# tf.compat.v1.math.in_top_k

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/nn_ops.py#L6527-L6559) |

Says whether the targets are in the top `K` predictions.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.nn.in_top_k`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/math/in_top_k)

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    tf.compat.v1.math.in_top_k(
        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 finite (not inf, -inf, or nan) and 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)\\\]

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| ## Args ||
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| `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). |

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| ## Returns ||
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| A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`. ||

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