add default parameters

src/train.py

@@ -63,25 +63,25 @@ def parse():
  parser.add_argument('--vocab', type=str, required=True, help='path of vocabulary (.pkl) or bpe vocab(.txt)')
  parser.add_argument('--shuffle', action='store_true', help='shuffle data')
  parser.add_argument('--save_dir', type=str, required=True, help='path to store the model')
- parser.add_argument('--batch_size', type=int, default=64, help='size of mini-batch')
- parser.add_argument('--lr', type=float, default=5e-4, help='Learning rate')
+ parser.add_argument('--batch_size', type=int, default=32, help='size of mini-batch')
+ parser.add_argument('--lr', type=float, default=1e-3, help='Learning rate')
  parser.add_argument('--alpha', type=float, required=True,
  help='balancing reconstruct loss and predictor loss')
  # vae training
- parser.add_argument('--beta', type=float, default=0.001,
+ parser.add_argument('--beta', type=float, default=0,
  help='balancing kl loss and other loss')
- parser.add_argument('--step_beta', type=float, default=0.0005,
+ parser.add_argument('--step_beta', type=float, default=0.002,
  help='value of beta increasing step')
- parser.add_argument('--max_beta', type=float, default=0.005)
- parser.add_argument('--kl_warmup', type=int, default=2000,
+ parser.add_argument('--max_beta', type=float, default=0.01)
+ parser.add_argument('--kl_warmup', type=int, default=0,
  help='Within these steps beta is set to 0')
  parser.add_argument('--kl_anneal_iter', type=int, default=1000)
 
  parser.add_argument('--num_workers', type=int, default=4, help='number of cpus to load data')
  parser.add_argument('--gpus', default=None, help='gpus to use')
- parser.add_argument('--epochs', type=int, default=20, help='max epochs')
+ parser.add_argument('--epochs', type=int, default=6, help='max epochs')
  parser.add_argument('--patience', type=int, default=3, help='early stopping number of epochs')
- parser.add_argument('--grad_clip', type=float, default=0,
+ parser.add_argument('--grad_clip', type=float, default=10.0,
  help='clip large gradient to prevent gradient boom')
  parser.add_argument('--monitor', type=str, default='val_loss',
  help='Value to monitor in early stopping')
@@ -91,9 +91,9 @@ def parse():
  default=['qed', 'logp'], help='properties to predict')
  parser.add_argument('--predictor_hidden_dim', type=int, default=200,
  help='hidden dim of predictor (MLP)')
- parser.add_argument('--node_hidden_dim', type=int, default=100,
+ parser.add_argument('--node_hidden_dim', type=int, default=300,
  help='dim of node hidden embedding in encoder and decoder')
- parser.add_argument('--graph_embedding_dim', type=int, default=200,
+ parser.add_argument('--graph_embedding_dim', type=int, default=400,
  help='dim of graph embedding by encoder and also condition for ae decoders')
  parser.add_argument('--latent_dim', type=int, default=56,
  help='dim of latent z for vae decoders')