pytorch实现卷积神经网络（LeNet）平均池化正则化的代码实现及模型预测

以下是基于PyTorch实现LeNet网络的代码，包括了平均池化和正则化：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable

# 定义LeNet网络结构
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, padding=2)
        self.avgpool1 = nn.AvgPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
        self.avgpool2 = nn.AvgPool2d(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)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(p=0.5)
    
    def forward(self, x):
        out = self.relu(self.conv1(x))
        out = self.avgpool1(out)
        out = self.relu(self.conv2(out))
        out = self.avgpool2(out)
        out = out.view(out.size(0), -1)
        out = self.relu(self.fc1(out))
        out = self.relu(self.fc2(out))
        out = self.dropout(out)
        out = self.fc3(out)
        return out

# 加载MNIST数据集
train_dataset = dsets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = dsets.MNIST(root='./data', train=False, transform=transforms.ToTensor())

# 定义超参数
batch_size = 100
learning_rate = 0.001
num_epochs = 10

# 加载数据集
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

# 实例化模型
model = LeNet()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.01)

# 训练模型
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = Variable(images)
        labels = Variable(labels)
        
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        if (i+1) % 100 == 0:
            print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f' % (epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.item()))

# 测试模型
model.eval()  # 设置模型为评估模式
correct = 0
total = 0
for images, labels in test_loader:
    images = Variable(images)
    outputs = model(images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted == labels).sum()

print('Accuracy of the model on the test images: %d %%' % (100 * correct / total))

在上面的代码中，我们使用了`nn.AvgPool2d`实现了平均池化，并在LeNet的全连接层中使用了`nn.Dropout`实现了正则化。运行上面的代码将会训练一个LeNet网络，并在MNIST测试集上进行测试，输出测试集上的准确率结果。