用CNN和MMD进行样本迁移pytorch

以下是使用 CNN 和 MMD 进行样本迁移的 PyTorch 代码示例：

1. 定义数据集和数据加载器

import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.utils.data as data

# 定义数据预处理
transform = transforms.Compose([
    transforms.Resize(32),
    transforms.CenterCrop(32),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载训练集和测试集
train_set = dset.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_set = dset.CIFAR10(root='./data', train=False, download=True, transform=transform)

# 定义数据加载器
train_loader = data.DataLoader(train_set, batch_size=64, shuffle=True, num_workers=2)
test_loader = data.DataLoader(test_set, batch_size=64, shuffle=False, num_workers=2)

2. 定义模型

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

# 定义卷积神经网络
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(128 * 4 * 4, 256)
        self.fc2 = nn.Linear(256, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv3(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 128 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 创建模型实例
model = CNN()

3. 定义优化器和损失函数

import torch.optim as optim

# 定义优化器
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 定义损失函数
criterion = nn.CrossEntropyLoss()

4. 定义 MMD 损失函数

import torch

def mmd_loss(x_src, x_tgt, kernel_mul=2.0, kernel_num=5):
    """
    x_src: 源域数据
    x_tgt: 目标域数据
    kernel_mul: RBF 核的宽度
    kernel_num: RBF 核的数量
    """
    batch_size = int(x_src.size()[0])
    kernels = []
    for i in range(kernel_num):
        sigma = kernel_mul ** i
        kernels.append(gaussian_kernel(x_src, x_tgt, sigma))
    loss = 0
    for kernel in kernels:
        loss += torch.mean(kernel)
    return loss / kernel_num

def gaussian_kernel(x_src, x_tgt, sigma):
    """
    x_src: 源域数据
    x_tgt: 目标域数据
    sigma: RBF 核的宽度
    """
    n = int(x_src.size()[0])
    m = int(x_tgt.size()[0])
    x = torch.cat([x_src, x_tgt], dim=0)
    xx = torch.mm(x, x.t())
    x2 = torch.sum(x ** 2, dim=1, keepdim=True)
    exponent = xx - 0.5 * (x2 + x2.t())
    kernel = torch.exp(-exponent / (2 * sigma ** 2))
    return kernel[:n, :n] + kernel[n:, n:] - kernel[:n, n:] - kernel[n:, :n]

5. 训练模型

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()

        # 将数据分为源域和目标域数据
        src_inputs = inputs[:32]
        tgt_inputs = inputs[32:]

        # 在源域数据上进行前向传播和反向传播
        src_outputs = model(src_inputs)
        src_loss = criterion(src_outputs, labels[:32])
        src_loss.backward()

        # 在目标域数据上进行前向传播和反向传播
        tgt_outputs = model(tgt_inputs)
        tgt_loss = mmd_loss(src_outputs, tgt_outputs)
        tgt_loss.backward()

        optimizer.step()

        # 输出统计信息
        running_loss += src_loss.item() + tgt_loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

在每个训练循环中，将数据分为源域和目标域数据，分别在源域和目标域数据上进行前向传播和反向传播。在目标域数据上计算 MMD 损失函数，并通过反向传播更新模型参数。