How to understand torch.expand_as operation?

a = torch.rand(2, 3)
b = torch.rand(4, 3)
c = b.expand_as(a)
print(c)

a = torch.rand(2, 3)
b = torch.rand(2, 4)
c = b.expand_as(a)
print(c)

I dont know why it does not work. What is the right way to figure it out?

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Hello @ronghui,

From the document,

Expand this tensor to the same size as other . self.expand_as(other) is equivalent to self.expand(other.size()) .

And the .expand() operation involves some broadcasting semantics here. I think you could not “expand” a large size tensor to a smaller one. So the code snippet above will not work.

If I am wrong, please correct me. Thanks.

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So you are saying two tensors must be “broadcastable” before we can apply expand_as on them?

After some try, it is just my own view that the .expand() operation maybe involves broadcasting semantics.

print out a,b will be easy for understanding.
Example 1)
output:

a: tensor([[0.9619, 0.0384, 0.7012],
        [0.5561, 0.3637, 0.9272]])
b: tensor([[0.5986, 0.2582, 0.6261],
        [0.6928, 0.9175, 0.6737],
        [0.9951, 0.8568, 0.6015],
        [0.7922, 0.5019, 0.8162]])

the reason is 1) the size of b is bigger than a’s, you can not expand b by a. 2) the dimension is not match, to output different c, you can size of b to (2, 2, 3) or others. Shown as below,

a = torch.rand(2, 3)
b = torch.rand(2,2, 3)
print('a:',a)
print('b:',b)
c = a.expand_as(b)
print('c:',c)

outputs:

a: tensor([[0.4748, 0.5521, 0.7741],
        [0.0785, 0.2785, 0.5222]])
b: tensor([[[0.7777, 0.3046, 0.8019],
         [0.7398, 0.1424, 0.6398]],

        [[0.9034, 0.8937, 0.8674],
         [0.1737, 0.3192, 0.4451]]])
c: tensor([[[0.4748, 0.5521, 0.7741],
         [0.0785, 0.2785, 0.5222]],

        [[0.4748, 0.5521, 0.7741],
         [0.0785, 0.2785, 0.5222]]])

Example 2) is same problem with example 1.

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