41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
# 生成数据
|
|
np.random.seed(0)
|
|
x = np.random.rand(100, 1) * 10 # 生成100个在0到10之间的随机数
|
|
y = 3.5 * x + 2 + np.random.randn(100, 1) * 2 # 真实的线性关系加上一些噪声
|
|
|
|
# 将numpy数组转换为torch张量
|
|
x_tensor = torch.from_numpy(x).float()
|
|
y_tensor = torch.from_numpy(y).float()
|
|
|
|
|
|
model = nn.Linear(in_features=1, out_features=1, bias=True)
|
|
|
|
criterion = nn.MSELoss()
|
|
optimizer = optim.SGD(model.parameters(), lr=0.01)
|
|
|
|
epochs = 100
|
|
for epoch in range(epochs):
|
|
# 前向传播
|
|
outputs = model(x_tensor)
|
|
loss = criterion(outputs, y_tensor)
|
|
|
|
# 反向传播和优化
|
|
optimizer.zero_grad()
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
if (epoch + 1) % 10 == 0:
|
|
print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss.item():.4f}')
|
|
|
|
# 可视化原始数据和拟合结果
|
|
predicted = model(x_tensor).detach().numpy() # detach()用于从计算图中移除张量，避免未来的梯度计算干扰。
|
|
plt.scatter(x, y, label='Original data')
|
|
plt.plot(x, predicted, color='red', label='Fitted line')
|
|
plt.legend()
|
|
plt.show() |