mindspore.ops.minimum

mindspore.ops.minimum(x, y)[source]

Computes the minimum of input tensors element-wise.

Note

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

  • The inputs must be two tensors or one tensor and one scalar.

  • 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.

\[output_i = \min(x_i, y_i)\]
Parameters

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

  • y (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 x and y is not one of the following: Tensor, Number, bool.

  • ValueError – If x and y are not the same shape after broadcast.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> # case 1 : same data type
>>> x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32)
>>> y = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32)
>>> output = ops.minimum(x, y)
>>> print(output)
[1. 2. 3.]
>>> # case 2 : different data type
>>> x = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.int32)
>>> y = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32)
>>> output = ops.minimum(x, y)
>>> print(output.dtype)
Float32