'ValueError: Target size (torch.Size([16])) must be the same as input size (torch.Size([16, 1]))
ValueError Traceback (most recent call last)
<ipython-input-30-33821ccddf5f> in <module>
23 output = model(data)
24 # calculate the batch loss
---> 25 loss = criterion(output, target)
26 # backward pass: compute gradient of the loss with respect to model parameters
27 loss.backward()
C:\Users\mnauf\Anaconda3\envs\federated_learning\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
487 result = self._slow_forward(*input, **kwargs)
488 else:
--> 489 result = self.forward(*input, **kwargs)
490 for hook in self._forward_hooks.values():
491 hook_result = hook(self, input, result)
C:\Users\mnauf\Anaconda3\envs\federated_learning\lib\site-packages\torch\nn\modules\loss.py in forward(self, input, target)
593 self.weight,
594 pos_weight=self.pos_weight,
--> 595 reduction=self.reduction)
596
597
C:\Users\mnauf\Anaconda3\envs\federated_learning\lib\site-packages\torch\nn\functional.py in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight)
2073
2074 if not (target.size() == input.size()):
-> 2075 raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
2076
2077 return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)
ValueError: Target size (torch.Size([16])) must be the same as input size (torch.Size([16, 1]))
I am training a CNN. Working on the Horses vs humans dataset. This is my code. I am using criterion = nn.BCEWithLogitsLoss()
and optimizer = optim.RMSprop(model.parameters(), lr=0.01
). My final layer is self.fc2 = nn.Linear(512, 1)
. Out last neuron, will output 1 for horse and 0 for human, right? or should I choose 2 neurons for output?
16
is the batch size. Since the error says ValueError: Target size (torch.Size([16])) must be the same as input size (torch.Size([16, 1]))
. I don't understand, where do I need to make change, to rectify the error.
Solution 1:[1]
target = target.unsqueeze(1)
, before passing target to criterion, changed the target tensor size from [16]
to [16,1]
. Doing it solved the issue. Furthermore, I also needed to do target = target.float()
before passing it to criterion, because our outputs are in float. Besides, there was another error in the code. I was using sigmoid activation function in the last layer, but I shouldn’t because the criterion I am using already comes with sigmoid builtin.
Solution 2:[2]
You can also try _, pred = torch.max(output, 1)
and then pass the pred
variable into Loss function.
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
Solution | Source |
---|---|
Solution 1 | Muhammad Naufil |
Solution 2 | AyanG_WAP7 |