mindspore.Tensor.max

Tensor.max() → Tensor

Returns the maximum value of the self tensor.

Returns: Scalar Tensor with the same dtype as input, the maximum value of the input.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> output = x.max()
>>> print(output)
[0.7]

Tensor.max(dim, keepdim=False)

Calculates the maximum value along with the given dim for the input tensor, and returns the maximum values and indices.

Parameters

dim (int) – The dimension to reduce.
keepdim (bool, optional) – Whether to keep dimension, if True the output will keep the same dimension as the self , the output will reduce dimension if False. Default: False.

Returns

tuple (Tensor), tuple of 2 tensors, containing the maximum value of the self tensor along the given dimension dim and the corresponding index.

values (Tensor) - The maximum value of self tensor, with the same shape as index, and same dtype as self.
index (Tensor) - The index for the maximum value of the self tensor, with dtype int64. If keepdim is True , the shape of output tensors is \((self_1, self_2, ..., self_{dim-1}, 1, self_{dim+1}, ..., self_N)\). Otherwise, the shape is \((self_1, self_2, ..., self_{dim-1}, self_{dim+1}, ..., self_N)\) .

Raises

TypeError – If keepdim is not a bool.
TypeError – If dim is not an int.
TypeError – If self tensor data type is Complex.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> output, index = x.max(0, keepdim=True)
>>> print(output, index)
[0.7] [3]

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 self 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.