Pytorch-Visdom可视化工具

Visdom相比TensorBoardX，更简洁方便一些（例如对image数据的可视化可以直接使用Tensor，而不必转到cpu上再转为numpy数据），刷新率也更快。

1.安装visdom

pip install visdom

2.开启监听进程

visdom本质上是一个web服务器，开启web服务器之后程序才能向服务器丢数据，web服务器把数据渲染到网页中去。

python -m visdom.server

但是很不幸报错了！ERROR:root:Error [Errno 2] No such file or directory while downloading https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_SVG，所以从头再来，先pip uninstall visdom卸掉visdom，再手动安装。

从网站下载visdom源文件https://github.com/facebookresearch/visdom并解压
command进入visdom所在文件目录，比如我的是cd F:\Chrome_Download\visdom-master
进入目录后执行pip install -e .
执行成功后，退回到用户目录，重新执行上面的python -m visidom.server
然后又报错了，一直提示Downloading scripts, this may take a little while，解决方案见https://github.com/casuallyName/document-sharing/tree/master/static
直到如图所示即启动成功

3.访问

用chrome浏览器访问url连接：http://localhost:8097

没想到又又报错了，页面加载失败（蓝色空白页面如下）

在visdom安装目录下（我的是F:\Anaconda\Lib\site-packages\visdom），将static文件夹换掉，下载地址为

链接：https://pan.baidu.com/s/1fZb-3GSZvk0kRpL73MBgcw
提取码：np04

直到出现横条框即visdom可用。

4.可视化训练

在之前定义网络结构（参考上一节）的基础上加上Visdom可视化。

在训练-测试的迭代过程之前，定义两条曲线，在训练-测试的过程中再不断填充点以实现曲线随着训练动态增长：

1 from visdom import Visdom
2 viz = Visdom()
3 viz.line([0.], [0.], win='train_loss', opts=dict(title='train loss'))
4 viz.line([[0.0, 0.0]], [0.], win='test', opts=dict(title='test loss&acc.',legend=['loss', 'acc.']))

第二行Visdom(env="xxx")参数env来设置环境窗口的名称，这里什么都没传，在默认的main窗口下。

viz.line的前两个参数是曲线的Y和X的坐标（前面是纵轴后面才是横轴），设置了不同的win参数，它们就会在不同的窗口中展示，

第四行定义的是测试集的loss和acc两条曲线，所以在X等于0时Y给了两个初始值。

为了知道训练了多少个batch，设置一个全局的计数器：

1 global_step = 0

在每个batch训练完后，为训练曲线添加点，来让曲线实时增长：

1 global_step += 1
2 viz.line([loss.item()], [global_step], win='train_loss', update='append')

这里用win参数来选择是哪条曲线，用update='append'的方式添加曲线的增长点，前面是Y坐标，后面是X坐标。

在每次测试结束后，并在另外两个窗口（用win参数设置）中展示图像（.images）和真实值（文本用.text）：

1 viz.line([[test_loss, correct / len(test_loader.dataset)]],
2              [global_step], win='test', update='append')
3 viz.images(data.view(-1, 1, 28, 28), win='x')
4 viz.text(str(pred.detach().numpy()), win='pred',
5              opts=dict(title='pred'))

附上完整代码：

  1 import  torch
  2 import  torch.nn as nn
  3 import  torch.nn.functional as F
  4 import  torch.optim as optim
  5 from   torchvision import datasets, transforms
  6 from visdom import Visdom
  7 
  8 #超参数
  9 batch_size=200
 10 learning_rate=0.01
 11 epochs=10
 12 
 13 #获取训练数据
 14 train_loader = torch.utils.data.DataLoader(
 15     datasets.MNIST('../data', train=True, download=True,          #train=True则得到的是训练集
 16                    transform=transforms.Compose([                 #transform进行数据预处理
 17                        transforms.ToTensor(),                     #转成Tensor类型的数据
 18                        #transforms.Normalize((0.1307,), (0.3081,)) #进行数据标准化(减去均值除以方差)
 19                    ])),
 20     batch_size=batch_size, shuffle=True)                          #按batch_size分出一个batch维度在最前面,shuffle=True打乱顺序
 21 
 22 #获取测试数据
 23 test_loader = torch.utils.data.DataLoader(
 24     datasets.MNIST('../data', train=False, transform=transforms.Compose([
 25         transforms.ToTensor(),
 26         #transforms.Normalize((0.1307,), (0.3081,))
 27     ])),
 28     batch_size=batch_size, shuffle=True)
 29 
 30 
 31 class MLP(nn.Module):
 32 
 33     def __init__(self):
 34         super(MLP, self).__init__()
 35         
 36         self.model = nn.Sequential(         #定义网络的每一层,
 37             nn.Linear(784, 200),
 38             nn.ReLU(inplace=True),
 39             nn.Linear(200, 200),
 40             nn.ReLU(inplace=True),
 41             nn.Linear(200, 10),
 42             nn.ReLU(inplace=True),
 43         )
 44 
 45     def forward(self, x):
 46         x = self.model(x)
 47         return x    
 48 
 49 
 50 net = MLP()
 51 #定义sgd优化器,指明优化参数、学习率，net.parameters()得到这个类所定义的网络的参数[[w1,b1,w2,b2,...]
 52 optimizer = optim.SGD(net.parameters(), lr=learning_rate)
 53 criteon = nn.CrossEntropyLoss()
 54 
 55 viz = Visdom()
 56 viz.line([0.], [0.], win='train_loss', opts=dict(title='train loss'))
 57 viz.line([[0.0, 0.0]], [0.], win='test', opts=dict(title='test loss&acc.',
 58                                                    legend=['loss', 'acc.%']))
 59 global_step = 0
 60 
 61 
 62 for epoch in range(epochs):
 63 
 64     for batch_idx, (data, target) in enumerate(train_loader):
 65         data = data.view(-1, 28*28)          #将二维的图片数据摊平[样本数,784]
 66 
 67         logits = net(data)                   #前向传播
 68         loss = criteon(logits, target)       #nn.CrossEntropyLoss()自带Softmax
 69 
 70         optimizer.zero_grad()                #梯度信息清空   
 71         loss.backward()                      #反向传播获取梯度
 72         optimizer.step()                     #优化器更新
 73 
 74         global_step += 1
 75         viz.line([loss.item()], [global_step], win='train_loss', update='append')
 76 
 77     
 78         if batch_idx % 100 == 0:             #每100个batch输出一次信息
 79             print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
 80                 epoch, batch_idx * len(data), len(train_loader.dataset),
 81                        100. * batch_idx / len(train_loader), loss.item()))
 82 
 83 
 84     test_loss = 0
 85     correct = 0                                         #correct记录正确分类的样本数
 86     for data, target in test_loader:
 87         data = data.view(-1, 28 * 28)
 88         logits = net(data)
 89         test_loss += criteon(logits, target).item()     #其实就是criteon(logits, target)的值，标量
 90         
 91         pred = logits.data.max(dim=1)[1]                #也可以写成pred=logits.argmax(dim=1)
 92         correct += pred.eq(target.data).sum()
 93 
 94 
 95     viz.line([[test_loss, 100.* correct / len(test_loader.dataset)]],
 96              [global_step], win='test', update='append')
 97     viz.images(data.view(-1, 1, 28, 28), win='x')
 98     viz.text(str(pred.detach().numpy()), win='pred',
 99              opts=dict(title='pred'))
100 
101 
102     test_loss /= len(test_loader.dataset)
103     print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
104         test_loss, correct, len(test_loader.dataset),
105         100. * correct / len(test_loader.dataset)))

Train Epoch: 0 [0/60000 (0%)] Loss: 2.301824
Train Epoch: 0 [20000/60000 (33%)] Loss: 2.285871
Train Epoch: 0 [40000/60000 (67%)] Loss: 2.262092

Test set: Average loss: 0.0112, Accuracy: 3499/10000 (35%)

Train Epoch: 1 [0/60000 (0%)] Loss: 2.226518
Train Epoch: 1 [20000/60000 (33%)] Loss: 2.188961
Train Epoch: 1 [40000/60000 (67%)] Loss: 2.087539

Test set: Average loss: 0.0101, Accuracy: 3653/10000 (37%)

Train Epoch: 2 [0/60000 (0%)] Loss: 1.965714
Train Epoch: 2 [20000/60000 (33%)] Loss: 1.886761
Train Epoch: 2 [40000/60000 (67%)] Loss: 1.871282

Test set: Average loss: 0.0088, Accuracy: 4404/10000 (44%)

Train Epoch: 3 [0/60000 (0%)] Loss: 1.822776
Train Epoch: 3 [20000/60000 (33%)] Loss: 1.687571
Train Epoch: 3 [40000/60000 (67%)] Loss: 1.720948

Test set: Average loss: 0.0079, Accuracy: 4717/10000 (47%)

Train Epoch: 4 [0/60000 (0%)] Loss: 1.589682
Train Epoch: 4 [20000/60000 (33%)] Loss: 1.544680
Train Epoch: 4 [40000/60000 (67%)] Loss: 1.413445

Test set: Average loss: 0.0074, Accuracy: 4807/10000 (48%)

Train Epoch: 5 [0/60000 (0%)] Loss: 1.410685
Train Epoch: 5 [20000/60000 (33%)] Loss: 1.442557
Train Epoch: 5 [40000/60000 (67%)] Loss: 1.318121

Test set: Average loss: 0.0067, Accuracy: 5742/10000 (57%)

Train Epoch: 6 [0/60000 (0%)] Loss: 1.244786
Train Epoch: 6 [20000/60000 (33%)] Loss: 1.322500
Train Epoch: 6 [40000/60000 (67%)] Loss: 1.340830

Test set: Average loss: 0.0059, Accuracy: 6304/10000 (63%)

Train Epoch: 7 [0/60000 (0%)] Loss: 1.295525
Train Epoch: 7 [20000/60000 (33%)] Loss: 1.222254
Train Epoch: 7 [40000/60000 (67%)] Loss: 1.070692

Test set: Average loss: 0.0041, Accuracy: 7704/10000 (77%)

Train Epoch: 8 [0/60000 (0%)] Loss: 0.833216
Train Epoch: 8 [20000/60000 (33%)] Loss: 0.719662
Train Epoch: 8 [40000/60000 (67%)] Loss: 0.654462

Test set: Average loss: 0.0028, Accuracy: 8470/10000 (85%)

Train Epoch: 9 [0/60000 (0%)] Loss: 0.497108
Train Epoch: 9 [20000/60000 (33%)] Loss: 0.509768
Train Epoch: 9 [40000/60000 (67%)] Loss: 0.493004

Test set: Average loss: 0.0023, Accuracy: 8681/10000 (87%)

Tip：一开始viz.images()那一句图片没有显示，需要把第18和26行的代码注释掉，显示数据的时候不需要标准化。

posted @ 2020-07-12 19:45 最咸的鱼阅读(2886) 评论(0) 编辑收藏举报

刷新页面返回顶部