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41f6dd8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import numpy
import os
import sys
import logging
VOCAB_SIZE = 90000
SRC = "ro"
TGT = "en"
DATA_DIR = "data/"
from nematus.nmt import train
logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
if __name__ == '__main__':
validerr = train(saveto='model/model.npz',
reload_=True,
dim_word=500,
dim=1024,
n_words=VOCAB_SIZE,
n_words_src=VOCAB_SIZE,
decay_c=0.,
clip_c=1.,
lrate=0.0001,
optimizer='adadelta',
maxlen=50,
batch_size=80,
valid_batch_size=80,
datasets=[DATA_DIR + '/corpus.bpe.' + SRC, DATA_DIR + '/corpus.bpe.' + TGT],
valid_datasets=[DATA_DIR + '/newsdev2016.bpe.' + SRC, DATA_DIR + '/newsdev2016.bpe.' + TGT],
dictionaries=[DATA_DIR + '/corpus.bpe.' + SRC + '.json',DATA_DIR + '/corpus.bpe.' + TGT + '.json'],
validFreq=10000,
dispFreq=1000,
saveFreq=30000,
sampleFreq=10000,
use_dropout=False,
dropout_embedding=0.2, # dropout for input embeddings (0: no dropout)
dropout_hidden=0.2, # dropout for hidden layers (0: no dropout)
dropout_source=0.1, # dropout source words (0: no dropout)
dropout_target=0.1, # dropout target words (0: no dropout)
overwrite=False,
external_validation_script='./validate.sh')
print validerr
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