【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%)
核心就是标红的地方。
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