mindspore.ops.InTopK

class mindspore.ops.InTopK(*args, **kwargs)[source]

Determines whether the targets are in the top k predictions.

Parameters

k (int) – Specifies the number of top elements to be used for computing precision.

Inputs:

  • x1 (Tensor) - A 2D Tensor defines the predictions of a batch of samples with float16 or float32 data type.

  • x2 (Tensor) - A 1D Tensor defines the labels of a batch of samples with int32 data type. The size of x2 must be equal to x1’s first dimension. The values of x2 can not be negative and must be equal to or less than index of x1’s second dimension.

Outputs:

Tensor has 1 dimension of type bool and the same shape with x2. For labeling sample i in x2, if the label in the first k predictions for sample i is in x1, then the value is True, otherwise False.

Raises

  • TypeError – If k is not an int.

  • TypeError – If x1 or x2 is not a Tensor.

  • TypeError – If dtype of x1 is neither float16 nor float32.

Supported Platforms:

Ascend

Examples

>>> x1 = Tensor(np.array([[1, 8, 5, 2, 7], [4, 9, 1, 3, 5]]), mindspore.float32)
>>> x2 = Tensor(np.array([1, 3]), mindspore.int32)
>>> in_top_k = ops.InTopK(3)
>>> output = in_top_k(x1, x2)
>>> print(output)
[ True  False]