给我一段用DermaMNIST进行联邦学习的案例代码
时间: 2024-05-12 21:17:14 浏览: 8
好的,以下是一段使用DermaMNIST进行联邦学习的案例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
import syft as sy
hook = sy.TorchHook(torch)
# Define the dataset class for DermaMNIST
class DermaMNIST(Dataset):
def __init__(self, data, target):
self.data = data
self.target = target
def __getitem__(self, index):
data = self.data[index]
target = self.target[index]
return data, target
# Initialize the virtual workers
alice = sy.VirtualWorker(hook, id="alice")
bob = sy.VirtualWorker(hook, id="bob")
# Load and split the DermaMNIST dataset
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
trainset = datasets.MNIST("./data", train=True, download=True, transform=transform)
testset = datasets.MNIST("./data", train=False, download=True, transform=transform)
trainset_federated = trainset.federate((alice, bob))
# Define the model architecture
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
x = nn.functional.relu(nn.functional.max_pool2d(self.drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return nn.functional.log_softmax(x, dim=1)
model = Net()
# Define the optimizer and loss function
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
criterion = nn.NLLLoss()
# Federated training loop
def train():
model.train()
for batch_idx, (data, target) in enumerate(trainset_federated):
model.send(data.location)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
model.get()
if batch_idx % 100 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * data.shape[0], len(trainset_federated),
100. * batch_idx / len(trainset_federated), loss.item()))
# Test the federated model
def test():
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in testset:
output = model(data.unsqueeze(0))
test_loss += criterion(output, target.unsqueeze(0)).item()
pred = output.argmax().item()
correct += pred == target
test_loss /= len(testset)
accuracy = correct / len(testset)
print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
test_loss, correct, len(testset), 100. * accuracy))
# Train the federated model for 10 epochs
for epoch in range(1, 11):
train()
test()
```
希望能够解答你的问题,如果你还有其他问题或者疑问,可以随时问我哦!