GoogLeNet是一个深度卷积神经网络架构，于2014年由Google团队在论文《Going Deeper with Convolutions》中提出。它是在ILSVRC竞赛中取得优异成绩的一种神经网络结构，可以用于图像分类和其他计算机视觉任务。
GoogLeNet之所以引人注目，是因为它在减少参数数量的同时保持了较高的准确率，以及引入了一种称为"Inception模块"的创新结构。Inception模块通过在同一层内使用多个不同大小的卷积核和池化操作，来捕捉不同尺度的图像特征。这种并行的特征提取方式有助于网络更好地捕捉图像中的局部和全局特征，从而提高了分类性能。
GoogLeNet的一个显著特点是其参数极少，相较于传统的深度卷积神经网络，它使用了全局平均池化层来显著减少参数数量。这有助于减轻过拟合问题，提高了模型的泛化能力。
GoogLeNet在深度学习领域的贡献是引入了Inception模块，展示了一种有效地减少参数数量同时保持模型性能的方法，为后续的网络设计提供了有价值的启发。然而，自GoogLeNet之后，深度神经网络架构还得到了许多进一步的发展和改进，如ResNet、Transformer等。
GoogLeNet在以下几个方面表现出色：
- 参数效率： GoogLeNet引入了Inception模块，允许在同一层内使用多个不同大小的卷积核和池化操作，从而更有效地捕获不同尺度的图像特征。这种设计减少了网络中的参数数量，使得模型更加轻量，同时仍然能够保持较高的准确性。
- 减轻梯度消失问题：在Inception模块内，1x1的卷积核被用来减少特征图的通道数，从而有助于减轻梯度消失问题，使网络更易于训练。
- 多尺度特征提取：通过Inception模块的并行结构，GoogLeNet可以同时从不同尺度上提取特征。这有助于网络更好地捕获图像中的局部和全局特征，提高了图像分类性能。
- 泛化能力： 由于参数较少且采用了全局平均池化层，GoogLeNet在一定程度上减轻了过拟合问题，提高了模型的泛化能力，可以更好地适应新数据。
- 图像分类性能：GoogLeNet在ILSVRC 2014竞赛中取得了优异的成绩，证明了它在图像分类任务上的有效性。它的设计理念也为后续的深度卷积神经网络提供了启发。
本次采用GoogLeNet对MNIST数据集进行训练，在炼丹侠平台中分别通过A100和CPU进行训练，对比两者之间性能差距。
GPU版本完整代码如下：
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# 定义GoogLeNet模型
class Inception(nn.Module):
def __init__(self, in_channels, out1x1, reduce3x3, out3x3, reduce5x5, out5x5, out1x1pool):
super(Inception, self).__init__()
# 定义Inception模块的各个分支
self.branch1 = nn.Conv2d(in_channels, out1x1, kernel_size=1)
self.branch2 = nn.Sequential(
nn.Conv2d(in_channels, reduce3x3, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(reduce3x3, out3x3, kernel_size=3, padding=1)
)
self.branch3 = nn.Sequential(
nn.Conv2d(in_channels, reduce5x5, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(reduce5x5, out5x5, kernel_size=5, padding=2)
)
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
nn.Conv2d(in_channels, out1x1pool, kernel_size=1)
)
def forward(self, x):
return torch.cat([self.branch1(x), self.branch2(x), self.branch3(x), self.branch4(x)], 1)
class GoogLeNet(nn.Module):
def __init__(self, num_classes=10):
super(GoogLeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3)
self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=1)
self.conv3 = nn.Conv2d(64, 192, kernel_size=3, padding=1)
self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(0.4)
self.fc = nn.Linear(1024, num_classes)
def forward(self, x):
x = self.maxpool1(self.conv1(x))
x = self.maxpool2(self.conv3(self.conv2(x)))
x = self.inception3b(self.inception3a(x))
x = self.maxpool3(x)
x = self.inception4e(self.inception4d(self.inception4c(self.inception4b(self.inception4a(x)))))
x = self.maxpool4(x)
x = self.inception5b(self.inception5a(x))
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.dropout(x)
x = self.fc(x)
return x
# 初始化模型并将其移至GPU
model = GoogLeNet().cuda()
# 定义数据预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# 加载训练数据集
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for images, labels in train_loader:
images = images.cuda()
labels = labels.cuda()
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}')
CPU版本完整代码如下：
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# 定义GoogLeNet模型
class Inception(nn.Module):
def __init__(self, in_channels, out1x1, reduce3x3, out3x3, reduce5x5, out5x5, out1x1pool):
super(Inception, self).__init__()
self.branch1 = nn.Conv2d(in_channels, out1x1, kernel_size=1)
self.branch2 = nn.Sequential(
nn.Conv2d(in_channels, reduce3x3, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(reduce3x3, out3x3, kernel_size=3, padding=1)
)
self.branch3 = nn.Sequential(
nn.Conv2d(in_channels, reduce5x5, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(reduce5x5, out5x5, kernel_size=5, padding=2)
)
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
nn.Conv2d(in_channels, out1x1pool, kernel_size=1)
)
def forward(self, x):
return torch.cat([self.branch1(x), self.branch2(x), self.branch3(x), self.branch4(x)], 1)
class GoogLeNet(nn.Module):
def __init__(self, num_classes=10):
super(GoogLeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3)
self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(64, 64, kernel_size=1)
self.conv3 = nn.Conv2d(64, 192, kernel_size=3, padding=1)
self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(0.4)
self.fc = nn.Linear(1024, num_classes)
def forward(self, x):
x = self.maxpool1(self.conv1(x))
x = self.maxpool2(self.conv3(self.conv2(x)))
x = self.inception3b(self.inception3a(x))
x = self.maxpool3(x)
x = self.inception4e(self.inception4d(self.inception4c(self.inception4b(self.inception4a(x)))))
x = self.maxpool4(x)
x = self.inception5b(self.inception5a(x))
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.dropout(x)
x = self.fc(x)
return x
# 初始化模型
model = GoogLeNet()
# 定义数据预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# 加载训练数据集
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for images, labels in train_loader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}')
在炼丹侠A100服务器上的训练过程如下：
https://www.bilibili.com/video/BV1Dp4y177fp/?spm_id_from=333....
在炼丹侠A100服务器上GoogLeNet的训练加速比肉眼可见，相比于CPU计算，使用A100进行训练的时间节省了3个多小时，在实际项目运行中这个时间性能提升可能会更多。