DermaMNIST数据集对应的标签英文名称
时间: 2023-09-23 21:03:00 浏览: 50
DermaMNIST数据集对应的标签英文名称包括:
1. Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec)
2. Basal cell carcinoma (bcc)
3. Benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl)
4. Dermatofibroma (df)
5. Melanoma (mel)
6. Melanocytic nevi (nv)
7. Vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc)
相关问题
DermaMNIST数据集的标签英文名称
The label English names of the DermaMNIST dataset are as follows:
- akiec: Actinic Keratoses
- bcc: Basal Cell Carcinoma
- bkl: Benign Keratosis-like Lesions
- df: Dermatofibroma
- mel: Melanoma
- nv: Melanocytic Nevi
- vasc: Vascular Lesions
给我一段用DermaMNIST进行联邦学习的案例代码
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!