77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
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()
|