arguments.py

import argparse
import torch


def get_args():
    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=5, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
                        help='learning rate (default: 0.001)')
    parser.add_argument('--momentum', type=float, default=0.99, metavar='M',
                        help='SGD momentum (default: 0.99)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10000, metavar='N',
                        help='how many batches to wait before logging training status')
    parser.add_argument('--dropout', action='store_true', default=False,
                        help='Activates dropout training!')
    parser.add_argument('--origin', type=str, default='mnist',
                        help='Kind of training!')
    parser.add_argument('--ewc', action='store_true')
    args = parser.parse_args()

    args.cuda = not args.no_cuda and torch.cuda.is_available()

    return args