稳定性更好的优化器AdamW怎么引用python代码

使用AdamW优化器的代码示例如下：

import torch
import torch.optim as optim

# 定义模型和损失函数
model = YourModel()
criterion = YourLossFunction()

# 定义AdamW优化器
optimizer = optim.AdamW(model.parameters(), lr=0.001, weight_decay=0.01)

# 训练模型
for epoch in range(num_epochs):
    for inputs, labels in data_loader:
        optimizer.zero_grad() # 梯度清零
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward() # 反向传播
        optimizer.step() # 更新参数

在此示例中，我们使用`optim.AdamW()`函数创建了一个AdamW优化器，并将模型的参数以及学习率和权重衰减率等超参数传递给了该函数。在训练过程中，我们在每个batch的前面调用了`optimizer.zero_grad()`来清空梯度，然后进行正向传播、计算损失并进行反向传播，最后调用`optimizer.step()`来更新模型的参数。

相关问题

模型优化器AdamW的代码怎么写

AdamW是一种基于Adam优化算法的变种，其主要区别在于对于权重衰减（Weight Decay）的处理方式不同。相比于Adam使用的L2正则化，AdamW采用的是权重衰减。

下面是一个简单的Python实现AdamW优化器的代码：

class AdamW(Optimizer):
    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        super(AdamW, self).__init__(params, defaults)

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            weight_decay = group['weight_decay']
            eps = group['eps']
            beta1, beta2 = group['betas']
            lr = group['lr']

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('AdamW does not support sparse gradients')
                
                state = self.state[p]

                # Initialize state
                if len(state) == 0:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p.data)
                    state['exp_avg_sq'] = torch.zeros_like(p.data)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta2_t = beta2 ** state['step']
                state['step'] += 1

                # Decay the weights
                if weight_decay != 0:
                    p.data.add_(-weight_decay * lr, p.data)

                # Perform optimization step
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                denom = (exp_avg_sq.sqrt() / math.sqrt(1 - beta2_t)).add_(eps)
                step_size = lr / (1 - beta1 ** state['step'])
                p.data.addcdiv_(-step_size, exp_avg, denom)

        return loss

上述代码实现了AdamW的优化过程，其中主要区别在于对权重衰减（weight_decay）的处理方式，采用了 L2正则化的替代方案。值得注意的是，上述代码实现的是torch中的AdamW优化器，如果要在其他框架或自己实现的深度学习框架中使用，需要根据实际情况进行调整。

adamw优化器的代码

AdamW（Weight Decay Regularization with Adaptive Moments）是一种结合了Adaptive Moment Estimation (Adam)算法和权重衰减（Weight Decay）的优化器，用于深度学习中。它在Adam的基础上增加了对权重衰减的支持，有助于防止过拟合。以下是AdamW的基本原理和一个简单的Python代码实现，使用PyTorch库：

import torch
from torch.optim import Optimizer

class AdamW(Optimizer):
    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=0.01, amsgrad=False):
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay, amsgrad=amsgrad)
        super(AdamW, self).__init__(params, defaults)

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p.data)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']

                state['step'] += 1

                if group['weight_decay'] != 0:
                    grad.add_(group['weight_decay'], p.data)

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)

                denom = exp_avg_sq.sqrt().add_(group['eps'])

                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']

                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                p.data.addcdiv_(-step_size, exp_avg, denom)

        return loss