241005

linear regression/1_cuda.py

@@ -39,7 +39,7 @@ def gradient_descent_runner(points, starting_b, starting_w, learningRate, num_it
 
 def run():
     points_np = np.genfromtxt("data1.csv", delimiter=',').astype(np.float32)
-    points = torch.tensor(points_np, device='cuda:5')
+    points = torch.tensor(points_np, device='cuda')
     learning_rate = 0.0001
     initial_b = 0.0
     initial_w = 0.0

linear regression/m1.py

@@ -0,0 +1,76 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, TensorDataset
+import matplotlib.pyplot as plt
+
+
+# 检查是否支持MPS（Apple Metal Performance Shaders）
+device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+print(f"使用设备: {device}")
+
+# 生成示例数据
+# y = 3x + 2 + 噪声
+torch.manual_seed(0)
+X = torch.linspace(-10, 10, steps=100).reshape(-1, 1)
+y = 3 * X + 2 + torch.randn(X.size()) * 2
+
+# 创建数据集和数据加载器
+dataset = TensorDataset(X, y)
+dataloader = DataLoader(dataset, batch_size=10, shuffle=True)
+
+
+# 定义线性回归模型
+class LinearRegressionModel(nn.Module):
+    def __init__(self):
+        super(LinearRegressionModel, self).__init__()
+        self.linear = nn.Linear(1, 1)  # 输入和输出都是1维
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+# 实例化模型并移动到设备
+model = LinearRegressionModel().to(device)
+
+# 定义损失函数和优化器
+criterion = nn.MSELoss()
+optimizer = optim.SGD(model.parameters(), lr=0.01)
+
+# 训练模型
+num_epochs = 100
+for epoch in range(num_epochs):
+    for batch_X, batch_y in dataloader:
+        batch_X = batch_X.to(device)
+        batch_y = batch_y.to(device)
+
+        # 前向传播
+        outputs = model(batch_X)
+        loss = criterion(outputs, batch_y)
+
+        # 反向传播和优化
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+    if (epoch + 1) % 10 == 0:
+        print(f"Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}")
+
+# 保存整个模型
+torch.save(model.state_dict(), 'm1.pth')
+print("整个模型已保存为 m1.pth")
+
+# 评估模型
+model.eval()
+with torch.no_grad():
+    X_test = torch.linspace(-10, 10, steps=100).reshape(-1, 1).to(device)
+    y_pred = model(X_test).cpu()
+
+
+plt.scatter(X.numpy(), y.numpy(), label='真实数据')
+plt.plot(X_test.cpu().numpy(), y_pred.numpy(), color='red', label='预测线')
+plt.legend()
+plt.xlabel('X')
+plt.ylabel('y')
+plt.title('线性回归结果')
+plt.show()

linear regression/m1test.py

@@ -0,0 +1,56 @@
+import torch
+import torch.nn as nn
+import matplotlib.pyplot as plt
+
+
+# 定义线性回归模型结构
+class LinearRegressionModel(nn.Module):
+    def __init__(self):
+        super(LinearRegressionModel, self).__init__()
+        self.linear = nn.Linear(1, 1)  # 输入和输出都是1维
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+def main():
+    # 检查是否支持MPS（Apple Metal Performance Shaders）
+    device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+    print(f"使用设备: {device}")
+
+    # 实例化模型并加载保存的模型参数
+    model = LinearRegressionModel().to(device)
+    model.load_state_dict(torch.load('m1.pth'))
+    with open('m1.pth', 'rb') as f:
+        f.seek(0, 2)
+        size = f.tell()
+    print(f"模型文件大小: {size} 字节")
+    model.eval()
+    # 输出模型大小
+    model_size = sum(p.numel() for p in model.parameters())
+    print(f"模型大小: {model_size} 个参数")
+    print("模型参数已加载")
+
+    # 生成测试数据
+    X_test = torch.linspace(-10, 10, steps=100).reshape(-1, 1).to(device)
+
+    # 使用加载的模型进行预测
+    with torch.no_grad():
+        y_pred = model(X_test).cpu()
+
+    # 将测试数据移至CPU并转换为NumPy数组
+    X_test_numpy = X_test.cpu().numpy()
+    y_pred_numpy = y_pred.numpy()
+
+    # 可视化预测结果
+    plt.scatter(X_test_numpy, 3 * X_test_numpy + 2, label='真实线性关系', color='blue')
+    plt.plot(X_test_numpy, y_pred_numpy, color='red', label='模型预测线')
+    plt.legend()
+    plt.xlabel('X')
+    plt.ylabel('y')
+    plt.title('加载模型后的线性回归预测结果')
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()