spyder的模型训练代码

Spyder是一个集成开发环境（IDE），主要用于科学计算和数据分析，特别是与Python语言结合。在Spyder中进行模型训练，通常涉及到深度学习库如TensorFlow或PyTorch。下面是一个简单的例子，展示如何使用PyTorch在Spyder中训练一个线性回归模型：

# 导入所需库
import torch
import torch.nn as nn
from sklearn.model_selection import train_test_split

# 假设我们有数据集
data = ... # 填充你的数据和标签
X_train, X_test, y_train, y_test = train_test_split(data['features'], data['labels'], test_size=0.2)

# 定义模型
class LinearRegression(nn.Module):
    def __init__(self, input_dim):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(input_dim, 1) 

    def forward(self, x):
        return self.linear(x)

model = LinearRegression(X_train.shape[1])

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# 训练循环
for epoch in range(num_epochs):  
    model.train()  # 将模式设置为训练
    optimizer.zero_grad()  # 清零梯度
    outputs = model(X_train)
    loss = criterion(outputs, y_train)
    loss.backward()  # 反向传播
    optimizer.step()  # 更新权重

# 评估模型
model.eval()
with torch.no_grad():
    predictions = model(X_test)
    mse = criterion(predictions, y_test)

print(f"Epoch {epoch + 1}: Loss = {mse.item()}")