wehub-resource-sync
69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
import torch
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from torch import nn
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import torch.nn.functional as F
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from torch.utils.data import DataLoader
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from torchvision import datasets, transforms
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class Flatten(nn.Module):
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def forward(self, x): return x.view(x.size(0), -1)
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class Net(nn.Sequential):
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def __init__(self):
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super().__init__(
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nn.Conv2d(1, 32, 3, 1), nn.ReLU(),
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nn.Conv2d(32, 64, 3, 1), nn.MaxPool2d(2), nn.Dropout2d(0.25),
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Flatten(), nn.Linear(9216, 128), nn.ReLU(), nn.Dropout2d(0.5),
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nn.Linear(128, 10), nn.LogSoftmax(dim=1) )
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def train(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 = F.nll_loss(output, target)
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loss.backward()
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optimizer.step()
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if batch_idx % 100 == 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(model, device, test_loader):
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model.eval()
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test_loss,correct = 0,0
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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 += F.nll_loss(output, target, reduction='sum').item()
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pred = output.argmax(dim=1, keepdim=True)
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correct += pred.eq(target.view_as(pred)).sum().item()
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print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
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test_loss/len(test_loader.dataset), correct, len(test_loader.dataset),
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100. * correct/len(test_loader.dataset)))
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batch_size,test_batch_size = 256,512
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epochs,lr = 1,1e-2
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda" if use_cuda else "cpu")
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kwargs = {'num_workers': 1, 'pin_memory': True}
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transform=transforms.Compose([transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))])
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train_loader = DataLoader(
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datasets.MNIST('../data', train=True, download=True, transform=transform),
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batch_size=batch_size, shuffle=True, **kwargs)
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test_loader = DataLoader(
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datasets.MNIST('../data', train=False, transform=transform),
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batch_size=test_batch_size, shuffle=True, **kwargs)
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model = Net().to(device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
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if __name__ == '__main__':
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for epoch in range(1, epochs+1):
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train(model, device, train_loader, optimizer, epoch)
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test(model, device, test_loader)
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