mindspore.Tensor.max

Tensor.max(axis=None, keepdims=False, *, initial=None, where=True, return_indices=False)

Return the maximum of a tensor or maximum along an axis.

Note

When axis is None, keepdims and subsequent parameters have no effect. At the same time, the index is fixed to return 0.

Parameters

  • axis (Union[None, int, list, tuple of ints], optional) – Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before. Default: None .

  • keepdims (bool, optional) – If this is set to True , the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. 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 (bool Tensor, optional) – A boolean tensor which is broadcasted to match the dimensions of array, and selects elements to include in the reduction. If non-default value is passed, initial must also be provided. Default: True .

  • return_indices (bool, optional) – Whether to return the index of the maximum value. Default: False . If axis is a list or tuple of ints, it must be False .

Returns

Tensor or scalar, maximum of input tensor. If axis is None , the result is a scalar value. If axis is given, the result is a tensor of dimension self.ndim - 1.

Raises

TypeError – If arguments have types not specified above.

See also

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> a = Tensor(np.arange(4).reshape((2, 2)).astype('float32'))
>>> output = a.max()
>>> print(output)
3.0
>>> value, indices = a.max(axis=0, return_indices=True)
>>> print(value)
[2. 3.]
>>> print(indices)
[1 1]