text: from __future__ import print_function
  import argparse
  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_attention import ConvAddAttention
  import time
  
  
  class Net(nn.Module):
      def __init__(self,max_type,layer_norm_flag,lam,gamma):
          super(Net, self).__init__()
          self.conv1 = ConvAddAttention(1, 20, 5, 1, max_type, layer_norm_flag, lam, gamma)
          self.conv2 = ConvAddAttention(20, 50, 5, 1, max_type, layer_norm_flag, lam, gamma)
          self.fc1 = nn.Linear(4*4*50, 500)
          self.fc2 = nn.Linear(500, 10)
  
      def forward(self, x):
          x = F.relu(self.conv1(x))
          x = F.max_pool2d(x, 2, 2)
          x = F.relu(self.conv2(x))
          x = F.max_pool2d(x, 2, 2)
          x = x.view(-1, 4*4*50)
          x = F.relu(self.fc1(x))
          x = self.fc2(x)
          return F.log_softmax(x, dim=1)
  
  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.max(1, keepdim=True)[1] # 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)))
  
      return correct / len(test_loader.dataset)
  
  def main(args = None):
      # Training settings
      if args is None:
          parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
          parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                              help='input batch size for training (default: 64)')
          parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                              help='input batch size for testing (default: 1000)')
          parser.add_argument('--epochs', type=int, default=10, metavar='N',
                              help='number of epochs to train (default: 10)')
          parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                              help='learning rate (default: 0.01)')
          parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                          help='SGD momentum (default: 0.5)')
          parser.add_argument('--no-cuda', action='store_true', default=False,
                              help='disables CUDA training')
          parser.add_argument('--seed', type=int, default=1, metavar='S',
                              help='random seed (default: 1)')
          parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                              help='how many batches to wait before logging training status')
  
          parser.add_argument('--save-model', action='store_true', default=False,
                              help='For Saving the current Model')
  
          # added by tanghao 20181217
          parser.add_argument('--norm-flag', type=bool, default=False,
                              help='Triggering the Layer Normalization flag for attention scores')
          parser.add_argument('--gamma', type=float, default=None,
                              help='Controlling the sparisty of gfusedmax/sparsemax, the smaller, the more sparse')
          parser.add_argument('--lam', type=float, default=1.0,
                              help='Lambda: Controlling the smoothness of gfusedmax, the larger, the smoother')
          parser.add_argument('--max-type', type=str, default='softmax',choices=['softmax','sparsemax','gfusedmax'],
                              help='mapping function in attention')
          parser.add_argument('--optim-type', type=str, default='SGD',choices=['SGD','Adam'],
                              help='mapping function in attention')
  
          args = parser.parse_args()
  
      print(args)
      args.seed = time.time()
      use_cuda = not args.no_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.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.max_type,args.norm_flag,args.lam,args.gamma).to(device)
      if args.optim_type == 'Adam':
          optimizer = optim.Adam(model.parameters(), lr=args.lr)
      else:
          optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
  
      for epoch in range(1, args.epochs + 1):
          train(args, model, device, train_loader, optimizer, epoch)
          test_acc = test(args, model, device, test_loader)
  
      if (args.save_model):
          torch.save(model.state_dict(),"mnist_%2.1f_%.4f_%s_%s_%d_%.2f_%.2f.pt"%(test_acc*100,args.lr,args.optim_type,args.max_type,args.norm_flag,args.lam,0.123 if args.gamma is None else args.gamma))
  
      return test_acc
  
  if __name__ == '__main__':
      main()
  