mindspore.Tensor.min

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

Return the minimum of a tensor or minimum along an axis.

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 minimum 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 tensor. Default: False.

  • initial (scalar, optional) – The maximum 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 tensor, and selects elements to include in the reduction. If non-default value is passed, initial must also be provided. Default: True.

Returns

Tensor or scalar, minimum of input tensor. If the 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 maximum of a tensor or maximum along an axis.

Examples

>>> from mindspore import Tensor
>>> import mindspore.numpy as np
>>> a = Tensor(np.arange(4).reshape((2,2)).astype('float32'))
>>> output = a.min()
>>> print(output)
0.0