mindspore.ops.min

mindspore.ops.min(x, axis=0, keep_dims=False)[source]

Calculates the minimum value along with the given axis for the input tensor. It returns the minimum values and indices.

Note

In auto_parallel and semi_auto_parallel mode, the first output index can not be used.

Warning

If there are multiple minimum values, the index of the first minimum value is used.
The value range of “axis” is [-dims, dims - 1]. “dims” is the dimension length of “x”.

Parameters

x (Tensor) – \((x_1, x_2, ..., x_N)\) .Complex tensor is not supported.
axis (int) – The dimension to reduce. Default: 0.
keep_dims (bool) – Whether to reduce dimension, if true the output will keep the same dimension as the input, the output will reduce dimension if false. Default: False.

Returns

tuple (Tensor), tuple of 2 tensors, containing the corresponding index and the minimum value of the input tensor.

index (Tensor) - The index for the minimum value of the input tensor, with dtype int32. If keep_dims is true, the shape of output tensors is \((x_1, x_2, ..., x_{axis-1}, 1, x_{axis+1}, ..., x_N)\). Otherwise, the shape is \((x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)\) .
values (Tensor) - The minimum value of input tensor, with the same shape as index, and same dtype as x.

Raises

TypeError – If x is not Tensor.
TypeError – If keep_dims is not a bool.
TypeError – If axis is not an int.

Supported Platforms:: Ascend GPU CPU

Examples

>>> x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> index, output = ops.min(x)
>>> print(index, output)
0 0.0
>>> index, output = ops.min(x, keep_dims=True)
>>> print(index, output)
[0] [0.0]