请跑一个代码,使用transformer算法在UCI乳腺癌数据集上实现二分类,且输出训练集、验证集和测试集的准确率
时间: 2024-04-30 13:19:31 浏览: 9
好的,以下是使用transformer算法在UCI乳腺癌数据集上实现二分类的代码,并输出训练集、验证集和测试集的准确率:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from transformers import TransformerEncoder, TransformerEncoderLayer
# 加载数据集
data = load_breast_cancer()
x = data.data
y = data.target
# 划分训练集、验证集和测试集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=42)
# 定义数据集类
class BreastCancerDataset(Dataset):
def __init__(self, x, y):
self.x = x
self.y = y
def __getitem__(self, index):
return torch.tensor(self.x[index], dtype=torch.float32), torch.tensor(self.y[index], dtype=torch.long)
def __len__(self):
return len(self.x)
# 定义模型类
class TransformerClassifier(nn.Module):
def __init__(self, n_feat, n_class, nhead, nhid, nlayers, dropout):
super().__init__()
self.transformer_encoder_layer = TransformerEncoderLayer(d_model=n_feat, nhead=nhead, dim_feedforward=nhid, dropout=dropout, activation='relu')
self.transformer_encoder = TransformerEncoder(self.transformer_encoder_layer, num_layers=nlayers)
self.fc = nn.Linear(n_feat, n_class)
def forward(self, x):
x = self.transformer_encoder(x)
x = x.mean(dim=1)
x = self.fc(x)
return x
# 训练函数
def train(model, dataloader, criterion, optimizer):
model.train()
train_loss = 0
train_acc = 0
for x, y in dataloader:
optimizer.zero_grad()
output = model(x)
loss = criterion(output, y)
loss.backward()
optimizer.step()
train_loss += loss.item() * len(x)
train_acc += (output.argmax(dim=1) == y).sum().item()
train_loss = train_loss / len(dataloader.dataset)
train_acc = train_acc / len(dataloader.dataset)
return train_loss, train_acc
# 验证函数
def validate(model, dataloader, criterion):
model.eval()
val_loss = 0
val_acc = 0
with torch.no_grad():
for x, y in dataloader:
output = model(x)
loss = criterion(output, y)
val_loss += loss.item() * len(x)
val_acc += (output.argmax(dim=1) == y).sum().item()
val_loss = val_loss / len(dataloader.dataset)
val_acc = val_acc / len(dataloader.dataset)
return val_loss, val_acc
# 测试函数
def test(model, dataloader):
model.eval()
test_acc = 0
with torch.no_grad():
for x, y in dataloader:
output = model(x)
test_acc += (output.argmax(dim=1) == y).sum().item()
test_acc = test_acc / len(dataloader.dataset)
return test_acc
# 设置超参数
n_feat = x.shape[1]
n_class = 2
nhead = 8
nhid = 64
nlayers = 4
dropout = 0.1
lr = 0.001
batch_size = 64
epochs = 10
# 创建数据集和数据加载器
train_dataset = BreastCancerDataset(x_train, y_train)
val_dataset = BreastCancerDataset(x_val, y_val)
test_dataset = BreastCancerDataset(x_test, y_test)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# 创建模型、损失函数和优化器
model = TransformerClassifier(n_feat, n_class, nhead, nhid, nlayers, dropout)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
# 训练和验证模型
for epoch in range(epochs):
train_loss, train_acc = train(model, train_dataloader, criterion, optimizer)
val_loss, val_acc = validate(model, val_dataloader, criterion)
print(f'Epoch {epoch+1}: Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}')
# 测试模型
test_acc = test(model, test_dataloader)
print(f'Test Acc: {test_acc:.4f}')
```
运行代码后,会输出10个epoch中每个epoch的训练损失、训练准确率、验证损失和验证准确率,最后输出测试集的准确率。