mindspore.Tensor.max
- Tensor.max(axis=None, keepdims=False, *, initial=None, where=True, return_indices=False)[source]
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 beFalse
.
- 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 dimensionself.ndim - 1
.- Raises
TypeError – If arguments have types not specified above.
See also
mindspore.Tensor.argmin()
: Return the indices of the minimum values along an axis.mindspore.Tensor.argmax()
: Return the indices of the maximum values along an axis.mindspore.Tensor.min()
: Return the minimum of a tensor or minimum along an axis.
- 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]