什么是全局平均池化？

全局平均池化是一种特殊的池化操作，它将每个特征图直接降维为一个标量值。具体来说，对于一个尺寸为H×W的特征图，全局平均池化会计算该特征图上所有元素的平均值，最终输出一个1×1的特征值。

全局平均池化具有以下优势：

1. 显著减少参数量：避免全连接层带来的参数爆炸

2. 降低过拟合风险：由于参数减少，模型复杂度降低

3. 增强平移不变性：对输入图像的位置变化更加鲁棒

4. 更好的可解释性：每个特征图对

RGB通道的关键点：

1. 三通道输入：分别对应红、绿、蓝颜色信息

2. 并行处理：每个卷积核同时处理所有输入通道

3. 特征融合：不同通道的信息在卷积过程中自动融合

通道数增加的原因：

1. 特征多样性：更多通道意味着可以检测更多类型的特征

2. 层次化学习：深层网络需要更复杂的特征表示

3. 信息容量：增加通道数提高网络的信息处理能力

4. 性能提升：适度的通道数增加通常能提升模型性能

测试

import torch
import torchvision
import torchvision.transforms as transforms

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1

for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))

全局平均池化

import torch.nn as nn
import torch.nn.functional as F

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

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, 5)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(16, 36, 5)
        #self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.pool2 = nn.MaxPool2d(2, 2)
        #使用全局平均池化层
        self.aap = nn.AdaptiveAvgPool2d(1)
        self.fc3 = nn.Linear(36, 10)

    def forward(self, x):
        x = self.pool1(F.relu(self.conv1(x)))
        x = self.pool2(F.relu(self.conv2(x)))
        x = self.aap(x)
        x = x.view(x.shape[0], -1)
        x = self.fc3(x)
        return x

net = Net()
net = net.to(device)

print("net_gvp have {} parameters in total".format(sum(x.numel() for x in net.parameters())))