tf.math.in_top_k
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Says whether the targets are in the top K predictions.

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Main aliases

tf.nn.in_top_k

tf.math.in_top_k(
    targets, predictions, 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,

o u t_{i} = p r e d i c t i o n s_{i, t a r g e t s_{i}} \in T o p K I n c l u d i n g T i e s (p r e d i c t i o n s_{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`. Computed Precision at `k` as a `bool Tensor`.

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tf.math.in_top_k
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