软件测试丨PyTorch 简介

import torch import torch.nn as nn import torch.optim as optim # 生成数据 x = torch.randn(100, 1) y = 2 * x + 1 + 0.1 * torch.randn(100, 1) # 定义模型 model = nn.Linear(1, 1) # 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 for epoch in range(100): # 前向传播 y_pred = model(x) # 计算损失 loss = criterion(y_pred, y) # 反向传播 optimizer.zero_grad() loss.backward() optimizer.step() if (epoch+1) % 10 == 0: print(f'Epoch [{epoch+1}/100], Loss: {loss.item():.4f}') # 输出训练后的参数 print('模型参数:', model.weight.item(), model.bias.item())