请问该如何提高res18结构的SNN在cifar10数据集上的准确率呢？

目前我训练的模型在测试集上的准确率只有(71.49%),在训练集上的准确率比较高，在 97%左右。看起来好像有些过拟合，但是改怎样进行调参来优化模型的准确率呢? 跪求大神帮帮。请问有可以参考的训练模型吗？现在的训练参数如下： T = 100， momentum = 0.9, lr = 1e-3, tau =2.0 代码如下：
net = spiking_resnet.spiking_resnet18(pretrained=False, spiking_neuron=neuron.IFNode, surrogate_function=surrogate.ATan(), detach_reset=True)

    print(net)
    net.to(args.device)

    train_dataset = torchvision.datasets.CIFAR10(
        root=args.data_dir,
        train=True,
        transform=torchvision.transforms.ToTensor(),
        download=True
    )
    test_dataset = torchvision.datasets.CIFAR10(
        root=args.data_dir,
        train=False,
        transform=torchvision.transforms.ToTensor(),
        download=True
    )

    train_data_loader = data.DataLoader(
        dataset=train_dataset,
        batch_size=args.b,
        shuffle=True,
        drop_last=True,
        num_workers=args.j,
        pin_memory=True
    )
    test_data_loader = data.DataLoader(
        dataset=test_dataset,
        batch_size=args.b,
        shuffle=False,
        drop_last=False,
        num_workers=args.j,
        pin_memory=True
    )

    scaler = None
    if args.amp:
        scaler = amp.GradScaler()

    start_epoch = 0
    max_test_acc = -1

    optimizer = None
    if args.opt == 'sgd':
        optimizer = torch.optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum)
    elif args.opt == 'adam':
        optimizer = torch.optim.Adam(net.parameters(), lr=args.lr)
    else:
        raise NotImplementedError(args.opt)

    # if args.resume:
    #     checkpoint = torch.load(args.resume, map_location='cpu')
    #     net.load_state_dict(checkpoint['net'])
    #     optimizer.load_state_dict(checkpoint['optimizer'])
    #     start_epoch = checkpoint['epoch'] + 1
    #     max_test_acc = checkpoint['max_test_acc']

    out_dir = os.path.join(args.out_dir, f'T{args.T}_b{args.b}_{args.opt}_lr{args.lr}')

    if args.amp:
        out_dir += '_amp'

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)
        print(f'Mkdir {out_dir}.')

    with open(os.path.join(out_dir, 'args.txt'), 'w', encoding='utf-8') as args_txt:
        args_txt.write(str(args))

    writer = SummaryWriter(out_dir, purge_step=start_epoch)
    with open(os.path.join(out_dir, 'args.txt'), 'w', encoding='utf-8') as args_txt:
        args_txt.write(str(args))
        args_txt.write('\n')
        args_txt.write(' '.join(sys.argv))

    encoder = encoding.PoissonEncoder()

    for epoch in range(start_epoch, args.epochs):
        start_time = time.time()
        net.train()
        train_loss = 0
        train_acc = 0
        train_samples = 0
        for img, label in train_data_loader:
            optimizer.zero_grad()
            img = img.to(args.device)
            label = label.to(args.device)
            label_onehot = F.one_hot(label, 10).float()

            if scaler is not None:
                with amp.autocast():
                    out_fr = 0.
                    for t in range(args.T):
                        encoded_img = encoder(img)
                        out_fr += net(encoded_img)
                    out_fr = out_fr / args.T
                    loss = F.mse_loss(out_fr, label_onehot)
                scaler.scale(loss).backward()
                scaler.step(optimizer)
                scaler.update()
            else:
                out_fr = 0.
                for t in range(args.T):
                    encoded_img = encoder(img)
                    out_fr += net(encoded_img)
                out_fr = out_fr / args.T
                loss = F.mse_loss(out_fr, label_onehot)
                loss.backward()
                optimizer.step()

            train_samples += label.numel()
            train_loss += loss.item() * label.numel()
            train_acc += (out_fr.argmax(1) == label).float().sum().item()

            functional.reset_net(net)

        train_time = time.time()
        train_speed = train_samples / (train_time - start_time)
        train_loss /= train_samples
        train_acc /= train_samples

        writer.add_scalar('train_loss', train_loss, epoch)
        writer.add_scalar('train_acc', train_acc, epoch)

        net.eval()
        test_loss = 0
        test_acc = 0
        test_samples = 0

        # begin test
        with torch.no_grad():
            for img, label in test_data_loader:
                img = img.to(args.device)
                label = label.to(args.device)
                label_onehot = F.one_hot(label, 10).float()
                out_fr = 0.
                for t in range(args.T):
                    encoded_img = encoder(img)
                    out_fr += net(encoded_img)
                out_fr = out_fr / args.T
                loss = F.mse_loss(out_fr, label_onehot)

                test_samples += label.numel()
                test_loss += loss.item() * label.numel()
                test_acc += (out_fr.argmax(1) == label).float().sum().item()
                functional.reset_net(net)
        test_time = time.time()
        test_speed = test_samples / (test_time - train_time)
        test_loss /= test_samples
        test_acc /= test_samples
        writer.add_scalar('test_loss', test_loss, epoch)
        writer.add_scalar('test_acc', test_acc, epoch)

        save_max = False
        print('*****test_acc*****', test_acc)
        print('*****max_test_acc*****', max_test_acc)

        if test_acc > max_test_acc:
            max_test_acc = test_acc
            save_max = True

        checkpoint = {
            'net': net.state_dict(),
            'optimizer': optimizer.state_dict(),
            'epoch': epoch,
            'max_test_acc': max_test_acc
        }
fangwei123456 / spikingjelly

请问该如何提高res18结构的SNN在cifar10数据集上的准确率呢？ #431
