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- from __future__ import print_function
- import argparse
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- import torch.optim as optim
- from torchvision import datasets, transforms
- from torch.optim.lr_scheduler import StepLR
-
-
- class Net(nn.Module):
- def __init__(self):
- super(Net, self).__init__()
- self.conv1 = nn.Conv2d(1, 32, 3, 1)
- self.conv2 = nn.Conv2d(32, 64, 3, 1)
- self.dropout1 = nn.Dropout2d(0.25)
- self.dropout2 = nn.Dropout2d(0.5)
- self.fc1 = nn.Linear(9216, 128)
- self.fc2 = nn.Linear(128, 10)
-
- def forward(self, x):
- x = self.conv1(x)
- x = F.relu(x)
- x = self.conv2(x)
- x = F.relu(x)
- x = F.max_pool2d(x, 2)
- x = self.dropout1(x)
- x = torch.flatten(x, 1)
- x = self.fc1(x)
- x = F.relu(x)
- x = self.dropout2(x)
- x = self.fc2(x)
- output = F.log_softmax(x, dim=1)
- return output
-
-
- def train(args, model, device, train_loader, optimizer, epoch):
- model.train()
- for batch_idx, (data, target) in enumerate(train_loader):
- data, target = data.to(device), target.to(device)
- optimizer.zero_grad()
- output = model(data)
- loss = F.nll_loss(output, target)
- loss.backward()
- optimizer.step()
- if batch_idx % args.log_interval == 0:
- print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
- epoch, batch_idx * len(data), len(train_loader.dataset),
- 100. * batch_idx / len(train_loader), loss.item()))
- if args.dry_run:
- break
-
-
- def test(model, device, test_loader):
- model.eval()
- test_loss = 0
- correct = 0
- with torch.no_grad():
- for data, target in test_loader:
- data, target = data.to(device), target.to(device)
- output = model(data)
- test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
- pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
- correct += pred.eq(target.view_as(pred)).sum().item()
-
- test_loss /= len(test_loader.dataset)
-
- print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
- test_loss, correct, len(test_loader.dataset),
- 100. * correct / len(test_loader.dataset)))
-
-
- def main():
- # Training settings
- parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
- parser.add_argument('--batch-size', type=int, default=64, metavar='N',
- help='input batch size for training (default: 64)')
- parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
- help='input batch size for testing (default: 1000)')
- parser.add_argument('--epochs', type=int, default=2, metavar='N',
- help='number of epochs to train (default: 2)')
- parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
- help='learning rate (default: 1.0)')
- parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
- help='Learning rate step gamma (default: 0.7)')
- parser.add_argument('--no-cuda', action='store_true', default=False,
- help='disables CUDA training')
- parser.add_argument('--dry-run', action='store_true', default=False,
- help='quickly check a single pass')
- parser.add_argument('--seed', type=int, default=1, metavar='S',
- help='random seed (default: 1)')
- parser.add_argument('--log-interval', type=int, default=10, metavar='N',
- help='how many batches to wait before logging training status')
- parser.add_argument('--save-model', action='store_true', default=False,
- help='For Saving the current Model')
- args = parser.parse_args()
- use_cuda = not args.no_cuda and torch.cuda.is_available()
-
- torch.manual_seed(args.seed)
-
- device = torch.device("cuda" if use_cuda else "cpu")
-
- kwargs = {'batch_size': args.batch_size}
- if use_cuda:
- kwargs.update({'num_workers': 1,
- 'pin_memory': True,
- 'shuffle': True},
- )
-
- transform=transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize((0.1307,), (0.3081,))
- ])
- # dataset1 = datasets.MNIST('./data', train=True, download=True,
- # transform=transform)
- # dataset2 = datasets.MNIST('./data', train=False,
- # transform=transform)
- dataset1 = datasets.FakeData(size=60000, image_size=(1, 28, 28), transform=transform)
- dataset2 = datasets.FakeData(size=10000, image_size=(1, 28, 28), transform=transform)
- train_loader = torch.utils.data.DataLoader(dataset1,**kwargs)
- test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)
-
- model = Net().to(device)
- optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
-
- scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
- for epoch in range(1, args.epochs + 1):
- train(args, model, device, train_loader, optimizer, epoch)
- test(model, device, test_loader)
- scheduler.step()
-
- if args.save_model:
- torch.save(model.state_dict(), "mnist_cnn.pt")
-
-
- if __name__ == '__main__':
- main()
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