pytorch 实现SVR，用径向基函数

在PyTorch中实现SVR（支持向量回归）并使用径向基函数，可以按照以下步骤进行：

1. 导入所需的库和模块：

import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from sklearn.datasets import make_regression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

2. 生成合成数据集：

X, y = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=42)

3. 对数据进行标准化处理：

scaler = StandardScaler()
X = scaler.fit_transform(X)
y = scaler.fit_transform(y.reshape(-1,1))

4. 将数据集划分为训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

5. 定义SVR模型：

class SVR(nn.Module):
    def __init__(self):
        super(SVR, self).__init__()
        self.fc1 = nn.Linear(1, 10)
        self.fc2 = nn.Linear(10, 1)
        self.activation = nn.Tanh()

    def forward(self, x):
        x = self.activation(self.fc1(x))
        x = self.fc2(x)
        return x

6. 创建模型实例和优化器：

model = SVR()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = nn.MSELoss()

7. 将数据转换为PyTorch张量并创建数据加载器：

train_data = TensorDataset(torch.from_numpy(X_train).float(), torch.from_numpy(y_train).float())
train_loader = DataLoader(train_data, batch_size=16, shuffle=True)

8. 训练模型：

num_epochs = 100
for epoch in range(num_epochs):
    for inputs, targets in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

9. 在测试集上评估模型：

test_data = TensorDataset(torch.from_numpy(X_test).float(), torch.from_numpy(y_test).float())
test_loader = DataLoader(test_data, batch_size=16, shuffle=False)

model.eval()
with torch.no_grad():
    total_loss = 0
    for inputs, targets in test_loader:
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        total_loss += loss.item() * inputs.size(0)
    mean_loss = total_loss / len(test_data)
    print("Mean Loss: ", mean_loss)

这样就完成了使用PyTorch实现SVR并使用径向基函数的过程。