python之Pytorch 验证模型错误 : Expected input batch_size (3) to match target batch_size (4)

我正在 Pytorch 中构建一个神经网络，它应该对 102 个类进行分类。

我有以下验证函数:

def validation(model, testloader, criterion): 
    test_loss = 0 
    accuracy = 0 
 
    for inputs, classes in testloader: 
        inputs = inputs.to('cuda') 
        output = model.forward(inputs) 
        test_loss += criterion(output, labels).item() 
 
        ps = torch.exp(output) 
        equality = (labels.data == ps.max(dim=1)[1]) 
        accuracy += equality.type(torch.FloatTensor).mean() 
 
    return test_loss, accuracy 

训练代码(调用验证):

epochs = 3 
print_every = 40 
steps = 0 
running_loss = 0 
testloader = dataloaders['test'] 
 
# change to cuda 
model.to('cuda') 
 
for e in range(epochs): 
    running_loss = 0 
    for ii, (inputs, labels) in enumerate(dataloaders['train']): 
        steps += 1 
 
        inputs, labels = inputs.to('cuda'), labels.to('cuda') 
 
        optimizer.zero_grad() 
 
        # Forward and backward passes 
        outputs = model.forward(inputs) 
        loss = criterion(outputs, labels) 
        loss.backward() 
        optimizer.step() 
 
        running_loss += loss.item() 
 
        if steps % print_every == 0: 
            model.eval() 
            with torch.no_grad(): 
                test_loss, accuracy = validation(model, testloader, criterion) 
 
            print("Epoch: {}/{}.. ".format(e+1, epochs), 
                  "Training Loss: {:.3f}.. ".format(running_loss/print_every), 
                  "Test Loss: {:.3f}.. ".format(test_loss/len(testloader)), 
                  "Test Accuracy: {:.3f}".format(accuracy/len(testloader))) 
 
            running_loss = 0 
            model.train() 

我收到此错误消息:

ValueError: Expected input batch_size (3) to match target batch_size (4). 

完整追溯:

--------------------------------------------------------------------------- 
ValueError                                Traceback (most recent call last) 
<ipython-input-63-f9f67ed13b94> in <module>() 
     28             model.eval() 
     29             with torch.no_grad(): 
---> 30                 test_loss, accuracy = validation(model, testloader, criterion) 
     31  
     32             print("Epoch: {}/{}.. ".format(e+1, epochs), 
 
<ipython-input-62-dbc77acbda5e> in validation(model, testloader, criterion) 
      6         inputs = inputs.to('cuda') 
      7         output = model.forward(inputs) 
----> 8         test_loss += criterion(output, labels).item() 
      9  
     10         ps = torch.exp(output) 
 
/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 
    489             result = self._slow_forward(*input, **kwargs) 
490         else: 
--> 491             result = self.forward(*input, **kwargs) 
    492         for hook in self._forward_hooks.values(): 
    493             hook_result = hook(self, input, result) 
 
/opt/conda/lib/python3.6/site-packages/torch/nn/modules/loss.py in forward(self, input, target) 
    191         _assert_no_grad(target) 
    192         return F.nll_loss(input, target, self.weight, self.size_average, 
--> 193                           self.ignore_index, self.reduce) 
    194  
    195  
 
/opt/conda/lib/python3.6/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce) 
   1328     if input.size(0) != target.size(0): 
   1329         raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).' 
-> 1330                          .format(input.size(0), target.size(0))) 
   1331     if dim == 2: 
   1332         return torch._C._nn.nll_loss(input, target, weight, size_average, ignore_index, reduce) 
 
ValueError: Expected input batch_size (3) to match target batch_size (4). 

我不明白错误从何而来。事实上，没有验证代码，训练部分可以完美运行。

请您参考如下方法:

在你的验证函数中，

def validation(model, testloader, criterion): 
    test_loss = 0 
    accuracy = 0 
 
    for inputs, classes in testloader: 
        inputs = inputs.to('cuda') 
        output = model.forward(inputs) 
        test_loss += criterion(output, labels).item() 
 
        ps = torch.exp(output) 
        equality = (labels.data == ps.max(dim=1)[1]) 
        accuracy += equality.type(torch.FloatTensor).mean() 
 
    return test_loss, accuracy 

您正在迭代测试加载器并将值传递给变量 inputs, classes 但您正在将 labels 传递给您的标准。