mindspore.mint.maximum

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

Compute the maximum of the two input tensors element-wise.

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

Note

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

  • 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.

Warning

If all inputs are scalar of integers. In Graph mode, the output will be a Tensor of int32, while in PyNative mode, the output will be a Tensor of int64.

Parameters

  • input (Union[Tensor, Number, bool]) – The first input.

  • other (Union[Tensor, Number, bool]) – The second input.

Returns

Tensor

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> # case 1: same data type
>>> input = mindspore.tensor([1.0, 5.0, 3.0], mindspore.float32)
>>> other = mindspore.tensor([4.0, 2.0, 6.0], mindspore.float32)
>>> mindspore.mint.maximum(input, other)
Tensor(shape=[3], dtype=Float32, value= [ 4.00000000e+00,  5.00000000e+00,  6.00000000e+00])
>>>
>>> # case 2: the data type is the one with higher precision or higher digits among the two inputs.
>>> input = mindspore.tensor([1.0, 5.0, 3.0], mindspore.int64)
>>> other = mindspore.tensor([4.0, 2.0, 6.0], mindspore.float64)
>>> mindspore.mint.maximum(input, other)
Tensor(shape=[3], dtype=Float64, value= [ 4.00000000e+00,  5.00000000e+00,  6.00000000e+00])