mindspore.ops.ReduceMin

class mindspore.ops.ReduceMin(keep_dims=False)[源代码]

Reduces a dimension of a tensor by the minimum value in the dimension, by default. And also can reduce a dimension of x along the axis. Determine whether the dimensions of the output and input are the same by controlling keep_dims.

Parameters

keep_dims (bool) – If true, keep these reduced dimensions and the length is 1. If false, don’t keep these dimensions. Default : False.

Inputs:

  • x (Tensor[Number]) - The input tensor. The dtype of the tensor to be reduced is number. \((N,*)\) where \(*\) means, any number of additional dimensions, its rank should be less than 8.

  • axis (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions. Only constant value is allowed. Must be in the range [-rank(x), rank(x)).

Outputs:

Tensor, has the same dtype as the x.

  • If axis is (), and keep_dims is False, the output is a 0-D tensor representing the minimum of all elements in the input tensor.

  • If axis is int, set as 2, and keep_dims is False, the shape of output is \((x_1, x_3, ..., x_R)\).

  • If axis is tuple(int), set as (2, 3), and keep_dims is False, the shape of output is \((x_1, x_4, ..., x_R)\).

Raises

  • TypeError – If keep_dims is not a bool.

  • TypeError – If x is not a Tensor.

  • TypeError – If axis is not one of the following: int, tuple or list.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = ops.ReduceMin(keep_dims=True)
>>> output = op(x, 1)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # case 1: Reduces a dimension by the minimum value of all elements in the dimension.
>>> x = 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 = op(x)
>>> print(output)
[[[1.]]]
>>> print(output.shape)
(1, 1, 1)
>>> # case 2: Reduces a dimension along axis 0.
>>> output = op(x, 0)
>>> print(output)
[[[1. 1. 1. 1. 1. 1.]
  [2. 2. 2. 2. 2. 2.]
  [3. 3. 3. 3. 3. 3.]]]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = op(x, 1)
>>> print(output)
[[[1. 1. 1. 1. 1. 1.]]
 [[4. 4. 4. 4. 4. 4.]]
 [[7. 7. 7. 7. 7. 7.]]]
>>> # case 4: Reduces a dimension along axis 2.
>>> output = op(x, 2)
>>> print(output)
[[[1.]
  [2.]
  [3.]]
 [[4.]
  [5.]
  [6.]]
 [[7.]
  [8.]
  [9.]]]