32×32的灰度照片，lenet5卷积神经网络代码

下面是一个简单的示例代码，用于训练一个基于LeNet-5的卷积神经网络来对32x32的灰度图像进行分类。该代码使用PyTorch框架，您需要安装PyTorch才能运行它。

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

# 定义LeNet-5模型
class LeNet5(nn.Module):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=2)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(16*5*5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 16*5*5)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.Compose([
    transforms.Grayscale(),
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5])
]))
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.Compose([
    transforms.Grayscale(),
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5])
]))
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128, shuffle=False)

# 定义训练参数
num_epochs = 10
learning_rate = 0.001

# 初始化模型和优化器
model = LeNet5()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 前向传播
        outputs = model(images)

        # 计算损失函数
        loss = nn.CrossEntropyLoss()(outputs, labels)

        # 反向传播并更新参数
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 输出训练状态信息
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))

这个例子使用了CIFAR-10数据集，您可以将其替换为您自己的32x32灰度图像数据集。请注意，您可能需要根据您的数据集进行一些微调，例如更改批量大小、学习率和训练时期数等。