Usage:
pycnn_factored_inflection.py [--cnn-mem MEM] [--input=INPUT] [--hidden=HIDDEN] [--epochs=EPOCHS] [--layers=LAYERS] [--optimization=OPTIMIZATION] TRAIN_PATH TEST_PATH RESULTS_PATH SIGMORPHON_PATH...
Arguments:
- TRAIN_PATH train file path (sigmorphon format)
- TEST_PATH test file path (sigmorphon format)
- RESULTS_PATH results file to be written
- SIGMORPHON_PATH sigmorphon root containing data, src dirs
Options:
- -h --help show this help message and exit
- --cnn-mem MEM allocates MEM bytes for (py)cnn
- --input=INPUT input vector dimensions
- --hidden=HIDDEN hidden layer dimensions
- --epochs=EPOCHS amount of training epochs
- --layers=LAYERS amount of layers in lstm network
- --optimization=OPTIMIZATION chosen optimization method ADAM/SGD/ADAGRAD/MOMENTUM
For example:
python pycnn_factored_inflection.py --input=120 --hidden=120 --epochs=100 --layers=2 --optimization=ADAM /Users/roeeaharoni/research_data/sigmorphon2016-master/data/navajo-task1-train /Users/roeeaharoni/research_data/sigmorphon2016-master/data/navajo-task1-dev /Users/roeeaharoni/Dropbox/phd/research/morphology/inflection_generation/results/navajo_results.txt /Users/roeeaharoni/research_data/sigmorphon2016-master/