新建仓库维护数据预测项目

FC_ML_NN/NN_Multi_Polynomial_Test.py

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+import torch
+import torch.nn as nn
+
+#多元多项式拟合
+
+# 数据生成 (x1, x2 -> y)
+x1 = torch.rand(100, 1) * 4 - 2  # [-2, 2]
+x2 = torch.rand(100, 1) * 3 - 1  # [-1, 2]
+y_true = 2.7*x1 + 3.0*x2 + 5.0*x1*x2 - 1.5*x1**2*x2  # 真实多项式
+
+# 特征工程
+def make_features(x1, x2, degree=3):
+    features = [torch.ones_like(x1)]
+    for d in range(1, degree+1):
+        for i in range(d+1):
+            features.append((x1**i) * (x2**(d-i)))
+    return torch.cat(features, dim=1)
+x_poly = make_features(x1, x2, degree=3)
+
+# 模型训练
+model = nn.Sequential(nn.Linear(10, 1))  # 3次多项式共10项
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=0.1)
+for epoch in range(2000):
+    y_pred = model(x_poly)
+    loss = nn.MSELoss()(y_pred, y_true)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    if epoch % 500 == 0:
+        print(f'Epoch {epoch}, Loss: {loss.item():.4f}')