能给出具体的优化代码吗

优化代码取决于具体的应用场景和模型结构，下面是一些常用的优化技巧及其代码实现：

1. 批量归一化（Batch Normalization）：

class BatchNorm(nn.Module):
    def __init__(self, num_features, eps=1e-5, momentum=0.1):
        super(BatchNorm, self).__init__()
        self.num_features = num_features
        self.eps = eps
        self.momentum = momentum
        self.weight = nn.Parameter(torch.Tensor(num_features))
        self.bias = nn.Parameter(torch.Tensor(num_features))
        self.register_buffer('running_mean', torch.zeros(num_features))
        self.register_buffer('running_var', torch.ones(num_features))
        self.reset_parameters()

    def reset_parameters(self):
        self.weight.data.uniform_()
        self.bias.data.zero_()

    def forward(self, x):
        if self.training:
            mean = x.mean(dim=0, keepdim=True)
            var = x.var(dim=0, keepdim=True)
            self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean
            self.running_var = (1 - self.momentum) * self.running_var + self.momentum * var
        else:
            mean = self.running_mean
            var = self.running_var
        x = (x - mean) / torch.sqrt(var + self.eps)
        x = self.weight * x + self.bias
        return x

2. Dropout：

class Dropout(nn.Module):
    def __init__(self, p=0.5):
        super(Dropout, self).__init__()
        self.p = p

    def forward(self, x):
        if not self.training or self.p == 0:
            return x
        mask = (torch.rand(x.shape) > self.p).float().to(x.device)
        x = x * mask / (1 - self.p)
        return x

3. 权重衰减（Weight Decay）：

optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)

4. 学习率衰减（Learning Rate Decay）：

scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=lr_decay_step, gamma=lr_decay_rate)

5. 渐进式训练（Progressive Training）：

for i in range(num_finetune_steps):
    if i < num_warmup_steps:
        lr = init_lr * (i + 1) / num_warmup_steps
    else:
        lr = init_lr * (1 - (i - num_warmup_steps) / (num_finetune_steps - num_warmup_steps))
    optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
    train(model, optimizer, ...)