torch.amax

torch.amax(input, dim, keepdim=False, *, out=None) → Tensor

Returns the maximum value of each slice of the input tensor in the given dimension(s) dim.

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.

If keepdim is True, the output tensor is of the same size as input except in the dimension(s) dim where it is of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensor having 1 (or len(dim)) fewer dimension(s).

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 (Tensor, optional) – the output tensor.

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[ 0.8177,  1.4878, -0.2491,  0.9130],
        [-0.7158,  1.1775,  2.0992,  0.4817],
        [-0.0053,  0.0164, -1.3738, -0.0507],
        [ 1.9700,  1.1106, -1.0318, -1.0816]])
>>> torch.amax(a, 1)
tensor([1.4878, 2.0992, 0.0164, 1.9700])