生成一段基于PyTorch框架实现传统伪造检测的代码

以下是一个基于 PyTorch 框架实现传统伪造检测的简单代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义模型
class FakeDetectionModel(nn.Module):
    def __init__(self):
        super(FakeDetectionModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(128 * 8 * 8, 1024)
        self.fc2 = nn.Linear(1024, 1)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = self.pool(nn.functional.relu(self.conv3(x)))
        x = torch.flatten(x, 1)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.sigmoid(self.fc2(x))
        return x

# 定义训练函数
def train(model, train_loader, optimizer, criterion, device):
    model.train()
    running_loss = 0.0
    for i, (images, labels) in enumerate(train_loader):
        images, labels = images.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels.float())
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / len(train_loader)

# 定义测试函数
def test(model, test_loader, criterion, device):
    model.eval()
    running_loss = 0.0
    correct = 0
    total = 0
    with torch.no_grad():
        for i, (images, labels) in enumerate(test_loader):
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            loss = criterion(outputs, labels.float())
            running_loss += loss.item()
            predicted = (outputs > 0.5).float()
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    return running_loss / len(test_loader), correct / total

# 加载数据集
train_data = np.load('train_data.npy')
train_labels = np.load('train_labels.npy')
test_data = np.load('test_data.npy')
test_labels = np.load('test_labels.npy')

# 创建数据加载器
train_set = torch.utils.data.TensorDataset(torch.Tensor(train_data), torch.Tensor(train_labels))
test_set = torch.utils.data.TensorDataset(torch.Tensor(test_data), torch.Tensor(test_labels))
train_loader = torch.utils.data.DataLoader(train_set, batch_size=32, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=32, shuffle=False)

# 定义超参数
learning_rate = 0.001
num_epochs = 10

# 创建模型和优化器
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = FakeDetectionModel().to(device)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.BCELoss()

# 训练模型
for epoch in range(num_epochs):
    train_loss = train(model, train_loader, optimizer, criterion, device)
    test_loss, test_acc = test(model, test_loader, criterion, device)
    print('Epoch [{}/{}], Train Loss: {:.4f}, Test Loss: {:.4f}, Test Accuracy: {:.2f}%'.format(
        epoch+1, num_epochs, train_loss, test_loss, test_acc*100))

该代码示例中定义了一个名为 `FakeDetectionModel` 的模型，它包含了三个卷积层和两个全连接层。然后使用 `train` 函数和 `test` 函数分别对模型进行训练和测试，并通过定义的数据集加载器来加载数据。最后，使用 PyTorch 提供的优化器和损失函数来训练模型，并输出训练和测试的结果。