Alternative to broadcasting

1

I am a bit confused about the rules of broadcasting the shapes.

Is there any safer alternative? For example, if I have a matrix of dimension p times q. How can I duplicate it r times and make it a tensor of shape p times q times r?

EDIT: specifically, the confusion I had is the following:

I have the following piece of code

print(reshape_q.size())
print(mean_p.size())
diff = -1.0*mean_p + reshape_q

but it gives the me the following error
torch.Size([700, 100, 1])
torch.Size([700, 100, 3])
Traceback (most recent call last):
File “main.py”, line 238, in
train()
File “main.py”, line 161, in train
loss, first, kld = loss_function(decoded, targets, mean_output, logvar_output, z)
File “main.py”, line 230, in loss_function
KLD = KL_G_vs_GMM(mu.view(shape), logvar.view(shape), z.view(shape), reference_means)
File “main.py”, line 209, in KL_G_vs_GMM
diff = -1.0*mean_p + reshape_q
File “/public/apps/anaconda3/4.3.1/lib/python3.6/site-packages/torch/autograd/variable.py”, line 745, in add
return self.add(other)
File “/public/apps/anaconda3/4.3.1/lib/python3.6/site-packages/torch/autograd/variable.py”, line 283, in add
return self._add(other, False)
File “/public/apps/anaconda3/4.3.1/lib/python3.6/site-packages/torch/autograd/variable.py”, line 277, in _add
return Add(inplace)(self, other)
File “/public/apps/anaconda3/4.3.1/lib/python3.6/site-packages/torch/autograd/_functions/basic_ops.py”, line 20, in forward
return a.add(b)
RuntimeError: sizes do not match at /py/conda-bld/pytorch_1493680494901/work/torch/lib/THC/generated/…/generic/THCTensorMathPointwise.cu:216

2

What is your PyTorch version?

I guess that broadcasting is supported from version 0.2.0.

3

broadcasting only expands leading dimensions (not trailing dimensions)

What you can do is this:

p.unsqueeze(-1).expand_as(q)

4

@Tengyu_MA 's tensors have same number of dimensions though… So broadcasting in this case should be supported. (and it is indeed at least on master).

5

Yeah, that might be the issue! because I tried a synthetic case and it worked!

so the reason should be that I used the wrong version