text: from __future__ import print_function
  import torch
  import torch.nn.functional as F
  import torch.optim as optim
  from torchvision import datasets, transforms
  from cnns.nnlib.utils.exec_args import get_args
  from cnns.nnlib.pytorch_architecture.net import Net
  
  
  def train(args, model, device, train_loader, optimizer, epoch):
      model.train()
      for batch_idx, (data, target) in enumerate(train_loader):
          data, target = data.to(device), target.to(device)
          optimizer.zero_grad()
          output = model(data)
          loss = F.nll_loss(output, target)
          loss.backward()
          optimizer.step()
          if batch_idx % args.log_interval == 0:
              print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                  epoch, batch_idx * len(data), len(train_loader.dataset),
                         100. * batch_idx / len(train_loader), loss.item()))
  
  
  def test(args, model, device, test_loader):
      model.eval()
      test_loss = 0
      correct = 0
      with torch.no_grad():
          for data, target in test_loader:
              data, target = data.to(device), target.to(device)
              output = model(data)
              test_loss += F.nll_loss(output, target,
                                      reduction='sum').item()  # sum up batch loss
              pred = output.argmax(dim=1,
                                   keepdim=True)  # get the index of the max log-probability
              correct += pred.eq(target.view_as(pred)).sum().item()
  
      test_loss /= len(test_loader.dataset)
  
      print(
          '\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
              test_loss, correct, len(test_loader.dataset),
              100. * correct / len(test_loader.dataset)))
  
  
  def main():
      # Training settings
      args = get_args()
  
      use_cuda = not args.use_cuda and torch.cuda.is_available()
  
      torch.manual_seed(args.seed)
  
      device = torch.device("cuda" if use_cuda else "cpu")
  
      kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
      train_loader = torch.utils.data.DataLoader(
          datasets.MNIST('../data', train=True, download=True,
                         transform=transforms.Compose([
                             transforms.ToTensor(),
                             transforms.Normalize((0.1307,), (0.3081,))
                         ])),
          batch_size=args.min_batch_size, shuffle=True, **kwargs)
      test_loader = torch.utils.data.DataLoader(
          datasets.MNIST('../data', train=False, transform=transforms.Compose([
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,))
          ])),
          batch_size=args.test_batch_size, shuffle=True, **kwargs)
  
      model = Net(args).to(device)
      optimizer = optim.SGD(model.parameters(), lr=args.learning_rate,
                            momentum=args.momentum)
  
      for epoch in range(1, args.epochs + 1):
          train(args, model, device, train_loader, optimizer, epoch)
          test(args, model, device, test_loader)
  
      if (args.save_model):
          torch.save(model.state_dict(), "mnist_cnn.pt")
  
  
  if __name__ == '__main__':
      main()
  