Hi folks, I’d like some help on indexing a 2D tensor. Suppose we have the following tensors
d = torch.tensor([[118, 175,   1],  [118, 188,   0], [ 66, 201,   1], [ 94, 204,   1],  [ 94, 206,   0]]) e = torch.tensor([[66, 201, 0.1], [94, 206, 0.2], [1, 23, 0.6],                 [118, 188, 0.3],                 [2, 3, 0.1],                 [3, 1, 0.2],                 [94, 204, 0.8],                 [118, 175, 0.7]])
Tensor e is a set of predictions from a model where the first 2 columns are sort of an ID and tensor d is a set of labels with first 2 columns as an ID. So I have less labels than predictions here and I’d like to only calculate the loss for the real labels. Hence, I need to index tensor e by tensor d's first 2 columns. Is there a way to do this? Or would using a 3D tensor be better?