域对抗思想实现MNIST向MNIST_M迁移的代码

实现MNIST向MNIST_M的迁移可以采用域对抗思想，具体实现可以使用深度学习框架PyTorch。以下是一份基于PyTorch实现的代码示例，用于将MNIST数据集训练的模型迁移到MNIST_M数据集上。

首先，需要安装PyTorch和 torchvision 库：

!pip install torch torchvision

然后，我们需要加载MNIST和MNIST_M数据集。MNIST数据集是手写数字的灰度图像，MNIST_M数据集是由三个不同颜色的背景（黑色、白色和砖红色）组成的手写数字图像。

import torch
import torchvision
from torchvision import datasets, transforms

# 设置数据集路径
data_dir = './data/'

# 定义转换器，将图像转换为张量并进行归一化处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 加载MNIST数据集
mnist_train = datasets.MNIST(data_dir, train=True, download=True, transform=transform)
mnist_test = datasets.MNIST(data_dir, train=False, download=True, transform=transform)

# 加载MNIST_M数据集
mnist_m_train = datasets.ImageFolder(data_dir + 'mnist_m/train/', transform=transform)
mnist_m_test = datasets.ImageFolder(data_dir + 'mnist_m/test/', transform=transform)

接下来，我们需要定义模型。在本示例中，我们使用一个简单的卷积神经网络作为分类器。

import torch.nn as nn
import torch.nn.functional as F

class Classifier(nn.Module):
    def __init__(self):
        super(Classifier, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1)
        self.dropout = nn.Dropout2d(p=0.25)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, 10)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = self.dropout(x)
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

然后，我们定义一个域分类器来判断图像来自MNIST或MNIST_M数据集。

class DomainClassifier(nn.Module):
    def __init__(self):
        super(DomainClassifier, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, stride=1, padding=1)
        self.dropout = nn.Dropout2d(p=0.25)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, 1)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = self.dropout(x)
        x = torch.flatten(x, 1)
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

接下来，我们定义训练和测试函数。

def train(model, domain_classifier, source_loader, target_loader, optimizer, criterion, device):
    model.train()
    domain_classifier.train()
    running_loss = 0.0
    running_corrects = 0
    for i, ((source_data, source_labels), (target_data, _)) in enumerate(zip(source_loader, target_loader)):
        batch_size = source_data.size(0)
        source_data, source_labels, target_data = source_data.to(device), source_labels.to(device), target_data.to(device)

        # 训练分类器
        optimizer.zero_grad()
        source_preds = model(source_data)
        source_loss = criterion(source_preds, source_labels)
        source_loss.backward()
        optimizer.step()

        # 训练域分类器
        optimizer.zero_grad()
        source_preds = model(source_data)
        source_domain_preds = domain_classifier(source_preds)
        target_preds = model(target_data)
        target_domain_preds = domain_classifier(target_preds)
        domain_loss = criterion(source_domain_preds, torch.ones(batch_size, 1).to(device)) + \
                      criterion(target_domain_preds, torch.zeros(batch_size, 1).to(device))
        domain_loss.backward()
        optimizer.step()

        running_loss += source_loss.item() * batch_size
        running_corrects += torch.sum(torch.argmax(source_preds, dim=1) == source_labels)

    epoch_loss = running_loss / len(source_loader.dataset)
    epoch_acc = running_corrects.float() / len(source_loader.dataset)

    return epoch_loss, epoch_acc


def test(model, loader, criterion, device):
    model.eval()
    running_loss = 0.0
    running_corrects = 0
    with torch.no_grad():
        for data, labels in loader:
            data, labels = data.to(device), labels.to(device)

            outputs = model(data)
            loss = criterion(outputs, labels)

            running_loss += loss.item() * data.size(0)
            running_corrects += torch.sum(torch.argmax(outputs, dim=1) == labels)

    epoch_loss = running_loss / len(loader.dataset)
    epoch_acc = running_corrects.float() / len(loader.dataset)

    return epoch_loss, epoch_acc

最后，我们进行模型训练和测试。

# 设置超参数
lr = 0.001
num_epochs = 20
batch_size = 128
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 加载数据集
source_loader = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True)
target_loader = torch.utils.data.DataLoader(mnist_m_train, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(mnist_m_test, batch_size=batch_size, shuffle=False)

# 定义模型、优化器和损失函数
model = Classifier().to(device)
domain_classifier = DomainClassifier().to(device)
optimizer = torch.optim.Adam(list(model.parameters()) + list(domain_classifier.parameters()), lr=lr)
criterion = nn.CrossEntropyLoss()

# 训练和测试
for epoch in range(num_epochs):
    train_loss, train_acc = train(model, domain_classifier, source_loader, target_loader, optimizer, criterion, device)
    test_loss, test_acc = test(model, test_loader, criterion, device)
    print(f'Epoch {epoch + 1}/{num_epochs}, '
          f'Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, '
          f'Test Loss: {test_loss:.4f}, Test Acc: {test_acc:.4f}')

在训练完成后，我们可以使用训练好的模型来进行预测。

def predict(model, loader, device):
    model.eval()
    predictions = []
    with torch.no_grad():
        for data in loader:
            data = data.to(device)

            outputs = model(data)
            preds = torch.argmax(outputs, dim=1)

            predictions += preds.cpu().tolist()

    return predictions

# 在MNIST_M测试集上进行预测
test_predictions = predict(model, test_loader, device)

这里的代码示例只是一个简单的实现，可能不够完善，但可以作为基础参考。