I am getting this error ("RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument target in method wrapper__nll_loss2d_forward)") while running the following code

username: Lohith_Kumaar
cooked: <p>def train_model(model, loss_fn, optimizer, scheduler, num_epochs=25):<br>
since= time.time()</p>
<pre><code>best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0

for epoch in range(num_epochs):
    print('Epoch {}/{}'.format(epoch, num_epochs -1))
    print('-' * 10)
    
    for phase in ['train', 'val']:
        if phase == 'train':
            model.train()
        else:
            model.eval()
            
        running_loss = 0
        running_corrects = 0
        
        for inputs, labels in dataloaders[phase]:
            inputs = inputs.to(device)
            labels = labels.to(device)
            
            optimizer.zero_grad()
            
            **with torch.set_grad_enabled(phase == 'train'):**
</code></pre>
<p>**                    probabilities = inputs**<br>
**                    _, predicions = torch.max(probabilities, 1)**<br>
**                    loss = loss_fn(probabilities, output)**<br>
if phase == ‘train’:<br>
loss.backward()<br>
optimizer.step()</p>
<pre><code>            running_loss += loss.item()*input.size(0)
            running_corrects += torch.sum(preds == output.data)
            if phase == 'train':
                  scheduler.step()
            epoch_loss = running_loss / data_size[phase]
            epoch_acc = running_corrects.double() / data_size[phase]
            
            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
            
            if phase == 'val' and epoch_acc &gt; best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
        print()
        
    time_elapsed = time.time() -since
    print('Training complete in {:.Of}m {:.Of}s'.format(time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))
    
    model.load_state_dict(best_model_wts)
    return model
</code></pre>
<p>I am getting error at the above mentioned lines</p>

username: ptrblck
cooked: <p><code>output</code> is undefined in your code snippet so I guess you might have initialized it globally on the CPU before?</p>

username: Lohith_Kumaar
cooked: <p>NO, I have now changed it now and<br>
def train_model(model, loss_fn, optimizer, lr_scheduler, num_epochs=25):<br>
since= time.time()</p>
<pre><code>best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0

for epoch in range(num_epochs):
    print('Epoch {}/{}'.format(epoch, num_epochs -1))
    print('-' * 10)
    
    for phase in ['train', 'val']:
        if phase == 'train':
            model.train()
        else:
            model.eval()
            
        running_loss = 0
        running_corrects = 0
        
        for inputs, outputs in dataloaders[phase]:
            inputs = inputs.to(device)
            outputs = outputs.to(device)
            
            optimizer.zero_grad()
            
            with torch.set_grad_enabled(phase == 'train'):
                **probabilities = inputs**
                _,predicions = torch.max(probabilities, 1)
                loss = loss_fn(probabilities, outputs)
                if phase == 'train':
                    loss.backward()
                    optimizer.step()
                  
            running_loss += loss.items()*input.size(0)
            running_corrects += torch.sum(predictions == output.data)
            if phase == 'train':
                lr_scheduler.step()
            epoch_loss = running_loss / data_size[phase]
            epoch_acc = running_corrects.double() / data_size[phase]
            
            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
            
            if phase == 'val' and epoch_acc &gt; best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
        print()
        
    time_elapsed = time.time() -since
    print('Training complete in {:.Of}m {:.Of}s'.format(time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))
    
    model.load_state_dict(best_model_wts)
    return model
</code></pre>
<p>now I removed the model in the bold lines and now the error is</p>
<p>“RuntimeError: only batches of spatial targets supported (3D tensors) but got targets of size: : [4]”</p>