Specify types for train.py args

Changes

1 changed files (+9/-9)

• train.py
  • @@ -11,20 +11,20 @@
    parser = ArgumentParser() parser.add_argument('-d', '--device', help='device to train with', default='cpu') parser.add_argument('-i', '--input', help='training data file', default='data') - parser.add_argument('-e', '--epochs', help='number of epochs to train for', default=100) - parser.add_argument('-s', '--seq-size', help='sequence size', default=32) - parser.add_argument('-b', '--batch-size', help='size of each training batch', default=256) - parser.add_argument('-m', '--embedding-dim', help='size of the embedding', default=512) - parser.add_argument('-l', '--lstm-size', help='size of the LSTM hidden state', default=512) - parser.add_argument('-a', '--layers', help='number of LSTM layers', default=3) - parser.add_argument('-r', '--dropout', help='how much dropout to apply', default=0.2) - parser.add_argument('-n', '--max-norm', help='maximum norm for gradient clipping', default=5) + parser.add_argument('-e', '--epochs', help='number of epochs to train for', default=100, type=int) + parser.add_argument('-s', '--seq-size', help='sequence size', default=32, type=int) + parser.add_argument('-b', '--batch-size', help='size of each training batch', default=256, type=int) + parser.add_argument('-m', '--embedding-dim', help='size of the embedding', default=512, type=int) + parser.add_argument('-l', '--lstm-size', help='size of the LSTM hidden state', default=512, type=int) + parser.add_argument('-a', '--layers', help='number of LSTM layers', default=3, type=int) + parser.add_argument('-r', '--dropout', help='how much dropout to apply', default=0.2, type=int) + parser.add_argument('-n', '--max-norm', help='maximum norm for gradient clipping', default=5, type=int) args = parser.parse_args() # Prepare dataloader dataset = Dataset(args.input, args.seq_size) - dataloader = DataLoader(dataset, batch_size=args.batch_size) + dataloader = DataLoader(dataset, args.batch_size) print(len(dataloader))