CODEWITHSUNDEEP
pythonpytorch
peer
peer

Reputation: 4679

How to multiply a tensor with a vector?

I have 2 tensors 

a = torch.tensor([1,2])
b = torch.tensor([[[10,20],
                   [30,40]],
                  [[1,2],
                   [3,4]]])

and I would like to combine them in such a way that 

a ? b = tensor([[[10,20],
                 [30,40]],
                [[ 2, 4],
                 [ 6, 8]]])

(and then sum over the 0th dimension, in the end I want to do a weighted sum)

I've tried:

""" no idea how to interpret that """
a @ b                                                                                                                                                                                                
tensor([[ 70, 100],
        [  7,  10]])

b @ a                                                                                                                                                                                                
tensor([[ 50, 110],
        [  5,  11]])

for i in range(b.size()[0]): # works but I don't think this will work with autograd 
      b[i] *= a[i] 

a * b              # multiplies right side by 2
tensor([[[10, 40],
         [30, 80]],

        [[ 1,  4],
         [ 3,  8]]])

a.unsqueeze(1)     # multiplies bottom side by 2
tensor([[[10, 20],
         [60, 80]],

        [[ 1,  2],
         [ 6,  8]]])

a.unsqueeze(2) * b # dimension out of range

Upvotes: 0

Views: 779

Answers (3)

Anubhav Singh
Anubhav Singh

Reputation: 8699

You can also try below code:

a = torch.tensor([1,2])
b = torch.tensor([[[10,20],
                   [30,40]],
                  [[1,2],
                   [3,4]]])

print((a.view(-1, 1)*torch.flatten(b, 1)).view(b.shape))

output:

tensor([[[10, 20],
         [30, 40]],

        [[ 2,  4],
         [ 6,  8]]])

Here, we are basically carrying out below steps:

  • Reshaping a to a 2d tensor of size [a.shape[0],1], i.e. [2, 1] in above case.
  • Then, we are using torch.flatten() to flatten tensor b starting from the first dimension(i.e. start_dim=1). Here, end_dim=-1 by default. Resultant size is [2, 4].
  • Performing element-wise multiplication.
  • Finally, reshaping the result to shape same as original tensor b, i.e [2, 2, 2].

Upvotes: 1

asymptote
asymptote

Reputation: 1402

This should workc = a.unsqueeze(1).unsqueeze(1) * b

Upvotes: 2

thedch
thedch

Reputation: 177

Interesting -- I tried a few different broadcasting tricks and didn't see any obvious wins, so the simple version:

b[0] *= a[0]
b[1] *= a[1]
c = b

Upvotes: 0

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