# tf.math.in_top_k

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Says whether the targets are in the top K predictions.

### Aliases:

• tf.compat.v2.math.in_top_k
• tf.compat.v2.nn.in_top_k
• 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.

$$predictions_i$$ 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,

$$outi = predictions{i, targets_i} \in TopKIncludingTies(predictions_i)$$

#### 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.