conda activate ps pip install visdom

python -m visdom.server

from visdom import Visdom# 创建一个实例viz=Visdom()# 创建一个直线，再把最新数据添加到直线上# y x二维两个轴，win 创建一个小窗口，不指定就默认为大窗口，opts其他信息比如名称viz.line([1,2,3,4],[1,2,3,4],win="train_loss",opts=dict(title='train_loss'))# 更一般的情况,因为下面y x数据不存在，只是示例#  append 添加到原来的后面，不然全部覆盖掉# viz.line([loss.item()],[global_step],win="train_loss",update='append')

from visdom import Visdomviz=Visdom()viz.line([[1,2],[5,6]],[1,2],win="loss_acc",opts=dict(title='train loss & acc',legend=['loss','acc']))

from visdom import Visdomviz=Visdom()# data 是一个batchviz.image(data.view(-1,1,28,28),win='x')viz.text(str(pred.datach().cpu().numpy()),win='pred',opts=dict(title='pred'))

import  torchimport  torch.nn as nnimport  torch.nn.functional as Fimport  torch.optim as optimfrom    torchvision import datasets, transformsfrom visdom import Visdombatch_size=200learning_rate=0.01epochs=10train_loader = torch.utils.data.DataLoader(    datasets.MNIST('../data', train=True, download=True,                   transform=transforms.Compose([                       transforms.ToTensor(),                       # transforms.Normalize((0.1307,), (0.3081,))                   ])),    batch_size=batch_size, shuffle=True)test_loader = torch.utils.data.DataLoader(    datasets.MNIST('../data', train=False, transform=transforms.Compose([        transforms.ToTensor(),        # transforms.Normalize((0.1307,), (0.3081,))    ])),    batch_size=batch_size, shuffle=True)class MLP(nn.Module):    def __init__(self):        super(MLP, self).__init__()        self.model = nn.Sequential(            nn.Linear(784, 200),            nn.LeakyReLU(inplace=True),            nn.Linear(200, 200),            nn.LeakyReLU(inplace=True),            nn.Linear(200, 10),            nn.LeakyReLU(inplace=True),        )    def forward(self, x):        x = self.model(x)        return xdevice = torch.device('cuda:0')net = MLP().to(device)optimizer = optim.SGD(net.parameters(), lr=learning_rate)criteon = nn.CrossEntropyLoss().to(device)viz = Visdom()viz.line([0.], [0.], win='train_loss', opts=dict(title='train loss'))viz.line([[0.0, 0.0]], [0.], win='test', opts=dict(title='test loss&acc.',                                                   legend=['loss', 'acc.']))global_step = 0for epoch in range(epochs):    for batch_idx, (data, target) in enumerate(train_loader):        data = data.view(-1, 28*28)        data, target = data.to(device), target.cuda()        logits = net(data)        loss = criteon(logits, target)        optimizer.zero_grad()        loss.backward()        # print(w1.grad.norm(), w2.grad.norm())        optimizer.step()        global_step += 1        viz.line([loss.item()], [global_step], win='train_loss', update='append')        if batch_idx % 100 == 0:            print('Train Epoch: {} [{}/{} ({:.0f}%)]/tLoss: {:.6f}'.format(                epoch, batch_idx * len(data), len(train_loader.dataset),                       100. * batch_idx / len(train_loader), loss.item()))    test_loss = 0    correct = 0    for data, target in test_loader:        data = data.view(-1, 28 * 28)        data, target = data.to(device), target.cuda()        logits = net(data)        test_loss += criteon(logits, target).item()        pred = logits.argmax(dim=1)        correct += pred.eq(target).float().sum().item()    viz.line([[test_loss, correct / len(test_loader.dataset)]],             [global_step], win='test', update='append')    viz.images(data.view(-1, 1, 28, 28), win='x')    viz.text(str(pred.detach().cpu().numpy()), win='pred',             opts=dict(title='pred'))    test_loss /= len(test_loader.dataset)    print('/nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)/n'.format(        test_loss, correct, len(test_loader.dataset),        100. * correct / len(test_loader.dataset)))