Hi everyone, i have a problem about loss function. I have 2 tensors and x=[23177,1,3] and y=[23177,1]. They are my special datasets. So when I turn them into a dataloader:

```
train_dataset = TensorDataset(train_x, train_y)
train_generator = DataLoader(train_dataset, batch_size=100,shuffle=False)
```

and my criterion and optimizer:

```
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
```

and my nn class is:

```
class Modele(nn.Module):
def __init__(self, D_in, H1, H2, D_out):
super().__init__()
self.linear1 = nn.Linear(D_in, H1)
self.linear2 = nn.Linear(H1, H2)
self.linear3 = nn.Linear(H2, D_out)
def forward(self, x):
x = F.relu(self.linear1(x))
x = F.relu(self.linear2(x))
x = self.linear3(x)
return x
```

and my computation loop:

epochs=15

for e in range(epochs):

```
running_loss = 0.0
running_corrects = 0.0
val_running_loss = 0.0
val_running_corrects = 0.0
for inputs,out in train_generator:
loss = criterion(model(inputs), out)
#_,preds=torch.max(outputs,1)
#outputss.append(preds.max().detach().numpy())
losses.append(loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
#outputss.append(outputs.detach().numpy())
#print(loss.item())
```

And my loss function graph:

and I got this error

C:\Users\gun\AppData\Local\Continuum\anaconda3\lib\site-packages\torch\nn\modules\loss.py:431: UserWarning: Using a target size (torch.Size([100, 1])) that is different to the input size (torch.Size([100, 1, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.

return F.mse_loss(input, target, reduction=self.reduction)

C:\Users\gun\AppData\Local\Continuum\anaconda3\lib\site-packages\torch\nn\modules\loss.py:431: UserWarning: Using a target size (torch.Size([12, 1])) that is different to the input size (torch.Size([12, 1, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have

the same size.

return F.mse_loss(input, target, reduction=self.reduction)

How can I solve this problem?