【PyTorch】线性回归,eastfax

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

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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