later.py

#
# 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=14, metavar='N',
#                         help='number of epochs to train (default: 14)')
#     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('--no-mps', action='store_true', default=False,
#                         help='disables macOS GPU 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()
#
#     torch.manual_seed(args.seed)
#
#     train_kwargs = {'batch_size': args.batch_size}
#     test_kwargs = {'batch_size': args.test_batch_size}
#     if use_cuda:
#         cuda_kwargs = {'num_workers': 1,
#                        'pin_memory': True,
#                        'shuffle': True}
#         train_kwargs.update(cuda_kwargs)
#         test_kwargs.update(cuda_kwargs)
#
#     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)
#     train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)
#     test_loader = torch.utils.data.DataLoader(dataset2, **test_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()