|
-
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- from torch.autograd import Variable
-
- from torchvision import datasets, transforms
-
- # Training settings
- batch_size = 64
-
- # MNIST Dataset
- dataset_path = "../data/mnist"
- train_dataset = datasets.MNIST(root=dataset_path,
- train=True,
- transform=transforms.ToTensor(),
- download=True)
-
- test_dataset = datasets.MNIST(root=dataset_path,
- train=False,
- transform=transforms.ToTensor())
-
- # Data Loader (Input Pipeline)
- train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
- batch_size=batch_size,
- shuffle=True)
-
- test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
- batch_size=batch_size,
- shuffle=False)
-
-
- # Define the network
- class Net_CNN(nn.Module):
- def __init__(self):
- super(Net_CNN, self).__init__()
- self.conv1 = nn.Conv2d(1, 6, 5)
- self.conv2 = nn.Conv2d(6, 16, 5)
- self.fc1 = nn.Linear(16*4*4, 120)
- self.fc2 = nn.Linear(120, 84)
- self.fc3 = nn.Linear(84, 10)
-
- def forward(self, x):
- x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
- x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
- x = x.view(x.size()[0], -1)
- x = F.relu(self.fc1(x))
- x = F.relu(self.fc2(x))
- x = self.fc3(x)
- return x
-
-
- # define optimizer & criterion
- model = Net_CNN()
- optim = torch.optim.Adam(model.parameters(), 0.01)
- criterion = nn.CrossEntropyLoss()
-
-
- # train the network
- for e in range(100):
- # train
- model.train()
- for batch_idx, (data, target) in enumerate(train_loader):
- data, target = Variable(data), Variable(target)
-
- out = model(data)
- loss = criterion(out, target)
- optim.zero_grad()
- loss.backward()
- optim.step()
-
- if batch_idx % 100 == 0:
- pred = out.data.max(1, keepdim=True)[1]
- c = float(pred.eq(target.data.view_as(pred)).cpu().sum() ) /out.size(0)
-
- print("epoch: %5d, loss: %f, acc: %f" %
- ( e +1, loss.data.item(), c))
-
- # test
- model.eval()
-
- test_loss = 0.0
- correct = 0.0
- for data, target in test_loader:
- data, target = Variable(data), Variable(target)
- output = model(data)
-
- # sum up batch loss
- test_loss += criterion(output, target).item()
-
- # get the index of the max
- pred = output.data.max(1, keepdim=True)[1]
- correct += float(pred.eq(target.data.view_as(pred)).cpu().sum())
-
- test_loss /= len(test_loader.dataset)
- print("\nTest set: Average loss: %.4f, Accuracy: %6d/%6d (%4.2f %%)\n" %
- (test_loss,
- correct, len(test_loader.dataset),
- 100.0*correct / len(test_loader.dataset)) )
|
