In the PyTorch Seq2Seq tutorial 4, the embedding and hidden context vectors were forces into a (1, 1, -1) shape.
Is there a reason for the (1, 1, -1) shape? Why do we need a 1x1x* 3D vector to keep a single vector in the inner most tensor?
Why can’t we just use a (-1) 1-dimensional vector?