mindspore.mint.maximum

mindspore.mint.maximum(input, other)[source]

Computes the maximum of input tensors element-wise.

Note

  • Inputs of input and other comply with the implicit type conversion rules to make the data types consistent.

  • The input must be two Tensors, or a Tensor and a Scalar.

  • When the inputs are two tensors, dtypes of them cannot be bool at the same time, and the shapes of them could be broadcast.

  • When the inputs are one tensor and one scalar, the scalar could only be a constant.

  • Broadcasting is supported.

  • If one of the elements being compared is a NaN, then that element is returned.

\[output_i = \max(input_i, other_i)\]
Parameters

  • input (Union[Tensor, Number, bool]) – The first input is a number or a bool or a tensor whose data type is number or bool.

  • other (Union[Tensor, Number, bool]) – The second input is a number or a bool when the first input is a tensor or a tensor whose data type is number or bool.

Returns

Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.

Raises

  • TypeError – If input and other is not one of the following: Tensor, Number, bool.

  • ValueError – If input and other are not the same shape.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> # case 1 : same data type
>>> input = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32)
>>> other = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32)
>>> output = mint.maximum(input, other)
>>> print(output)
[4. 5. 6.]
>>> # case 2 : different data type
>>> input = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.int32)
>>> other = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32)
>>> output = mint.maximum(input, other)
>>> print(output.dtype)
Float32