mindspore.mint.amin

mindspore.mint.amin(input, dim=(), keepdim=False)[source]

Compute the minimum value of all elements along the specified dimension.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

input (Tensor) – The input tensor.
dim (Union[int, tuple(int), list(int)], optional) – Specify the dimension for computation, when the dim is (), all dimensions are reduced. Default ().
keepdim (bool, optional) – Whether the output tensor retains the dimension dim. Default False.

Returns

Tensor

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> x = mindspore.tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = mindspore.mint.amin(x, 1, keepdim=True)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # case 1: Reduce a dimension by the minimum value of all elements in the dimension.
>>> x = mindspore.tensor(np.array([[[1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8], [9, 9, 9, 9, 9, 9]]]),
... mindspore.float32)
>>> output = mindspore.mint.amin(x)
>>> print(output)
1.0
>>> print(output.shape)
()
>>> # case 2: Reduce a dimension along axis 0.
>>> output = mindspore.mint.amin(x, 0, True)
>>> print(output)
[[[1. 1. 1. 1. 1. 1.]
  [2. 2. 2. 2. 2. 2.]
  [3. 3. 3. 3. 3. 3.]]]
>>> # case 3: Reduce a dimension along axis 1.
>>> output = mindspore.mint.amin(x, 1, True)
>>> print(output)
[[[1. 1. 1. 1. 1. 1.]]

 [[4. 4. 4. 4. 4. 4.]]

 [[7. 7. 7. 7. 7. 7.]]]
>>> # case 4: Reduce a dimension along axis 2.
>>> output = mindspore.mint.amin(x, 2, True)
>>> print(output)
[[[1.]
  [2.]
  [3.]]

 [[4.]
  [5.]
  [6.]]

 [[7.]
  [8.]
  [9.]]]