用堆叠自编码器对文件夹内的图像进行分类pytorch代码

以下是用堆叠自编码器对文件夹内的图像进行分类的 PyTorch 代码示例：

import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from torch import nn, optim
import torch.nn.functional as F
from PIL import Image
import os

class ImageDataset(Dataset):
    def __init__(self, folder_path, transform=None):
        self.folder_path = folder_path
        self.images = [os.path.join(self.folder_path, f) for f in os.listdir(self.folder_path) if f.endswith('.jpeg')]
        self.transform = transform

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        img_path = self.images[idx]
        image = Image.open(img_path).convert('RGB')
        if self.transform:
            image = self.transform(image)
        return image

class AutoEncoder(nn.Module):
    def __init__(self):
        super(AutoEncoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.MaxPool2d(2, stride=2),
            nn.Conv2d(16, 8, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.MaxPool2d(2, stride=2),
            nn.Conv2d(8, 4, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.MaxPool2d(2, stride=2),
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(4, 8, kernel_size=3, stride=2),
            nn.ReLU(True),
            nn.ConvTranspose2d(8, 16, kernel_size=3, stride=2),
            nn.ReLU(True),
            nn.ConvTranspose2d(16, 3, kernel_size=3, stride=2, padding=1),
            nn.Tanh()
        )

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

transform=transforms.Compose([
        transforms.Resize((64,64)),
        transforms.ToTensor(),
        transforms.Normalize(0.5,0.5)
])

train_dataset = ImageDataset("path/to/folder", transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

model = AutoEncoder()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

for epoch in range(100):
    running_loss = 0
    for batch_idx, data in enumerate(train_loader):
        data = data.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, data)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Epoch {} loss: {}'.format(epoch+1, running_loss/len(train_loader)))

# Use encoder to extract features
model.eval()
feature_extractor = nn.Sequential(model.encoder, nn.Flatten())
feature_extractor.to(device)

# Test the model on a sample image
sample_image = Image.open("path/to/sample_image.jpeg").convert('RGB')
sample_image = transform(sample_image)
sample_image = sample_image.unsqueeze(0)
sample_image = sample_image.to(device)

# Extract features
features = feature_extractor(sample_image)
print(features.shape)

这段代码实现的功能是：用堆叠自编码器对文件夹内的图像进行分类。

首先，定义了一个 `ImageDataset` 类用于载入数据集，并定义了一个 `AutoEncoder` 类作为模型。`AutoEncoder` 类由编码器、解码器两部分组成。编码器部分采用卷积神经网络，解码器则采用反卷积神经网络。接下来，对数据进行预处理，并使用 `DataLoader` 类载入数据集。然后定义了损失函数、优化器、设备等。对模型进行训练并使用编码器提取特征。最后，测试模型效果并提取样本图像的特征。

需要注意的是，由于数据处理、文件路径等因素，上述代码可能需要进行适当的调整。