tf.nn.in_top_k(predictions, targets, k, name=None)

tf.nn.in_top_k(predictions, targets, k, name=None)

See the guide: Neural Network > Evaluation

Says whether the targets are in the top K predictions.

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,

$$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$


  • 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).


A Tensor of type bool. Computed Precision at k as a bool Tensor.

Defined in tensorflow/python/ops/