mindspore.mint.min

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mindspore.mint.min(input, dim=None, keepdim=False)[source]

Calculates the minimum value along with the given dimension for the input tensor.

Parameters

  • input (Tensor) – The input tensor, can be any dimension. Complex tensor is not supported for now.

  • dim (int, optional) – The dimension to reduce. Default: None .

  • keepdim (bool, optional) – Whether to reduce dimension, if true, the output will keep same dimension with the input, the output will reduce dimension if false. Default: False .

Returns

Tensor if dim is the default value None , the minimum value of input tensor, with the shape \(()\) , and same dtype as input.

tuple (Tensor) if dim is not the default value None , tuple of 2 tensors, containing the minimum value of the input tensor along the given dimension dim and the corresponding index.

  • values (Tensor) - The minimum value of input tensor along the given dimension dim, with same dtype as input. If keepdim is True , the shape of output tensors is \((input_1, input_2, ..., input_{axis-1}, 1, input_{axis+1}, ..., input_N)\) . Otherwise, the shape is \((input_1, input_2, ..., input_{axis-1}, input_{axis+1}, ..., input_N)\) .

  • index (Tensor) - The index for the minimum value of the input tensor along the given dimension dim, with the same shape as values.

Raises

ValueError – If dim is the default value None and keepdim is not False .

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> from mindspore.ops.function.array_func import min_ext
>>> x = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]), mindspore.float32)
>>> output, index = min_ext(x, 0, keepdim=True)
>>> print(output, index)
[0.0] [0]