mindspore.Tensor.maximum

Tensor.maximum(other) → Tensor

Computes the maximum of input tensors element-wise.

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

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.

Warning

If all inputs are of scalar int type, the output is a Tensor of type int32 in GRAPH mode and a Tensor of type int64 in PYNATIVE mode.

Parameters

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

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.maximum(y)
>>> print(output)
[4. 5. 6.]
>>> # 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.maximum(y)
>>> print(output.dtype)
Float32