【PyTorch】线性回归,eastfax（线性回归 pytorch）

文件名：【PyTorch】线性回归,eastfax 【PyTorch】线性回归

文章目录 1. 代码实现1.1 一元线性回归模型的训练 2. 代码解读2.1. tensorboardX2.1.1. tensorboardX的安装2.1.2. tensorboardX的使用

1. 代码实现

波士顿房价数据集下载

1.1 一元线性回归模型的训练 import numpy as npimport torchimport torch.nn as nnfrom torch.utils.data import TensorDataset, DataLoader, random_splitfrom tensorboardX import SummaryWriterdevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')batch_size = 2num_epochs = 200writer = SummaryWriter()model = nn.Linear(1, 1).to(device)nn.init.normal_(model.weight, mean=0, std=0.01)nn.init.constant_(model.bias, 0)criterion = nn.MSELoss()optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)data = np.load('dataset/boston_housing/boston_housing.npz')X = torch.tensor(data['x'][:, 0].reshape(-1, len(model.weight)), dtype=torch.float, device=device)y = torch.tensor(data['y'].reshape(-1, 1), dtype=torch.float, device=device)dataset = TensorDataset(X, y)dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)for epoch in range(num_epochs):for _X, _y in dataloader:_X, _y = _X.to(device), _y.to(device)loss = criterion(model(_X), _y)optimizer.zero_grad()loss.backward()optimizer.step()loss = criterion(model(X), y)torch.save(model.state_dict(), 'model/linearRegression.pt')model.load_state_dict(torch.load('model/linearRegression.pt'))writer.add_scalar('Loss/train', loss, epoch)writer.add_scalar('W/train', model.weight, epoch)writer.add_scalar('b/train', model.bias, epoch)writer.close() 2. 代码解读 2.1. tensorboardX

tensorboardX是一种能将训练过程可视化的工具

2.1.1. tensorboardX的安装

安装命令：

pip install tensorboardX

VSCode集成了TensorBoard支持，不过事先要安装torch-tb-profiler，安装命令：

pip install torch-tb-profiler

安装完成后，在Python源文件中tensorboardX模块导入处，点击“启动TensorBoard会话”按钮，然后选择运行事件所在目录，默认选择当前目录即可，tensorboard会自动在当前目录查找运行事件，由此即可启动TensorBoard。 此外，也可以通过以下命令在浏览器查看tensorboard可视化结果：

# logdir为运行事件所在目录> tensorboard logdir=runsTensorFlow installation not found - running with reduced feature set.I1202 20:37:50.824767 15412 plugin.py:429] Monitor runs beginServing TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_allTensorBoard 2.14.0 at http://localhost:6006/ (Press CTRL+C to quit)# 手动打开命令输出提供的本地服务器地址，如http://localhost:6006/ 2.1.2. tensorboardX的使用 直接创建对象 from tensorboardX import SummaryWriterwriter = SummaryWriter()# writer.add_scalar()：添加监控变量writer.close() 使用上下文管理器 from tensorboardX import SummaryWriterwith SummaryWriter() as writer:# writer.add_scalar()：添加监控变量