【colab pytorch】使用tensorboardcolab可视化

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils, datasets

!pip install tensorboardcolab
from tensorboardcolab import TensorBoardColab

class Network(nn.Module):
  def __init__(self):
    super(Network, self).__init__()
    self.conv1 = nn.Conv2d(1, 20, 5, 1)
    self.conv2 = nn.Conv2d(20, 50, 5, 1)
    self.fc1 = nn.Linear(4*4*50, 500)
    self.fc2 = nn.Linear(500, 10)
   
  def forward(self, x):
    x = F.relu(self.conv1(x))
    x = F.max_pool2d(x, 2, 2)
    x = F.relu(self.conv2(x))
    x = F.max_pool2d(x, 2, 2)
    x = x.view(-1, 4*4*50)
    x = F.relu(self.fc1(x))
    x = self.fc2(x)
    return F.log_softmax(x, dim=1)
    

class Config:  
  def __init__(self, **kwargs):
    for key, value in kwargs.items():
      setattr(self, key, value)


model_config = Config(
    cuda = True if torch.cuda.is_available() else False,
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu"),
    seed = 2,
    lr = 0.01,
    epochs = 4,
    save_model = False,
    batch_size = 32,
    log_interval = 100
)
    
class Trainer:
  
  def __init__(self, config):
    
    self.cuda = config.cuda
    self.device = config.device
    self.seed = config.seed
    self.lr = config.lr
    self.epochs = config.epochs
    self.save_model = config.save_model
    self.batch_size = config.batch_size
    self.log_interval = config.log_interval
    
    self.globaliter = 0
    self.tb = TensorBoardColab()
    
    torch.manual_seed(self.seed)

    kwargs = {'num_workers': 1, 'pin_memory': True} if self.cuda else {}

    self.train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=True, download=True,
                     transform=transforms.Compose([
                         transforms.ToTensor(),
                         transforms.Normalize((MNIST_MEAN,), (MNIST_STD,))
                     ])),
        batch_size=self.batch_size, shuffle=True, **kwargs)

    self.test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=False, transform=transforms.Compose([
                             transforms.ToTensor(),
                             transforms.Normalize((MNIST_MEAN,), (MNIST_STD,))
                         ])),
        batch_size=self.batch_size, shuffle=True, **kwargs)


    self.model = Network().to(self.device)
    self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
      
      
  def train(self, epoch):
  
    self.model.train()
    for batch_idx, (data, target) in enumerate(self.train_loader):
      
      self.globaliter += 1
      data, target = data.to(self.device), target.to(self.device)

      self.optimizer.zero_grad()
      predictions = self.model(data)

      loss = F.nll_loss(predictions, target)
      loss.backward()
      self.optimizer.step()

      if batch_idx % self.log_interval == 0:
        print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                  epoch, batch_idx * len(data), len(self.train_loader.dataset),
                  100. * batch_idx / len(self.train_loader), loss.item()))
        self.tb.save_value('Train Loss', 'train_loss', self.globaliter, loss.item())
        
        
  def test(self, epoch):
    self.model.eval()
    test_loss = 0
    correct = 0

    with torch.no_grad():
      for data, target in self.test_loader:
        data, target = data.to(self.device), target.to(self.device)
        predictions = self.model(data)

        test_loss += F.nll_loss(predictions, target, reduction='sum').item()
        prediction = predictions.argmax(dim=1, keepdim=True)
        correct += prediction.eq(target.view_as(prediction)).sum().item()

      test_loss /= len(self.test_loader.dataset)
      accuracy = 100. * correct / len(self.test_loader.dataset)

      print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
          test_loss, correct, len(self.test_loader.dataset), accuracy))

      
def main():
  
  trainer = Trainer(model_config)
  
  for epoch in range(1, trainer.epochs + 1):
      trainer.train(epoch)
      trainer.test(epoch)
      trainer.tb.flush_line('train_loss')

  if (trainer.save_model):
      torch.save(trainer.model.state_dict(),"mnist_cnn.pt")

  

main()

Wait for 8 seconds...
TensorBoard link:
http://db797eee.ngrok.io
Train Epoch: 1 [0/60000 (0%)]    Loss: 2.320306
Train Epoch: 1 [3200/60000 (5%)]    Loss: 0.881239
Train Epoch: 1 [6400/60000 (11%)]    Loss: 0.013655
Train Epoch: 1 [9600/60000 (16%)]    Loss: 0.013620
Train Epoch: 1 [12800/60000 (21%)]    Loss: 0.225101
Train Epoch: 1 [16000/60000 (27%)]    Loss: 0.248218
Train Epoch: 1 [19200/60000 (32%)]    Loss: 0.207354
Train Epoch: 1 [22400/60000 (37%)]    Loss: 0.139395
Train Epoch: 1 [25600/60000 (43%)]    Loss: 0.206405
Train Epoch: 1 [28800/60000 (48%)]    Loss: 0.090241
Train Epoch: 1 [32000/60000 (53%)]    Loss: 0.216764
Train Epoch: 1 [35200/60000 (59%)]    Loss: 0.295801
Train Epoch: 1 [38400/60000 (64%)]    Loss: 0.021000
Train Epoch: 1 [41600/60000 (69%)]    Loss: 0.050552
Train Epoch: 1 [44800/60000 (75%)]    Loss: 0.238085
Train Epoch: 1 [48000/60000 (80%)]    Loss: 0.298676
Train Epoch: 1 [51200/60000 (85%)]    Loss: 0.301436
Train Epoch: 1 [54400/60000 (91%)]    Loss: 0.271787
Train Epoch: 1 [57600/60000 (96%)]    Loss: 0.019811

Test set: Average loss: 0.1088, Accuracy: 9677/10000 (97%)

Train Epoch: 2 [0/60000 (0%)]    Loss: 0.036418
Train Epoch: 2 [3200/60000 (5%)]    Loss: 0.024196
Train Epoch: 2 [6400/60000 (11%)]    Loss: 0.029856
Train Epoch: 2 [9600/60000 (16%)]    Loss: 0.084013
Train Epoch: 2 [12800/60000 (21%)]    Loss: 0.345446
Train Epoch: 2 [16000/60000 (27%)]    Loss: 0.453756
Train Epoch: 2 [19200/60000 (32%)]    Loss: 0.409682
Train Epoch: 2 [22400/60000 (37%)]    Loss: 0.159656
Train Epoch: 2 [25600/60000 (43%)]    Loss: 0.009557
Train Epoch: 2 [28800/60000 (48%)]    Loss: 0.282826
Train Epoch: 2 [32000/60000 (53%)]    Loss: 0.047159
Train Epoch: 2 [35200/60000 (59%)]    Loss: 0.379264
Train Epoch: 2 [38400/60000 (64%)]    Loss: 0.043181
Train Epoch: 2 [41600/60000 (69%)]    Loss: 0.486660
Train Epoch: 2 [44800/60000 (75%)]    Loss: 0.108486
Train Epoch: 2 [48000/60000 (80%)]    Loss: 0.242821
Train Epoch: 2 [51200/60000 (85%)]    Loss: 0.218120
Train Epoch: 2 [54400/60000 (91%)]    Loss: 0.381496
Train Epoch: 2 [57600/60000 (96%)]    Loss: 0.134828

Test set: Average loss: 0.1861, Accuracy: 9496/10000 (95%)

Train Epoch: 3 [0/60000 (0%)]    Loss: 0.081437
Train Epoch: 3 [3200/60000 (5%)]    Loss: 0.121195
Train Epoch: 3 [6400/60000 (11%)]    Loss: 0.054902
Train Epoch: 3 [9600/60000 (16%)]    Loss: 0.031254
Train Epoch: 3 [12800/60000 (21%)]    Loss: 0.036273
Train Epoch: 3 [16000/60000 (27%)]    Loss: 0.162744
Train Epoch: 3 [19200/60000 (32%)]    Loss: 0.028073
Train Epoch: 3 [22400/60000 (37%)]    Loss: 0.114689
Train Epoch: 3 [25600/60000 (43%)]    Loss: 0.139724
Train Epoch: 3 [28800/60000 (48%)]    Loss: 0.353534
Train Epoch: 3 [32000/60000 (53%)]    Loss: 0.001959
Train Epoch: 3 [35200/60000 (59%)]    Loss: 0.117742
Train Epoch: 3 [38400/60000 (64%)]    Loss: 0.024078
Train Epoch: 3 [41600/60000 (69%)]    Loss: 0.063214
Train Epoch: 3 [44800/60000 (75%)]    Loss: 0.068128
Train Epoch: 3 [48000/60000 (80%)]    Loss: 0.055476
Train Epoch: 3 [51200/60000 (85%)]    Loss: 0.025761
Train Epoch: 3 [54400/60000 (91%)]    Loss: 0.490388
Train Epoch: 3 [57600/60000 (96%)]    Loss: 0.275244

Test set: Average loss: 0.1570, Accuracy: 9594/10000 (96%)

Train Epoch: 4 [0/60000 (0%)]    Loss: 0.150237
Train Epoch: 4 [3200/60000 (5%)]    Loss: 0.049188
Train Epoch: 4 [6400/60000 (11%)]    Loss: 0.008692
Train Epoch: 4 [9600/60000 (16%)]    Loss: 0.061360
Train Epoch: 4 [12800/60000 (21%)]    Loss: 0.004389
Train Epoch: 4 [16000/60000 (27%)]    Loss: 0.027968
Train Epoch: 4 [19200/60000 (32%)]    Loss: 0.075881
Train Epoch: 4 [22400/60000 (37%)]    Loss: 0.074000
Train Epoch: 4 [25600/60000 (43%)]    Loss: 0.069731
Train Epoch: 4 [28800/60000 (48%)]    Loss: 0.330368
Train Epoch: 4 [32000/60000 (53%)]    Loss: 0.393174
Train Epoch: 4 [35200/60000 (59%)]    Loss: 0.318519
Train Epoch: 4 [38400/60000 (64%)]    Loss: 0.164669
Train Epoch: 4 [41600/60000 (69%)]    Loss: 0.161486
Train Epoch: 4 [44800/60000 (75%)]    Loss: 0.017525
Train Epoch: 4 [48000/60000 (80%)]    Loss: 0.104918
Train Epoch: 4 [51200/60000 (85%)]    Loss: 0.000450
Train Epoch: 4 [54400/60000 (91%)]    Loss: 0.128227
Train Epoch: 4 [57600/60000 (96%)]    Loss: 0.005374

Test set: Average loss: 0.1227, Accuracy: 9717/10000 (97%)