tf.raw_ops.InTopK
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Says whether the targets are in the top K predictions.
tf.raw_ops.InTopK(
predictions, targets, k, name=None
)
This outputs a batch_size bool array, an entry out[i] is true if the
prediction for the target class is among the top k predictions among
all predictions for example i. Note that the behavior of InTopK differs
from the TopK op in its handling of ties; if multiple classes have the
same prediction value and straddle the top-k boundary, all of those
classes are considered to be in the top k.
More formally, let
\(predictions_i\) be the predictions for all classes for example i,
\(targets_i\) be the target class for example i,
\(out_i\) be the output for example i,
\[out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)\]
Args |
|---|
predictions
|
A Tensor of type float32.
A batch_size x classes tensor.
|
targets
|
A Tensor. Must be one of the following types: int32, int64.
A batch_size vector of class ids.
|
k
|
An int. Number of top elements to look at for computing precision.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type bool.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.InTopK\n\nSays whether the targets are in the top `K` predictions.\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.InTopK`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/InTopK)\n\n\u003cbr /\u003e\n\n tf.raw_ops.InTopK(\n predictions, targets, k, name=None\n )\n\nThis outputs a `batch_size` bool array, an entry `out[i]` is `true` if the\nprediction for the target class is among the top `k` predictions among\nall predictions for example `i`. Note that the behavior of `InTopK` differs\nfrom the `TopK` op in its handling of ties; if multiple classes have the\nsame prediction value and straddle the top-`k` boundary, all of those\nclasses are considered to be in the top `k`.\n\nMore formally, let\n\n\\\\(predictions_i\\\\) be the predictions for all classes for example `i`,\n\\\\(targets_i\\\\) be the target class for example `i`,\n\\\\(out_i\\\\) be the output for example `i`,\n\n\\\\\\[out_i = predictions_{i, targets_i} \\\\in TopKIncludingTies(predictions_i)\\\\\\]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|-------------------------------------------------------------------------------------------------------|\n| `predictions` | A `Tensor` of type `float32`. A `batch_size` x `classes` tensor. |\n| `targets` | A `Tensor`. Must be one of the following types: `int32`, `int64`. A `batch_size` vector of class ids. |\n| `k` | An `int`. Number of top elements to look at for computing precision. |\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 `bool`. ||\n\n\u003cbr /\u003e"]]
