mindspore.Tensor.min

Tensor.min() → Tensor

Returns the minimum value of the self tensor.

Returns

Scalar Tensor with the same dtype as self, the minimum value of the self.

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 = Tensor.min(x)
>>> print(output)
0.0
Tensor.min(dim, keepdim=False)

Calculates the minimum value along with the given dim for the self tensor, and returns the minimum values and indices.

Parameters

  • dim (int) – The dimension to reduce.

  • keepdim (bool, optional) – Whether to reduce 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 minimum value of the self tensor along the given dimension dim and the corresponding index.

  • values (Tensor) - The minimum value of self tensor along the given dimension dim, with the same shape as index, and same dtype as self.

  • index (Tensor) - The index for the minimum 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
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.min(0, keepdim=True)
>>> print(output, index)
[0.0] [0]
Tensor.min(axis=None, keepdims=False, *, initial=None, where=True, return_indices=False) → Tensor | number.Number

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 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.

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 = 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]