need more context on this :
It is really important for you to commit to memory and practice these bits of tensor jargon: rank is the number of axes or dimensions in a tensor; shape is the size of each axis of a tensor.
Watch out because the term “dimension” is sometimes used in two ways. Consider that we live in “three-dimensonal space” where a physical position can be described by a 3-vector
v. But according to PyTorch, the attribute
v.ndim (which sure looks like the “number of dimensions” of
v) equals one, not three! Why? Because
v is a vector, which is a tensor of rank one, meaning that it has only one axis (even if that axis has a length of three). In other words, sometimes dimension is used for the size of an axis (“space is three-dimensional”); other times, it is used for the rank, or the number of axes (“a matrix has two dimensions”).
so ‘v’ is a 3 vector entity . but according to Pytorch v.dim = 1 not 3 because v is a tensor of rank 1 . but the definition of a rank of a tensor is “number of axes or dimensions in a tensor” which in this case happens to be 3 . so how is v.dim a tensor of rank 1 and not a tensor of rank 3 .
its confusing .