241005

linear regression/m1test.py

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+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()