tf.raw_ops.UpperBound
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Applies upper_bound(sorted_search_values, values) along each row.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
tf.compat.v1.raw_ops.UpperBound
tf.raw_ops.UpperBound(
sorted_inputs,
values,
out_type=tf.dtypes.int32,
name=None
)
Each set of rows with the same index in (sorted_inputs, values) is treated
independently. The resulting row is the equivalent of calling
np.searchsorted(sorted_inputs, values, side='right').
The result is not a global index to the entire
Tensor, but rather just the index in the last dimension.
A 2-D example:
sorted_sequence = [[0, 3, 9, 9, 10],
[1, 2, 3, 4, 5]]
values = [[2, 4, 9],
[0, 2, 6]]
result = UpperBound(sorted_sequence, values)
result == [[1, 2, 4],
[0, 2, 5]]
Args |
|---|
sorted_inputs
|
A Tensor. 2-D Tensor where each row is ordered.
|
values
|
A Tensor. Must have the same type as sorted_inputs.
2-D Tensor with the same numbers of rows as sorted_search_values. Contains
the values that will be searched for in sorted_search_values.
|
out_type
|
An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type out_type.
|
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Last updated 2024-04-26 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.UpperBound\n\n\u003cbr /\u003e\n\nApplies upper_bound(sorted_search_values, values) along each row.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.UpperBound`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/UpperBound)\n\n\u003cbr /\u003e\n\n tf.raw_ops.UpperBound(\n sorted_inputs,\n values,\n out_type=../../tf/dtypes#int32,\n name=None\n )\n\nEach set of rows with the same index in (sorted_inputs, values) is treated\nindependently. The resulting row is the equivalent of calling\n`np.searchsorted(sorted_inputs, values, side='right')`.\n\nThe result is not a global index to the entire\n`Tensor`, but rather just the index in the last dimension.\n\nA 2-D example:\nsorted_sequence = \\[\\[0, 3, 9, 9, 10\\],\n\\[1, 2, 3, 4, 5\\]\\]\nvalues = \\[\\[2, 4, 9\\],\n\\[0, 2, 6\\]\\]\n\nresult = UpperBound(sorted_sequence, values)\n\nresult == \\[\\[1, 2, 4\\],\n\\[0, 2, 5\\]\\]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `sorted_inputs` | A `Tensor`. 2-D Tensor where each row is ordered. |\n| `values` | A `Tensor`. Must have the same type as `sorted_inputs`. 2-D Tensor with the same numbers of rows as `sorted_search_values`. Contains the values that will be searched for in `sorted_search_values`. |\n| `out_type` | An optional [`tf.DType`](../../tf/dtypes/DType) from: `tf.int32, tf.int64`. Defaults to [`tf.int32`](../../tf#int32). |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` of type `out_type`. ||\n\n\u003cbr /\u003e"]]
