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mindspore.ops.max

mindspore.ops.max(input, axis=None, keepdims=False, *, initial=None, where=None)[source]

Calculates the maximum value along with the given axis for the input tensor. It returns the maximum values and indices.

Note

In auto_parallel and semi_auto_parallel mode, the first output index can not be used.
When axis is None, keepdims and subsequent parameters have no effect. At the same time, the index is fixed to return 0.

Warning

If there are multiple maximum values, the index of the first maximum value is used.
The value range of "axis" is [-dims, dims - 1]. "dims" is the dimension length of "input".

Also see: mindspore.ops.ArgMaxWithValue.

Parameters

input (Tensor) – The input tensor, can be any dimension. Complex tensor is not supported for now.
axis (int) – The dimension to reduce. When axis is None, computing the maximum value of all elements in input .Default: None .
keepdims (bool) – Whether to reduce dimension, if true, the output will keep same dimension with the input, the output will reduce dimension if false. Default: False .

Keyword Arguments

initial (scalar, optional) – The minimum value of an output element. Must be present to allow computation on empty slice. Default: None .
where (Tensor[bool], optional) – A Tensor indicating whether to replace the primitive value in input with the value in initial. If True , do not replace, otherwise replace. For the index of True in where, the corresponding value in initial must be assigned. Default: None , which indicates True by default.

Returns

tuple (Tensor), tuple of 2 tensors, containing the corresponding index and the maximum value of the input tensor.

values (Tensor) - The maximum value of input tensor, with the same shape as index, and same dtype as x.
index (Tensor) - The index for the maximum value of the input tensor, with dtype int64. If keepdims is true, the shape of output tensors is $(i n p u t_{1}, i n p u t_{2}, . . ., i n p u t_{a x i s - 1}, 1, i n p u t_{a x i s + 1}, . . ., i n p u t_{N})$ . Otherwise, the shape is $(i n p u t_{1}, i n p u t_{2}, . . ., i n p u t_{a x i s - 1}, i n p u t_{a x i s + 1}, . . ., i n p u t_{N})$ .

Raises

TypeError – If input is not Tensor.
TypeError – If keepdims is not a bool.
TypeError – If axis is not an int.
TypeError – If initial is not a number.

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)
>>> output, index = ops.max(x)
>>> print(output, index)
0.7 0
>>> 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 = ops.max(y, axis=0, keepdims=True)
>>> print(output, index)
[[3.2 0.4 0.4 2.9 4. ]] [[1 1 0 1 1]]