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

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.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.