ValueError:目标大小(torch.Size([16]))必须与输入大小(torch.Size([16，1]))相同 [英] ValueError: Target size (torch.Size([16])) must be the same as input size (torch.Size([16, 1]))

问题描述

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]))

我正在训练CNN.研究马与人"数据集.这是我的代码.我正在使用 criterion = nn.BCEWithLogitsLoss()和 optimizer = optim.RMSprop(model.parameters()，lr = 0.01 ).我的最后一层是 self.fc2 = nn.Linear(512，1).最后一个神经元，对马输出1，对人输出0，对吗?还是应该选择2个神经元进行输出?

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 是批处理大小.由于错误显示 ValueError:目标大小(torch.Size([16]))必须与输入大小(torch.Size([16，1]))相同.我不明白，我需要在哪里进行更改以纠正错误.

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.

推荐答案

target = target.unsqueeze(1)，在将目标传递给标准之前，将目标张量的大小从 [16]更改了] 到 [16,1] .这样做解决了这个问题.此外，在将其传递到标准之前，我还需要先执行 target = target.float()，因为我们的输出为float形式.此外，代码中还有另一个错误.我在最后一层使用了Sigmoid激活函数，但我不应该这样做，因为我所使用的标准已经内置了Sigmoid.

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.

这篇关于ValueError:目标大小(torch.Size([16]))必须与输入大小(torch.Size([16，1]))相同的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！