Pytorch tensor shape
I have a simple question regarding the shapes of 2 different tensors - tensor_1 and tensor_2.

tensor_1.shape outputs torch.Size([784, 1]);
tensor_2.shape outputs torch.Size([784]).

I understand that the first one is rank-2 tensor, whereas the second is rank-1. What's hard for me is to conceptualize the difference between shape [784, 1] and [784].
Is it correct to think that tensor_1 has 784 rows and 1 column with a scalar inside each place? If so, why can't we call it simply a column vector (which is, in fact, rank-1 tensor), which also has values displayed vertically?
Similarly, can the shape of the second tensor ([784]) be imagined as 784 values inside a horizontal vector?

You cant call tensor_1 as column vector because of its dimension . indexing that particular tensor is done in 2D
eg . tensor_1[1,1]
Coming to tensor_2 , its a scalar tensor having only one dimension.
And of course you can make it have a shape of tensor_1, just do
tensor_2 = tensor_2.unsqueeze(1)   #This method will make tensor_2 have a shape of tensor_1

