mindspore.Tensor.min

Tensor.min(input) → Tensor

Calculates the minimum value of the input tensor.

Also see mindspore.ops.extend.min().

Return the minimum of a tensor or minimum 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) – An axis or axes along which to operate. By default, flattened input is used. If axis is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before. Default: None .
keepdims (bool, optional) – If 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 (Tensor[bool], 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 minimum value. Default: False . If axis is a list or tuple of ints, it must be False .

Returns

Tensor or scalar, minimum 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

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.max(): 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 = Tensor.min(a)
>>> print(output)
0.0
>>> output = Tensor.min(a, axis=0)
>>> print(output)
[0. 1.]
>>> output = Tensor.min(a, axis=0, initial=9, where=Tensor([False]))
>>> print(output)
[9. 9.]
>>> output = Tensor.min(a, axis=0, initial=9, where=Tensor([False, True]))
>>> print(output)
[9. 1.]
>>> value, indices = Tensor.min(a, axis=0, return_indices=True)
>>> print(value)
[0. 1.]
>>> print(indices)
[0 0]