Hello ptrblck Thanks for your reply
I just removed the acc from  the train because of the unrealistic result so if there anyway to calc the acc in the train ,…please pass it to me 
def train(epoch):
    model.train()
    correct = 0
    total_train = 0
    correct_train = 0
    for batch_idx, (data, target,length) in enumerate(train_loader):
        
        data = np.expand_dims(data, axis=1)
        data = torch.FloatTensor(data)
        if args.cuda:
            data, target = data.cuda(), target.cuda()
            

        
        data, target = data, target
        optimizer.zero_grad()
        
        output = model(data)
        
        loss = criterion(output,torch.max(target.data, 1)[1])
        loss.backward()
        optimizer.step()
        total_train += target.nelement()
        train_accuracy = 100 * correct_train / total_train
        pred = output.data.max(
            1, keepdim=True)[1]  # get the index of the max log-probability
        correct += (pred.cpu() == torch.max(target.data, 1)[1].cpu()).sum()
        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():
    model.eval()
    test_loss = 0
    correct = 0
    total =0 
    for data, target ,length in validation_loader:
        
        data = np.expand_dims(data, axis=1)
        data = torch.FloatTensor(data)
        #print(target.size)
        
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = data, target
        
        output = model(data)
        
        test_loss += criterion(output, torch.max(target.data, 1)[1])
        
        
        
        pred = output.data.max(
            1, keepdim=True)[1]  # get the index of the max log-probability
        
        
        correct += (pred.cpu() == torch.max(target.data, 1)[1].cpu()).sum()

    print(
        '\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss.item(), correct, len(validation_loader.dataset),
            100. * correct / len(validation_loader.dataset)))
    test_loss = 0

  
    
for epoch in range(1, args.epochs + 1):
    train(epoch)
    test()