CNN-Renew

Pytorch/Project/ez0/noise.py

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+import torch
+import torch.nn as nn
+import numpy as np
+import matplotlib.pyplot as plt
+from torch import optim
+
+# x 张量
+x = torch.linspace(-10, 10, 100).unsqueeze(1)
+
+# 特征矩阵
+X = torch.cat([x, x**2, x**3, torch.sin(2*x)], dim=1)
+
+# 高斯噪声
+noise = torch.from_numpy(np.random.normal(0, 3, size=x.shape)).float()
+
+# 带噪声的 y
+y_noisy = 0.5 * x**3 - 2 * x**2 + 3*x + 5 + 4 * torch.sin(2*x) + noise
+
+# 可视化
+'''
+plt.scatter(x, y_noisy, label="Noisy data")
+plt.plot(x, 0.5 * x**3 - 2 * x**2 + 3*x + 5 + 4 * torch.sin(2*x), color='red', label="Original function")
+plt.legend()
+plt.show()
+'''
+ez = nn.Sequential(
+    nn.Linear(4, 8),
+    nn.ReLU(),
+    nn.Linear(8, 4),
+    nn.ReLU(),
+    nn.Linear(4, 1),
+)
+
+criterion = nn.MSELoss()
+optimizer = optim.Adam(ez.parameters(), lr=0.0065)
+
+for i in range(10000):
+    #向前传播
+    out = ez(X)
+    loss = criterion(out, y_noisy)
+    #反向传播
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    if i % 100 == 0:
+        print("Step:", i, "loss =", loss.item())
+
+with torch.no_grad():
+    y_pred = ez(X)
+
+plt.scatter(x.numpy(), y_noisy.numpy(), alpha=0.3, label="Noisy data")
+plt.plot(x.numpy(), y_pred.numpy(), color='red', label="NN prediction")
+plt.legend()
+plt.show()