dmlc--dgl
VoVAllen
91 行
3.9 KiB
Python
91 行
3.9 KiB
Python
import argparse
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import torch
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import torch.optim as optim
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from torchvision import datasets, transforms
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from model import Net
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def train(args, model, device, train_loader, optimizer, epoch):
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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data, target = data.to(device), target.to(device)
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optimizer.zero_grad()
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output = model(data)
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loss = model.margin_loss(output, target)
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loss.backward()
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optimizer.step()
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if batch_idx % args.log_interval == 0:
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print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
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epoch, batch_idx * len(data), len(train_loader.dataset),
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100. * batch_idx / len(train_loader), loss.item()))
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def test(args, model, device, test_loader):
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model.eval()
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test_loss = 0
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correct = 0
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with torch.no_grad():
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for data, target in test_loader:
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data, target = data.to(device), target.to(device)
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output = model(data)
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test_loss += model.margin_loss(output, target).item() # sum up batch loss
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pred = output.norm(dim=2).squeeze().max(1, keepdim=True)[1] # get the index of the max log-probability
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correct += pred.eq(target.view_as(pred)).sum().item()
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test_loss /= len(test_loader.dataset)
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print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
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test_loss, correct, len(test_loader.dataset),
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100. * correct / len(test_loader.dataset)))
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def main():
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# Training settings
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parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
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parser.add_argument('--batch-size', type=int, default=512, metavar='N',
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help='input batch size for training (default: 64)')
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parser.add_argument('--test-batch-size', type=int, default=512, metavar='N',
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help='input batch size for testing (default: 1000)')
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parser.add_argument('--epochs', type=int, default=10, metavar='N',
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help='number of epochs to train (default: 10)')
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parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
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help='learning rate (default: 0.01)')
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parser.add_argument('--no-cuda', action='store_true', default=False,
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help='disables CUDA training')
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parser.add_argument('--seed', type=int, default=1, metavar='S',
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help='random seed (default: 1)')
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parser.add_argument('--log-interval', type=int, default=10, metavar='N',
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help='how many batches to wait before logging training status')
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args = parser.parse_args()
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use_cuda = not args.no_cuda and torch.cuda.is_available()
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torch.manual_seed(args.seed)
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device = torch.device("cuda" if use_cuda else "cpu")
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kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
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train_loader = torch.utils.data.DataLoader(
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datasets.MNIST('../data', train=True, download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))
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])),
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batch_size=args.batch_size, shuffle=True, **kwargs)
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test_loader = torch.utils.data.DataLoader(
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datasets.MNIST('../data', train=False, transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))
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])),
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batch_size=args.test_batch_size, shuffle=True, **kwargs)
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model = Net(device=device).to(device)
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optimizer = optim.Adam(model.parameters(), lr=args.lr)
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for epoch in range(1, args.epochs + 1):
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train(args, model, device, train_loader, optimizer, epoch)
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test(args, model, device, test_loader)
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if __name__ == '__main__':
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main()
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