mindspore.Tensor.minimum
- Tensor.minimum(other) Tensor
Computes the minimum of input tensors element-wise.
\[output_i = \min(tensor_i, other_i)\]Note
self and other comply with the implicit type conversion rules to make the data types consistent.
The other can be a tensor or a scalar.
When other is a tensor, dtypes of self and other cannot be bool at the same time, and the shapes of them could be broadcast.
When other is a 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.
- Parameters
other (Union[Tensor, Number, bool]) – The input is a number or a bool 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 self and other.
- Raises
TypeError – If other is not one of the following: Tensor, Number, bool.
ValueError – If self and other are not the same shape after broadcast.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor >>> # 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 = x.minimum(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 = x.minimum(y) >>> print(output.dtype) Float32