounts, classes = histogram(\\[0, 1, 0, 1, 0, 3, 0, 1\\], categorical=True)\nprobabilities = counts / tf.reduce_sum(counts)\nclass_weights = dict(map(lambda (a, b): (a.numpy(), 1.0 / b.numpy()),\nzip(classes, probabilities)))\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `x` | A `Tensor`, `SparseTensor`, or `RaggedTensor`. |\n| `boundaries` | (Optional) A `Tensor` or `int` used to build the histogram; ignored if `categorical` is True. If possible, provide boundaries as multiple sorted values. Default to 10 intervals over the 0-1 range, or find the min/max if an int is provided (not recommended because multi-phase analysis is inefficient). |\n| `categorical` | (Optional) A `bool` that treats `x` as discrete values if true. |\n| `name` | (Optional) A name for this operation. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|--------------|-----------------------------------------------------------------|\n| `counts` | The histogram, as counts per bin. |\n| `boundaries` | A `Tensor` used to build the histogram representing boundaries. |\n\n\u003cbr /\u003e"]]
View source on GitHub