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mindspore.ops.aminmax

mindspore.ops.aminmax(input, *, axis=0, keepdims=False)[source]

It returns the minimum and maximum value along the given axis of input tensor.

Parameters

input (Tensor) – The input tensor, can be any dimension. Set the shape of input tensor as $(x_{1}, x_{2}, . . ., x_{N})$ .

Keyword Arguments

axis (int, optional) – The dimension to reduce. The value range of axis is [-rank, rank), where "rank" is the dimension of input. If axis is None, computes the minimum and maximum value along the entire input tensor. Default: 0 .
keepdims (bool, optional) – Whether to maintain dimension. When set to True, the output will keep the same dimension as the input, or the dimension specified by axis is reduced. Default: False .

Returns

tuple (Tensor), containing the minimum value and maximum value of the input tensor.

If keepdims is True, the shape of output tensors is $(x_{1}, x_{2}, . . ., x_{a x i s - 1}, 1, x_{a x i s + 1}, . . ., x_{N})$ .
If keepdims is False, the shape of output tensors is $(x_{1}, x_{2}, . . ., x_{a x i s - 1}, x_{a x i s + 1}, . . ., x_{N})$ .

Raises

TypeError – If keepdims is not a bool.
TypeError – If axis is not an int and not None.
ValueError – If axis is not in range [-rank, rank).

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> output0, output1 = ops.aminmax(x)
>>> print(output0, output1)
0.0 0.7
>>> output2, output3 = ops.aminmax(x, axis=-1, keepdims=True)
>>> print(output2, output3)
[0.] [0.7]
>>> x = Tensor(np.array([[0.0, 0.4, 0.6, 0.7, 0.1], [0.78, 0.97, 0.5, 0.82, 0.99]]), mindspore.float32)
>>> output4, output5 = ops.aminmax(x, axis=None, keepdims=True)
>>> print(output4, output5)
[[0.]] [[0.99]]