mindspore.mint.max

mindspore.mint.max(input, dim=None, keepdim=False)[source]

Calculates the maximum value along with the given dimension for the input tensor.

Parameters

input (Tensor) – The input tensor, can be any dimension. Complex tensor is not supported for now.
dim (int, optional) – The dimension to reduce. Default: None .
keepdim (bool, optional) – Whether to reduce dimension, if true, the output will keep same dimension with the input, the output will reduce dimension if false. Default: False .

Returns

Tensor if dim is the default value None , the maximum value of input tensor, with the shape \(()\) , and same dtype as input.

tuple (Tensor) if dim is not the default value None , tuple of 2 tensors, containing the maximum value of the input tensor along the given dimension dim and the corresponding index.

values (Tensor) - The maximum value of input tensor along the given dimension dim, with same dtype as input. If keepdim is True , the shape of output tensors is \((input_1, input_2, ..., input_{axis-1}, 1, input_{axis+1}, ..., input_N)\) . Otherwise, the shape is \((input_1, input_2, ..., input_{axis-1}, input_{axis+1}, ..., input_N)\) .
index (Tensor) - The index for the maximum value of the input tensor along the given dimension dim, with the same shape as values.

Raises

ValueError – If dim is the default value None and keepdim is not False .

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> y = Tensor(np.array([[0.0, 0.3, 0.4, 0.5, 0.1],
...                      [3.2, 0.4, 0.1, 2.9, 4.0]]), mindspore.float32)
>>> output, index = mint.max(y, 0, True)
>>> print(output, index)
[[3.2 0.4 0.4 2.9 4. ]] [[1 1 0 1 1]]