AMC-loss pytorch代码
时间: 2024-06-15 13:02:00 浏览: 25
AMC-loss (Adversarial Mutual Contrast) 是一种在深度学习中用于增强模型对对抗样本鲁棒性的损失函数。它结合了对抗训练(adversarial training)和对比学习的思想,通过同时优化模型的正向预测和负向区分能力。在PyTorch中实现AMC-loss,你需要定义一个新的损失函数,并可能在训练循环中使用`nn.Module`或自定义优化器。
以下是一个简单的AMC-loss的PyTorch代码概述:
```python
import torch
import torch.nn as nn
class AMCContrastLoss(nn.Module):
def __init__(self, base_loss, device='cuda'):
super(AMCContrastLoss, self).__init__()
self.base_loss = base_loss
self.device = device
self.criterion = nn.CrossEntropyLoss()
def forward(self, outputs, targets, adversarial_samples):
# 假设outputs是模型的预测,targets是真实标签,adversarial_samples是生成的对抗样本
real_pred, adv_pred = outputs[0], outputs # 分离原始输出和对抗输出
# 正向预测
real_loss = self.base_loss(real_pred, targets)
# 负向区分(对比学习)
contrast_logits = torch.cat((real_pred, adv_pred), dim=0)
contrast_labels = torch.cat((torch.zeros_like(targets), torch.ones_like(targets)), dim=0).to(self.device)
contrast_loss = self.criterion(contrast_logits, contrast_labels)
# 总损失
total_loss = real_loss + contrast_loss
return total_loss
# 使用示例
model = MyModel().to(device)
optimizer = torch.optim.Adam(model.parameters())
amc_loss = AMCContrastLoss(nn.CrossEntropyLoss())
for inputs, targets in train_loader:
inputs, targets = inputs.to(device), targets.to(device)
adversarial_inputs = generate_adversarial(inputs) # 假设这个函数能生成对抗样本
outputs = model(inputs, adversarial_inputs)
loss = amc_loss(outputs, targets, adversarial_inputs)
optimizer.zero_grad()
loss.backward()
optimizer.step()
```
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