有相关的示例代码吗

这里提供一个使用PyTorch实现自编码器对图片进行无监督分类的示例代码，供参考：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets

# 定义自编码器模型
class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
            nn.ReLU()
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(32, 16, kernel_size=4, stride=2, padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(16, 3, kernel_size=4, stride=2, padding=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

# 加载数据集
transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor()
])
train_set = datasets.ImageFolder(root='D:/wjd', transform=transform)
train_loader = data.DataLoader(train_set, batch_size=32, shuffle=True)

# 训练自编码器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Autoencoder().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

num_epochs = 10
for epoch in range(num_epochs):
    running_loss = 0.0
    for data in train_loader:
        images, _ = data
        images = images.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, images)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * images.size(0)
    epoch_loss = running_loss / len(train_loader.dataset)
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, epoch_loss))

# 提取特征向量并聚类
features = []
labels = []
with torch.no_grad():
    for data in train_loader:
        images, target = data
        images = images.to(device)
        outputs = model.encoder(images)
        features.append(outputs.cpu().numpy())
        labels.append(target.numpy())
features = np.concatenate(features, axis=0)
labels = np.concatenate(labels, axis=0)

from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=5, random_state=0).fit(features)

# 打印聚类结果
print(kmeans.labels_)

这个示例代码中，我们使用了PyTorch来实现自编码器模型，并使用ImageFolder类来加载数据集。在训练过程中，我们使用均方误差作为损失函数，并使用Adam优化器来更新模型参数。在训练完毕后，我们使用模型的编码器部分来提取特征向量，并使用K-Means算法对特征向量进行聚类，从而对图片进行无监督分类。