CNN经典网络模型 - ResNet简介及代码实现
本帖最后由 Shaw0xyz 于 2024-6-23 15:46 编辑1. 引言
卷积神经网络（CNN）是深度学习中广泛应用的一种神经网络结构。在众多CNN模型中，残差网络（ResNet）因其出色的性能和深度网络训练中的稳定性而备受关注。本文将介绍ResNet的基本概念，并提供一个简要的代码实现。
2. ResNet简介
2.1 残差网络的背景
深度神经网络在层数增加时，训练过程中的梯度消失和梯度爆炸问题使得网络性能难以提升。为了缓解这一问题，ResNet引入了残差块，通过残差连接（shortcut connection）使得网络更深且更易于训练。
2.2 残差块结构
ResNet的核心是残差块。一个典型的残差块包括两个卷积层和一个恒等映射（identity mapping）。其基本结构如下：
输入 → 卷积层1 → 批归一化（Batch Normalization）→ ReLU → 卷积层2 → 批归一化 → 残差连接 → ReLU → 输出
残差连接跳过了两个卷积层，将输入直接加到输出上，这样网络学习的是输入和输出之间的残差，而不是直接学习输出。
2.3 ResNet的变种
ResNet有多个变种，包括ResNet-18、ResNet-34、ResNet-50、ResNet-101等。数字表示网络层数。ResNet-50及以上的变种采用了“瓶颈结构”来减少计算量，这种结构在每个残差块中增加了一个1x1卷积层。
3. ResNet代码实现
下面我们以PyTorch为例，简要实现一个ResNet-18模型。
3.1 导入必要的库
首先，导入PyTorch及相关模块：
import torch
import torch.nn as nn
import torch.nn.functional as F
3.2 定义残差块
class BasicBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.shortcut = nn.Sequential()
if stride != 1 or in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
3.3 定义ResNet模型
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks, stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks, stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks, stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks, stride=2)
self.linear = nn.Linear(512, num_classes)
def _make_layer(self, block, out_channels, num_blocks, stride):
strides = + *(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_channels, out_channels, stride))
self.in_channels = out_channels
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def ResNet18():
return ResNet(BasicBlock, )
3.4 模型训练和测试
在定义好模型后，可以进行模型训练和测试。以下是训练和测试代码的简要示例：
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
transform = transforms.Compose()
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=100, shuffle=True)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
test_loader = DataLoader(test_dataset, batch_size=100, shuffle=False)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ResNet18().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Training loop
for epoch in range(10):
model.train()
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
print(f'Epoch {epoch+1}, Loss: {loss.item()}')
# Testing loop
model.eval()
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Accuracy: {100 * correct / total}%')
4. 结论
本文介绍了ResNet的基本概念、残差块的结构以及如何在PyTorch中实现ResNet-18模型。通过这一实践，读者可以深入理解ResNet的设计思想和实现方法，并应用于实际的深度学习任务中。
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