mindspore.Tensor.min

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Tensor.min(axis=None, keepdims=False, *, initial=None, where=True, return_indices=False)[source]

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.

Supported Platforms:

Ascend GPU CPU

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.

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> a = Tensor(np.arange(4).reshape((2, 2)).astype('float32'))
>>> output = a.min()
>>> print(output)
0.0
>>> output = a.min(axis=0)
>>> print(output)
[0. 1.]
>>> output = a.min(axis=0, initial=9, where=Tensor([False]))
>>> print(output)
[9. 9.]
>>> output = a.min(axis=0, initial=9, where=Tensor([False, True]))
>>> print(output)
[9. 1.]
>>> value, indices = a.min(axis=0, return_indices=True)
>>> print(value)
[0. 1.]
>>> print(indices)
[0 0]