TensorFlow implementation of Pointer Networks.
Support multithreaded data pipelines to reduce I/O latency.
- Python 2.7
- tqdm
- TensorFlow 0.12.1
To train a model:
$ python main.py --task=tsp --max_data_length=20 --hidden_dim=512 # download dataset used in the paper $ python main.py --task=tsp --max_data_length=10 --hidden_dim=128 # generate dataset itself To train a model:
$ python main.py $ tensorboard --logdir=logs --host=0.0.0.0 To test a model:
$ python main.py --is_train=False Train/Test loss of max_data_length=10 after 24,000 steps:
$ python main.py --task=tsp --max_data_length=10 --hidden_dim=128 $ ... $ 2017-01-18 17:17:48,054:INFO:: $ 2017-01-18 17:17:48,054:INFO::test loss: 0.00149279157631 $ 2017-01-18 17:17:48,055:INFO::test x: [1 6 2 5 4 3 0 0 0 0 0] $ 2017-01-18 17:17:48,057:INFO::test y: [1 6 2 5 4 3 0 0 0 0 0] (True) $ 2017-01-18 17:17:48,059:INFO::test x: [1 3 8 4 7 5 2 6 0 0 0] $ 2017-01-18 17:17:48,059:INFO::test y: [1 3 8 4 7 5 2 6 0 0 0] (True) $ 2017-01-18 17:17:48,058:INFO::test x: [ 1 8 3 7 9 5 6 4 2 10 0] $ 2017-01-18 17:17:48,058:INFO::test y: [ 1 8 3 7 9 5 6 4 2 10 0] (True) 
Train/Test loss of max_data_length=20 after 40,000 steps:
$ python main.py --task=tsp --max_data_length=20 --hidden_dim=512 (in progress)
Taehoon Kim / @carpedm20