tf.math.in_top_k

Outputs whether the targets are in the top K predictions.

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. predictions does not have to be normalized.

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.

target = tf.constant([0, 1, 3])
pred = tf.constant([
 [1.2, -0.3, 2.8, 5.2],
 [0.1, 0.0, 0.0, 0.0],
 [0.0, 0.5, 0.3, 0.3]],
 dtype=tf.float32)
print(tf.math.in_top_k(target, pred, 2))
tf.Tensor([False  True  True], shape=(3,), dtype=bool)

targets A batch_size vector of class ids. Must be int32 or int64.
predictions A batch_size x classes tensor of type float32.
k An int. The parameter to specify search space.
name A name for the operation (optional).

A Tensor with the same shape of targets with type of bool. Each element specifies if the target falls into top-k predictions.