# Could I use that nn.Criterion module compute combined tensor?

For example, I have 6 tensors with different dimension:

tensor11 size: (b, 64, 224, 224)
tensor21 size: (b, 128, 112, 112)
tensor31 size: (b, 256, 56, 56)

tensor12 size: (b, 64, 224, 224)
tensor22 size: (b, 128, 112, 112)
tensor32 size: (b, 256, 56, 56)

Could I get the tensors which tensor1 has the size (tensor11 size, tensor12 size, tensor13 size), tensor2 has the size(tensor12 size, tensor22 size, tensor 32 size).
If possible, how could I use nn.MSELoss() to computer the loss between elements between these 2 tensor on same positions and get these 3 losses between (tensor11, tensor12), (tensor21, tensor22) and (tensor31, tensor32) by one step while these losses will be utilised in BP?
Thanks.

Hi, maybe you could flatten the tensors and concat them? Use

`flatten1 = tensor11.view(1, -1)` and `torch.cat(flatten1, flatten2, flatten3)`

Would it work if you’d just calculate the 3 losses separately and then add them?

``````mse = nn.MSELoss()
loss1 = mse(tensor1l, tensor12)
loss2 = mse(tensor2l, tensor22)
loss3 = mse(tensor3l, tensor32)
total_loss = loss1 + loss2 + loss3
# backpropagate
``````

Thanks very much. I calculate 3 losses respectively and concatenate them by () now. It’s weird that the loss can be used in back propagation in this way. I mean python always seems the element in () as A tuple in my computer 1 Like

The wonders of Pytorch 