写一个将注意力机制加到别的网络中的实例代码,包括数据集
时间: 2024-03-24 08:40:47 浏览: 17
好的,以下是一个将注意力机制加到CNN网络中的实例代码,使用的数据集为CIFAR-10。
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# 定义注意力机制
class Attention(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1):
super(Attention, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=1, padding=0, bias=False)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=kernel_size, stride=1, padding=0, bias=False)
self.conv3 = nn.Conv2d(out_channels, out_channels, kernel_size=kernel_size, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
self.bn3 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
out += residual
out = self.relu(out)
return out
# 定义CNN网络
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.attention1 = Attention(64, 64)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(64, 128, 3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(128)
self.attention2 = Attention(128, 128)
self.pool2 = nn.MaxPool2d(2, 2)
self.conv3 = nn.Conv2d(128, 256, 3, stride=1, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(256)
self.attention3 = Attention(256, 256)
self.pool3 = nn.MaxPool2d(2, 2)
self.fc = nn.Linear(256 * 4 * 4, 10)
def forward(self, x):
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.attention1(out)
out = self.pool1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.attention2(out)
out = self.pool2(out)
out = self.conv3(out)
out = self.bn3(out)
out = self.relu(out)
out = self.attention3(out)
out = self.pool3(out)
out = out.view(-1, 256 * 4 * 4)
out = self.fc(out)
return out
# 加载数据集
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
# 训练网络
net = CNN()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)
for epoch in range(100):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
```
在这个CNN网络中,我们使用了三个注意力机制,分别应用于三个卷积层。在每个卷积层的输出和输入加和时应用注意力机制,以增强网络对重要信息的关注。
请注意,这只是一个简单的示例,实际应用中,注意力机制的设计需要根据具体问题进行调整。
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