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Showing content from https://docs.pytorch.org/docs/stable/generated/torch.max.html below:

torch.max — PyTorch 2.7 documentation

torch.max(input) → Tensor¶

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

Parameters

input (Tensor) – the input tensor.

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 0.6763,  0.7445, -2.2369]])
>>> torch.max(a)
tensor(0.7445)

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

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

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 maximal values in a reduced row then the indices of the first maximal value are returned.

Parameters

• input (Tensor) – the input tensor.

• dim (int or tuple of ints, optional) – the dimension or dimensions to reduce. If None, all dimensions are reduced.

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

Keyword Arguments

out (tuple, optional) – the result tuple of two output tensors (max, max_indices)

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[-1.2360, -0.2942, -0.1222,  0.8475],
        [ 1.1949, -1.1127, -2.2379, -0.6702],
        [ 1.5717, -0.9207,  0.1297, -1.8768],
        [-0.6172,  1.0036, -0.6060, -0.2432]])
>>> torch.max(a, 1)
torch.return_types.max(values=tensor([0.8475, 1.1949, 1.5717, 1.0036]), indices=tensor([3, 0, 0, 1]))
>>> a = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
>>> a.max(dim=1, keepdim=True)
torch.return_types.max(
values=tensor([[2.], [4.]]),
indices=tensor([[1], [1]]))
>>> a.max(dim=1, keepdim=False)
torch.return_types.max(
values=tensor([2., 4.]),
indices=tensor([1, 1]))

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

See torch.maximum().

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