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Users/username/code/linear_regression_m1.py

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+import torch
+
+# 检查MPS可用性（需要PyTorch 1.12+和macOS 12.3+）
+device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+
+# 生成训练数据（移动到MPS设备）
+X = torch.randn(1000, 2).to(device)  # 1000个样本，2个特征
+y = X @ torch.tensor([2.0, -3.4], device=device) + 4  # 真实关系式
+y += 0.01 * torch.randn(y.shape, device=device)  # 添加噪声
+
+# 定义模型（必须继承nn.Module）
+class LinearRegression(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = torch.nn.Linear(2, 1)  # 输入2维，输出1维
+        
+    def forward(self, x):
+        return self.linear(x)
+
+model = LinearRegression().to(device)  # 将模型移至MPS设备
+criterion = torch.nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
+
+# 训练循环
+for epoch in range(500):
+    # 前向传播
+    outputs = model(X)
+    loss = criterion(outputs, y.unsqueeze(1))
+    
+    # 反向传播
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    
+    if epoch % 50 == 0:
+        print(f'Epoch {epoch}, loss: {loss.item():.4f}')
+
+# 输出最终参数
+print("Learned weights:", model.linear.weight.data)
+print("Learned bias:", model.linear.bias.data)