mindspore.mint.maximum

mindspore.mint.maximum(input, other)

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

$$outpu{t}_i=max(inpu{t}_i,othe{r}_i)$$

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 Tensor of int32, while in PyNative mode, the output will be 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])