Oh, yes I posted the wrong code, sorry!
Here is the real code:
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable

kwargs = {}
train_data = torch.utils.data.DataLoader(datasets.MNIST('data',train=True, download=True,
                                                        transform=transforms.Compose([transforms.ToTensor(),
                                                                                      transforms.Normalize((0.1307,),
                                                                                                           (0.3081,))])),
                                         batch_size=64, shuffle=True, **kwargs)
test_data = torch.utils.data.DataLoader(datasets.MNIST('data', train=False,
                                                       transform=transforms.Compose([transforms.ToTensor(),
                                                                                     transforms.Normalize((0.1307,),
                                                                                                          (0.3081,))])),
                                        batch_size=64, shuffle=True, **kwargs)

class Netz(nn.Module):
    def __init__(self):
        super(Netz, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv_dropout = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 60)
        self.fc2 = nn.Linear(60, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.max_pool2d(x, 2)
        x = F.relu(x)
        x = self.conv2(x)
        x = self.conv_dropout(x)
        x = F.max_pool2d(x, 2)
        x = F.relu(x)
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x)

model = Netz()

optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.8)
def train(epoch):
    model.train()
    for batch_id, (data, target) in enumerate(train_data):
        data = Variable(data)
        target = Variable(target)
        optimizer.zero_grad()
        out = model(data)
        criterion = F.nll_loss
        loss = criterion(out, target)
        loss.backward()
        optimizer.step()
        print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, batch_id * len(data),
                                                                       len(train_data.dataset),
                                                                       100. * batch_id / len(train_data),
                                                                       loss.data[0]))


for epoch in range(1, 30):
    train(epoch)


Sorry for my dumbness