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%)

核心就是标红的地方。

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