以下是一个基于PyTorch的简单CNN模型实现：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义CNN模型
class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, 7)

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.max_pool2d(x, 2)
        x = torch.relu(self.conv2(x))
        x = torch.max_pool2d(x, 2)
        x = x.view(-1, 64 * 7 * 7)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 数据预处理
transform = transforms.Compose([
    transforms.Grayscale(),
    transforms.Resize((48, 48)),
    transforms.ToTensor(),
])

# 加载数据集
train_dataset = datasets.ImageFolder(root='path_to_train_data', transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)

# 初始化模型、损失函数和优化器
model = SimpleCNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    for images, labels in train_loader:
        outputs = model(images)
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print(f'Epoch {epoch+1}, Loss: {loss.item()}')