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Showing content from https://docs.pytorch.org/docs/main/generated/torch.min.html below:

torch.min — PyTorch main documentation

Returns the minimum value of all elements in the input tensor.

Note

The difference between max/min and amax/amin is:

• amax/amin supports reducing on multiple dimensions,

• amax/amin does not return indices.

Both amax/amin evenly distribute gradients between equal values when there are multiple input elements with the same minimum or maximum value.

For max/min:

• If reduce over all dimensions(no dim specified), gradients evenly distribute between equally max/min values.

• If reduce over one specified axis, only propagate to the indexed element.

Parameters

input (Tensor) – the input tensor.

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 0.6750,  1.0857,  1.7197]])
>>> torch.min(a)
tensor(0.6750)

torch.min(input, dim, keepdim=False, *, out=None)

Returns a namedtuple (values, indices) where values is the minimum value of each row of the input tensor in the given dimension dim. And indices is the index location of each minimum value found (argmin).

If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input.

Note

If there are multiple minimal values in a reduced row then the indices of the first minimal value are returned.

Parameters

• input (Tensor) – the input tensor.

• dim (int, optional) – the dimension to reduce. If omitted, all dimensions are reduced. Explicit None is not supported.

• keepdim (bool, optional) – whether the output tensor has dim retained or not. Default: False.

Keyword Arguments

out (tuple, optional) – the tuple of two output tensors (min, min_indices)

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[-0.6248,  1.1334, -1.1899, -0.2803],
        [-1.4644, -0.2635, -0.3651,  0.6134],
        [ 0.2457,  0.0384,  1.0128,  0.7015],
        [-0.1153,  2.9849,  2.1458,  0.5788]])
>>> torch.min(a, 1)
torch.return_types.min(values=tensor([-1.1899, -1.4644,  0.0384, -0.1153]), indices=tensor([2, 0, 1, 0]))

torch.min(input, other, *, out=None) → Tensor

See torch.minimum().

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