基于自适应池化方式的卷积神经网络 python

以下是一个基于自适应池化方式的卷积神经网络的Python实现示例：

import torch
import torch.nn as nn

class AdaptivePoolNet(nn.Module):
    def __init__(self):
        super(AdaptivePoolNet, self).__init__()
        
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3)
        self.pool = nn.AdaptiveAvgPool2d((1, 1)) # 自适应平均池化层
        self.fc = nn.Linear(128, 10) # 全连接层

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.relu(self.conv3(x))
        x = self.pool(x)
        x = x.view(-1, 128)
        x = self.fc(x)
        return x

# 实例化模型
model = AdaptivePoolNet()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 2000 == 1999:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

在这个示例中，我们使用了PyTorch框架，定义了一个名为AdaptivePoolNet的类，它继承了nn.Module类。我们在构造函数中定义了卷积层、自适应平均池化层和全连接层。在前向传播函数中，我们依次将输入x通过卷积层、ReLU激活函数和自适应平均池化层进行处理，然后将结果展开并通过全连接层输出。在训练过程中，我们使用了交叉熵损失函数和Adam优化器进行优化。