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+from __future__ import print_function
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+import argparse
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+import torch
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+import torch.nn as nn
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+import torch.nn.functional as F
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+import torch.optim as optim
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+from torchvision import datasets, transforms
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+
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+
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+class Net(nn.Module):
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+ def __init__(self):
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+ super(Net, self).__init__()
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+ self.conv1 = nn.Conv2d(1, 20, 5, 1)
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+ self.conv2 = nn.Conv2d(20, 50, 5, 1)
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+ self.fc1 = nn.Linear(4*4*50, 500)
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+ self.fc2 = nn.Linear(500, 10)
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+
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+ def forward(self, x):
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+ x = F.relu(self.conv1(x))
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+ x = F.max_pool2d(x, 2, 2)
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+ x = F.relu(self.conv2(x))
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+ x = F.max_pool2d(x, 2, 2)
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+ x = x.view(-1, 4*4*50)
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+ x = F.relu(self.fc1(x))
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+ x = self.fc2(x)
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+ return F.log_softmax(x, dim=1)
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+
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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 = F.nll_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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+
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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 += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
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+ pred = output.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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+
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+ test_loss /= len(test_loader.dataset)
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+
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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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+
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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=64, 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=1000, 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('--momentum', type=float, default=0.5, metavar='M',
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+ help='SGD momentum (default: 0.5)')
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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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+
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+ parser.add_argument('--save-model', action='store_true', default=False,
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+ help='For Saving the current Model')
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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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+
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+ torch.manual_seed(args.seed)
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+
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+ device = torch.device("cuda" if use_cuda else "cpu")
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+
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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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+
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+
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+ model = Net().to(device)
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+ optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
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+
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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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+
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+ if (args.save_model):
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+ torch.save(model.state_dict(),"mnist_cnn.pt")
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+
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+if __name__ == '__main__':
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+ main()
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