How does the “view” method work in PyTorch?

Question

I am confused about the method view() in the following code snippet.

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__in         


        

Answer 1

Let's try to understand view by the following examples:

    a=torch.range(1,16)

print(a)

    tensor([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13., 14.,
            15., 16.])

print(a.view(-1,2))

    tensor([[ 1.,  2.],
            [ 3.,  4.],
            [ 5.,  6.],
            [ 7.,  8.],
            [ 9., 10.],
            [11., 12.],
            [13., 14.],
            [15., 16.]])

print(a.view(2,-1,4))   #3d tensor

    tensor([[[ 1.,  2.,  3.,  4.],
             [ 5.,  6.,  7.,  8.]],

            [[ 9., 10., 11., 12.],
             [13., 14., 15., 16.]]])
print(a.view(2,-1,2))

    tensor([[[ 1.,  2.],
             [ 3.,  4.],
             [ 5.,  6.],
             [ 7.,  8.]],

            [[ 9., 10.],
             [11., 12.],
             [13., 14.],
             [15., 16.]]])

print(a.view(4,-1,2))

    tensor([[[ 1.,  2.],
             [ 3.,  4.]],

            [[ 5.,  6.],
             [ 7.,  8.]],

            [[ 9., 10.],
             [11., 12.]],

            [[13., 14.],
             [15., 16.]]])

-1 as an argument value is an easy way to compute the value of say x provided we know values of y, z or the other way round in case of 3d and for 2d again an easy way to compute the value of say x provided we know values of y or vice versa..