RuntimeError: The size of tensor a (20) must match the size of tensor b (3) at non-singleton dimension 2

Hi, I’m having a trouble with the training data have the x_train shape of (7304,20) and y_train of (7304,3). While loading through the model, while calculating the loss between labeled and trained_data, I have above error. Here is the further detail of the code:

num_epochs = 100
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
for epoch in range(num_epochs):
    for i, batch in enumerate(train_loader):
        x = batch["sample"].to(device)
        rul = batch["target"].to(device)
        x = x.unsqueeze(1)
        output = model(x)
        loss = criterion(output,rul)
        #Backward and Optimize

And the full error lines:

RuntimeError                              Traceback (most recent call last)
Cell In[70], line 12
     10 x = x.unsqueeze(1)
     11 output = model(x)
---> 12 loss = criterion(output,rul)
     14 #Backward and Optimize
     15 #optimizer.zero_grad()
     16 #loss.backward()
     17 #optimizer.step()
     19 #print("Epoch{}/{}.Iter{}/{}.Loss{}").format(epoch+1,num_epochs,iter,num_iters,loss)

File ~/.local/lib/python3.10/site-packages/torch/nn/modules/, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

File ~/.local/lib/python3.10/site-packages/torch/nn/modules/, in MSELoss.forward(self, input, target)
    535 def forward(self, input: Tensor, target: Tensor) -> Tensor:
--> 536     return F.mse_loss(input, target, reduction=self.reduction)

File ~/.local/lib/python3.10/site-packages/torch/nn/, in mse_loss(input, target, size_average, reduce, reduction)
   3291 if size_average is not None or reduce is not None:
   3292     reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 3294 expanded_input, expanded_target = torch.broadcast_tensors(input, target)
   3295 return torch._C._nn.mse_loss(expanded_input, expanded_target, _Reduction.get_enum(reduction))

File ~/.local/lib/python3.10/site-packages/torch/, in broadcast_tensors(*tensors)
     72 if has_torch_function(tensors):
     73     return handle_torch_function(broadcast_tensors, tensors, *tensors)
---> 74 return _VF.broadcast_tensors(tensors)

RuntimeError: The size of tensor a (20) must match the size of tensor b (3) at non-singleton dimension 2

I know the error occurs due to the shape of the x and y, but how can I fix this problem ?

The error is raised in nn.MSELoss which expects the output and target tensors to have the same shape as the loss will be computed elementwise.
You would thus need to check why your model doesn’t create outputs matching the target shape and might need to change the model architecture.