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mindspore.mint.minimum

mindspore.mint.minimum(input, other)

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

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

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.

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

• Shapes of them are supposed to be broadcast.

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

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.minimum(input, other)
Tensor(shape=[3], dtype=Float32, value= [ 1.00000000e+00,  2.00000000e+00,  3.00000000e+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.minimum(input, other)
Tensor(shape=[3], dtype=Float64, value= [ 1.00000000e+00,  2.00000000e+00,  3.00000000e+00])