DANN的python代码
时间: 2023-12-15 10:32:14 浏览: 287
以下是DANN的Python代码,包括梯度反转层和整体模型的搭建:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
# 梯度反转层的构建(GRL)
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
ctx.alpha = alpha
return x.view_as(x)
@staticmethod
def backward(ctx, grad_output):
output = grad_output.neg() *
相关问题
DANN 自定义数据集 图像分类 python代码
以下是使用Python和PyTorch框架构建DANN模型进行图像分类的代码示例。假设我们的数据集包括两个域:源域和目标域,每个域包含10个类别,每个类别包含100张大小为28x28的灰度图像。
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.autograd import Function
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
class CustomDataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __getitem__(self, index):
x = self.data[index]
y = self.labels[index]
return x, y
def __len__(self):
return len(self.data)
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
ctx.alpha = alpha
return x
@staticmethod
def backward(ctx, grad_output):
output = grad_output.neg() * ctx.alpha
return output, None
class DANN(nn.Module):
def __init__(self):
super(DANN, self).__init__()
self.feature_extractor = nn.Sequential(
nn.Conv2d(1, 32, 5),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(32, 48, 5),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(48 * 4 * 4, 100),
nn.ReLU()
)
self.class_classifier = nn.Sequential(
nn.Linear(100, 100),
nn.ReLU(),
nn.Linear(100, 10)
)
self.domain_classifier = nn.Sequential(
nn.Linear(100, 100),
nn.ReLU(),
nn.Linear(100, 2)
)
def forward(self, x, alpha):
features = self.feature_extractor(x)
class_output = self.class_classifier(features)
reverse_features = ReverseLayerF.apply(features, alpha)
domain_output = self.domain_classifier(reverse_features)
return class_output, domain_output
def train(model, dataloader):
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
criterion_class = nn.CrossEntropyLoss()
criterion_domain = nn.CrossEntropyLoss()
for epoch in range(10):
for i, (source_data, source_labels) in enumerate(dataloader['source']):
source_data, source_labels = source_data.to(device), source_labels.to(device)
target_data, _ = next(iter(dataloader['target']))
target_data = target_data.to(device)
source_domain_labels = torch.zeros(source_data.size(0)).long().to(device)
target_domain_labels = torch.ones(target_data.size(0)).long().to(device)
optimizer.zero_grad()
source_class_output, source_domain_output = model(source_data, 0.1)
source_class_loss = criterion_class(source_class_output, source_labels)
source_domain_loss = criterion_domain(source_domain_output, source_domain_labels)
target_class_output, target_domain_output = model(target_data, 0.1)
target_domain_loss = criterion_domain(target_domain_output, target_domain_labels)
loss = source_class_loss + source_domain_loss + target_domain_loss
loss.backward()
optimizer.step()
if i % 10 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, i+1, len(dataloader['source']), loss.item()))
def test(model, dataloader):
correct = 0
total = 0
with torch.no_grad():
for data, labels in dataloader['target']:
data, labels = data.to(device), labels.to(device)
outputs, _ = model(data, 0)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))
if __name__ == '__main__':
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
transform = transforms.Compose([
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
source_dataset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
target_dataset = torchvision.datasets.USPS(root='./data', train=True, download=True, transform=transform)
source_data = source_dataset.data.unsqueeze(1).float()
source_labels = source_dataset.targets
target_data = target_dataset.data.unsqueeze(1).float()
target_labels = target_dataset.targets
source_loader = DataLoader(CustomDataset(source_data, source_labels), batch_size=64, shuffle=True)
target_loader = DataLoader(CustomDataset(target_data, target_labels), batch_size=64, shuffle=True)
dataloader = {'source': source_loader, 'target': target_loader}
model = DANN().to(device)
train(model, dataloader)
test(model, dataloader)
```
在这个示例中,我们使用了MNIST和USPS两个数据集作为源域和目标域,分别包含0~9十个数字的手写数字图像。我们使用了PyTorch中的MNIST和USPS数据集类来加载数据,并将图像转换成PyTorch需要的张量格式。同时,我们使用了PyTorch中的DataLoader类来构建数据迭代器,方便进行批量训练和测试。我们使用了交叉熵损失函数来计算分类和域分类的损失,并使用随机梯度下降(SGD)优化器来更新模型参数。在每个epoch开始时,我们从源域数据集中随机选择一批数据,从目标域数据集中选择一批数据,并将其送入模型进行训练。我们使用了Reverse Gradient Layer来实现域适应学习,将特征提取器的梯度反转,从而使得域分类器无法区分源域和目标域之间的特征。在测试阶段,我们将目标域数据集送入训练好的模型中,计算分类准确率。
你可以生成DANN的代码吗
当您提到DANN时,我想您指的是域自适应神经网络。下面是一个基本的DANN的实现代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
class DANN(nn.Module):
def __init__(self):
super(DANN, self).__init__()
# 特征提取器
self.feature_extractor = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=5),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 50, kernel_size=5),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
# 领域分类器
self.domain_classifier = nn.Sequential(
nn.Linear(50*4*4, 100),
nn.ReLU(inplace=True),
nn.Linear(100, 2),
)
# 类别分类器
self.class_classifier = nn.Sequential(
nn.Linear(50*4*4, 100),
nn.ReLU(inplace=True),
nn.Linear(100, 10),
)
def forward(self, x, alpha):
# 提取特征
x = self.feature_extractor(x)
x = x.view(-1, 50*4*4)
# 计算类别概率
class_output = self.class_classifier(x)
# 计算领域概率
domain_output = self.domain_classifier(x*alpha)
return class_output, domain_output
```
DANN的训练过程包括两个阶段:1)特征提取器和类别分类器的训练;2)领域分类器的训练。下面是训练代码的示例:
```python
def train_dann(model, dataloader, optimizer, alpha):
# 设置为训练模式
model.train()
# 训练特征提取器和类别分类器
for i, (images, labels, domains) in enumerate(dataloader):
images = images.float()
labels = labels.long()
domains = domains.long()
optimizer.zero_grad()
class_output, domain_output = model(images, alpha)
# 计算分类和领域的损失
class_loss = nn.CrossEntropyLoss()(class_output, labels)
domain_loss = nn.CrossEntropyLoss()(domain_output, domains)
# 计算总损失
loss = class_loss + domain_loss
# 反向传播和更新参数
loss.backward()
optimizer.step()
# 训练领域分类器
for i, (images, labels, domains) in enumerate(dataloader):
images = images.float()
domains = domains.long()
optimizer.zero_grad()
_, domain_output = model(images, alpha)
# 计算领域的损失
domain_loss = nn.CrossEntropyLoss()(domain_output, domains)
# 反向传播和更新参数
domain_loss.backward()
optimizer.step()
```
在这个训练过程中,`alpha`参数用于控制特征提取器的权重,它应该在训练过程中动态调整。另外,您需要自己实现数据加载和预处理的部分。
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