import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset,TensorDataset
from torchvision.transforms import ToTensor
# 定义欺诈检测模型
class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.fc1 = nn.Linear(29, 64)
self.fc2 = nn.Linear(64, 32)
self.fc3 = nn.Linear(32, 2)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# 定义元学习模型
class MAML(nn.Module):
def __init__(self, model):
super(MAML, self).__init__()
self.model = model
self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)
def forward(self, x):
return self.model(x)
def meta_update(self, support_data, support_labels, query_data, query_labels, num_updates=1):
# 复制模型的初始参数
fast_weights = [p.clone().detach() for p in self.model.parameters()]
for i in range(num_updates):
# 在支持集上计算梯度并更新参数
support_preds = self.model(support_data, fast_weights)
support_loss = nn.functional.cross_entropy(support_preds, support_labels)
grad = torch.autograd.grad(support_loss, fast_weights)
fast_weights = [w - 0.01 * g for w, g in zip(fast_weights, grad)]
# 在查询集上计算损失
query_preds = self.model(query_data, fast_weights)
query_loss = nn.functional.cross_entropy(query_preds, query_labels)
# 执行元学习更新
self.optimizer.zero_grad()
query_loss.backward()
self.optimizer.step()
return query_loss.item()
# 训练循环
def train(model, maml, train_loader, num_epochs=10, num_updates=5):
model.train()
for epoch in range(num_epochs):
epoch_loss = 0
for i, (x, y) in enumerate(train_loader):
# 元学习模型更新
support_data, support_labels = x[:, :20], y[:20]
query_data, query_labels = x[:, 20:], y[20:]
loss = maml.meta_update(support_data, support_labels, query_data, query_labels, num_updates=num_updates)
epoch_loss += loss
print(f'Epoch {epoch+1}, loss={epoch_loss:.4f}')
# 测试循环
def test(model, test_loader):
model.eval()
correct = 0
total = 0
with torch.no_grad():
for x, y in test_loader:
preds = model(x)
predicted = torch.argmax(preds, dim=1)
total += y.size(0)
correct += (predicted == y).sum().item()
accuracy = 100 * correct / total
print(f'Test accuracy: {accuracy:.2f}%')
# 生成随机的训练集和测试集
def generate_data(num_train=1000, num_test=200):
# 特征数和类别数
num_features = 30
num_classes = 2
# 生成训练集和测试集特征
train_features = np.random.randn(num_train, num_features)
test_features = np.random.randn(num_test, num_features)
# 生成训练集和测试集标签
train_labels = np.random.randint(num_classes, size=num_train)
test_labels = np.random.randint(num_classes, size=num_test)
# 对标签进行欺诈样本的设置
fraud_index = np.random.choice(num_train, int(0.1*num_train), replace=False)
train_labels[fraud_index] = 1
return train_features, train_labels, test_features, test_labels
# 生成数据
train_features, train_labels, test_features, test_labels = generate_data()
# 转换数据为tensor并加载到DataLoader中
train_dataset = TensorDataset(torch.tensor(train_features, dtype=torch.float32), torch.tensor(train_labels, dtype=torch.long))
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_dataset = TensorDataset(torch.tensor(test_features, dtype=torch.float32), torch.tensor(test_labels, dtype=torch.long))
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# 初始化模型和元学习模型
model = MLP()
maml = MAML(model)
# 训练模型
train(model, maml, train_loader)
# 测试模型
test(model, test_loader)
File D:\Python3.10\lib\site-packages\torch\nn\modules\module.py:1501, in Module._call_impl(self, *args, **kwargs) 1496 # If we don't have any hooks, we want to skip the rest of the logic in 1497 # this function, and just call forward. 1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1499 or _global_backward_pre_hooks or _global_backward_hooks 1500 or _global_forward_hooks or _global_forward_pre_hooks): -> 1501 return forward_call(*args, **kwargs) 1502 # Do not call functions when jit is used 1503 full_backward_hooks, non_full_backward_hooks = [], []
TypeError: MLP.forward() takes 2 positional arguments but 3 were given
