def main(_):
if params.test:
test(params)
elif params.preprocess:
preprocess(params)
else:
train(params)
def main(job_id, params):
print 'Anything printed here will end up in the output directory for job #%d' % job_id
print params
trainerr, validerr, testerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=20,
batch_size=16,
valid_batch_size=16,
validFreq=1000,
dispFreq=1,
saveFreq=1000,
sampleFreq=1000,
dataset='wmt14enfr',
dictionary=
'/data/lisatmp3/chokyun/wmt14/parallel-corpus/en-fr/vocab.fr.pkl',
use_dropout=True if params['use-dropout'][0] else False)
return validerr
def main(job_id, params):
print params
validerr = train(saveto=params['model'][0],
<<<<<<< HEAD
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=1000,
maxlen=50,
batch_size=32,
valid_batch_size=32,
validFreq=100,
dispFreq=10,
saveFreq=100,
sampleFreq=100,
datasets=['../data/hal/train/tok/en',
'../data/hal/train/tok/ja'],
valid_datasets=['../data/hal/dev/tok/en',
'../data/hal/dev/tok/ja'],
dictionaries=['../data/hal/train/tok/en.pkl',
'../data/hal/train/tok/ja.pkl'],
use_dropout=params['use-dropout'][0])
def main(job_id, params):
# print params
validerr = train(
saveto=params['saveto'][0],
loadfrom=params['loadfrom'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words_src=params['n-words-src'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=50,
batch_size=80,
valid_batch_size=80,
validFreq=100,
dispFreq=10,
saveFreq=20000,
sampleFreq=100,
max_epochs=5000, # max iteration
patience=1000, # early stop patience with BLEU score
finish_after=1000000, # max updates
datasets=['../data/cn.1w_with_unk.txt'],
valid_datasets=['../NIST/MT02/en0'],
dictionaries=['../data/en.txt.shuf.pkl'],
use_dropout=params['use-dropout'][0],
overwrite=False,
**bleuvalid_params)
return validerr
def main(job_id, params):
print(params)
validerr = train(datasets=[
'data/training/news-commentary-v9.fr-en.fr.tok',
'data/training/news-commentary-v9.fr-en.en.tok'
],
valid_datasets=[
'data/dev/newstest2013.fr.tok',
'data/dev/newstest2013.en.tok'
],
dictionaries=[
'data/training/news-commentary-v9.fr-en.fr.tok.pkl',
'data/training/news-commentary-v9.fr-en.en.tok.pkl'
],
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=15,
batch_size=32,
valid_batch_size=32,
validFreq=100,
dispFreq=100,
saveFreq=100,
sampleFreq=1000,
patience=10,
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print params
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen_src=300,
maxlen_trg= 75,
batch_size=32,
valid_batch_size=32,
datasets=['/Users/HyNguyen/Documents/Research/Data/stories_4nncnn/test.content.tok',
'/Users/HyNguyen/Documents/Research/Data/stories_4nncnn/test.summary.tok'],
valid_datasets=['/Users/HyNguyen/Documents/Research/Data/stories_4nncnn/train100.content.tok',
'/Users/HyNguyen/Documents/Research/Data/stories_4nncnn/train100.summary.tok'],
dictionaries=['/Users/HyNguyen/Documents/Research/Data/stories_4nncnn/dict.content.pkl',
"/Users/HyNguyen/Documents/Research/Data/stories_4nncnn/dict.summary.pkl"],
validFreq=5000,
dispFreq=10,
saveFreq=5000,
sampleFreq=1000,
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print ('timestamp {} {}'.format('running',time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())))
print (params)
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=10000,
maxlen=60,
batch_size=80,
validFreq_fine=2000,
validFreq=10000,
val_burn_in=20000,
val_burn_in_fine=400000,
dispFreq=20,
saveFreq=2000,
sampleFreq=200,
datasets=['/data/ycli/resource/wmt2017/deen/corpus.tc.en.bpe',
'/data/ycli/resource/wmt2017/deen/corpus.tc.de.bpe'],
valid_datasets=['/data/ycli/resource/wmt2017/deen/valid/valid_en_bpe',
'/data/ycli/resource/wmt2017/deen/valid/valid_de_bpe',
'./data/valid_out'],
dictionaries=['/data/ycli/resource/wmt2017/deen/vocab/v30-bpe/vocab_en.pkl',
'/data/ycli/resource/wmt2017/deen/vocab/v30-bpe/vocab_de.pkl'],
use_dropout=params['use-dropout'][0],
overwrite=False,
valid_mode=params['valid_mode'][1],
bleu_script=params['bleu_script'][0])
return validerr
def main(job_id, params):
print 'Anything printed here will end up in the output directory for job #%d' % job_id
print params
trainerr, validerr, testerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
encoder='gru',
decoder='gru_cond',
hiero='gru_hiero', # or None
n_words_src=params['n-words-src'][0],
n_words=params['n-words'][0],
decay_c=params['decay-c'][0],
alpha_c=params['alpha-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=50,
batch_size=64,
valid_batch_size=64,
validFreq=1000,
dispFreq=1,
saveFreq=500,
sampleFreq=10,
dataset='openmt15zhen',
dictionary='./openmt15/vocab.en.pkl',
dictionary_src='./openmt15/vocab.zh.pkl',
use_dropout=True if params['use-dropout'][0] else False)
return validerr
def main(job_id, params):
print params
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=1000,
maxlen=50,
batch_size=32,
valid_batch_size=32,
validFreq=100,
dispFreq=10,
saveFreq=1000,
sampleFreq=100,
datasets=['/home/zhouh/Data/nmt/corpus.ch',
'/home/zhouh/Data/nmt/corpus.en'],
valid_datasets=['/home/zhouh/Data/nmt/devntest/MT02/MT02.src',
'/home/zhouh/Data/nmt/devntest/MT02/reference0'],
dictionaries=['/home/zhouh/Data/nmt/corpus.ch.pkl',
'/home/zhouh/Data/nmt/corpus.en.pkl'],
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print params
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=250,
batch_size=32,
valid_batch_size=32,
datasets=['../data/newstest2011.content.tok','../data/newstest2011.summary.tok'],
valid_datasets=['../data/newstest2011.content.tok','../data/newstest2011.summary.tok'],
dictionaries=['../data/all_content-summary.content.tok.bpe.pkl',"../data/all_content-summary.summary.tok.bpe.pkl"],
validFreq=5000,
dispFreq=10,
saveFreq=5000,
sampleFreq=1000,
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print params
basedir = '/data/lisatmp3/firatorh/nmt/europarlv7'
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=15,
batch_size=32,
valid_batch_size=32,
datasets=['%s/europarl-v7.fr-en.fr.tok'%basedir,
'%s/europarl-v7.fr-en.en.tok'%basedir],
valid_datasets=['%s/newstest2011.fr.tok'%basedir,
'%s/newstest2011.en.tok'%basedir],
dictionaries=['%s/europarl-v7.fr-en.fr.tok.pkl'%basedir,
'%s/europarl-v7.fr-en.en.tok.pkl'%basedir],
validFreq=500000,
dispFreq=1,
saveFreq=100,
sampleFreq=50,
use_dropout=params['use-dropout'][0])
return validerr
def main(job_id, params):
print 'Anything printed here will end up in the output directory for job #%d' % job_id
print params
trainerr, validerr, testerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
encoder='gru',
decoder='gru_cond_simple',
hiero=None, #'gru_hiero', # or None
n_words_src=params['n-words-src'][0],
n_words=params['n-words'][0],
decay_c=params['decay-c'][0],
alpha_c=params['alpha-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=100,
batch_size=64,
valid_batch_size=64,
validFreq=1000,
dispFreq=1,
saveFreq=500,
sampleFreq=10,
dataset='stan',
dictionary='./stan/vocab_and_data_sub_europarl/vocab_sub_europarl.fr.pkl',
dictionary_src='./stan/vocab_and_data_sub_europarl/vocab_sub_europarl.en.pkl',
use_dropout=False)
return validerr
def main(job_id, params):
print "Anything printed here will end up in the output directory for job #%d" % job_id
print params
trainerr, validerr, testerr = train(
saveto=params["model"][0],
reload_=params["reload"][0],
dim_word=params["dim_word"][0],
dim=params["dim"][0],
n_words=params["n-words"][0],
n_words_src=params["n-words"][0],
decay_c=params["decay-c"][0],
lrate=params["learning-rate"][0],
optimizer=params["optimizer"][0],
maxlen=20,
batch_size=16,
valid_batch_size=16,
validFreq=1000,
dispFreq=1,
saveFreq=1000,
sampleFreq=1000,
dataset="wmt14enfr",
dictionary="/data/lisatmp3/chokyun/wmt14/parallel-corpus/en-fr/vocab.fr.pkl",
use_dropout=True if params["use-dropout"][0] else False,
)
return validerr
def main(job_id, params):
print params
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words-src'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=1000,
maxlen=50,
batch_size=32,
valid_batch_size=32,
validFreq=100,
dispFreq=10,
saveFreq=100,
sampleFreq=100,
datasets=['../../data/train.en.tok',
'../../data/train.de.tok'],
valid_datasets=['../../data/val.en.tok',
'../../data/val.de.tok'],
dictionaries=['../../data/train.en.tok.pkl',
'../../data/train.de.tok.pkl'],
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print params
basedir = "/data/lisatmp3/firatorh/nmt/europarlv7"
validerr = train(
saveto=params["model"][0],
reload_=params["reload"][0],
dim_word=params["dim_word"][0],
dim=params["dim"][0],
n_words=params["n-words"][0],
n_words_src=params["n-words"][0],
decay_c=params["decay-c"][0],
clip_c=params["clip-c"][0],
lrate=params["learning-rate"][0],
optimizer=params["optimizer"][0],
maxlen=15,
batch_size=32,
valid_batch_size=32,
datasets=["%s/europarl-v7.fr-en.fr.tok" % basedir, "%s/europarl-v7.fr-en.en.tok" % basedir],
valid_datasets=["%s/newstest2011.fr.tok" % basedir, "%s/newstest2011.en.tok" % basedir],
dictionaries=["%s/europarl-v7.fr-en.fr.tok.pkl" % basedir, "%s/europarl-v7.fr-en.en.tok.pkl" % basedir],
validFreq=500000,
dispFreq=1,
saveFreq=100,
sampleFreq=50,
use_dropout=params["use-dropout"][0],
overwrite=False,
)
return validerr
def main(job_id, params):
print 'Anything printed here will end up in the output directory for job #%d' % job_id
print params
trainerr, validerr, testerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words-src'][0],
decay_c=params['decay-c'][0],
alpha_c=params['alpha-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=20,
batch_size=16,
valid_batch_size=16,
validFreq=1000,
dispFreq=1,
saveFreq=500,
sampleFreq=10,
dataset='iwslt14zhen',
dictionary=
'/data/lisatmp3/firatorh/nmt/zh-en_lm/trainedModels/unionFinetuneRnd/union_dict.pkl',
use_dropout=True if params['use-dropout'][0] else False)
return validerr
def main(job_id, params):
print 'Anything printed here will end up in the output directory for job #%d' % job_id
print params
trainerr, validerr, testerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=20,
batch_size=160,
valid_batch_size=16,
validFreq=1000,
dispFreq=1,
saveFreq=1000,
sampleFreq=1000,
dataset='mydata',
dictionary='v_dst_wi.pkl',
dictionary_src='v_src_wi.pkl',
use_dropout=True if params['use-dropout'][0] else False)
return validerr
def main(job_id, params):
print params
username = os.environ['USER']
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=50,
batch_size=32,
valid_batch_size=32,
datasets=[
'/ichec/home/users/%s/data/all.en.concat.shuf.gz' % username,
'/ichec/home/users/%s/data/all.fr.concat.shuf.gz' % username],
valid_datasets=[
'/ichec/home/users/%s/data/newstest2011.en.tok' % username,
'/ichec/home/users/%s/data/newstest2011.fr.tok' % username],
dictionaries=[
'/ichec/home/users/%s/data/all.en.concat.gz.pkl' % username,
'/ichec/home/users/%s/data/all.fr.concat.gz.pkl' % username],
validFreq=5000,
dispFreq=10,
saveFreq=5000,
sampleFreq=1000,
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print params
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=50,
batch_size=32,
valid_batch_size=32,
datasets=['/home/xqli/data/big/big.ch',
'/home/xqli/data/big/big.en'],
valid_datasets=['/home/xqli/data/nist/03.seg',
'/home/xqli/data/nist/03.en'],
dictionaries=['/home/xqli/data/big/big.ch.pkl',
'/home/xqli/data/big/big.en.pkl'],
validFreq=5000,
dispFreq=10,
saveFreq=5000,
sampleFreq=1000,
use_dropout=params['use-dropout'][0])
return validerr
def main(job_id, params):
print params
bleu_params = {
'valid_path': '../data/validate/',
'temp_dir': '../temp/',
'translate_script': 'translate_gpu.py',
'bleu_script': 'multi-bleu.perl'
}
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=1000,
patience_bleu=100,
maxlen=100,
batch_size=100,
valid_batch_size=100,
validFreq=100,
dispFreq=10,
saveFreq=100,
sampleFreq=100,
datasets=['../data/cn.txt.sort', '../data/en.txt.sort'],
valid_datasets=['../data/MT02.cn.dev', '../data/MT02.en.dev'],
dictionaries=['../data/cn.txt.pkl', '../data/en.txt.pkl'],
use_dropout=params['use-dropout'][0],
overwrite=True,
**bleu_params)
return validerr
def main(job_id, params):
print params
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=50,
batch_size=32,
valid_batch_size=32,
datasets=[
'/home/ubuntu/codes/dl4mt-tutorial/data/europarl-v7.fr-en.en.tok',
'/home/ubuntu/codes/dl4mt-tutorial/data/europarl-v7.fr-en.fr.tok'
],
valid_datasets=[
'/home/ubuntu/codes/dl4mt-tutorial/data/newstest2011.en.tok',
'/home/ubuntu/codes/dl4mt-tutorial/data/newstest2011.fr.tok'
],
dictionaries=[
'/home/ubuntu/codes/dl4mt-tutorial/data/europarl-v7.fr-en.en.tok.pkl',
'/home/ubuntu/codes/dl4mt-tutorial/data/europarl-v7.fr-en.fr.tok.pkl'
],
validFreq=5000,
dispFreq=10,
saveFreq=5000,
sampleFreq=1000,
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print params
username = os.environ['USER']
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=50,
batch_size=32,
valid_batch_size=32,
datasets=[
'/ichec/home/users/%s/data/all.en.concat.shuf.gz' % username,
'/ichec/home/users/%s/data/all.fr.concat.shuf.gz' % username
],
valid_datasets=[
'/ichec/home/users/%s/data/newstest2011.en.tok' % username,
'/ichec/home/users/%s/data/newstest2011.fr.tok' % username
],
dictionaries=[
'/ichec/home/users/%s/data/all.en.concat.gz.pkl' % username,
'/ichec/home/users/%s/data/all.fr.concat.gz.pkl' % username
],
validFreq=5000,
dispFreq=10,
saveFreq=5000,
sampleFreq=1000,
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print params
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim_enc=params['dim_enc'], # multi layer
dim_dec=params['dim_dec'][0],
dim_attention=params['dim_attention'][0],
dim_coverage=params['dim_coverage'][0],
kernel_coverage=params['kernel_coverage'][0],
down_sample=params['down_sample'],
dim_target=params['dim_target'][0],
dim_feature=params['dim_feature'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
model_cost_coeff=params['model_cost_coeff'][0],
optimizer=params['optimizer'][0],
patience=15,
maxlen=params['maxlen'][0],
batch_size=8,
valid_batch_size=8,
validFreq=-1,
dispFreq=100,
saveFreq=-1,
sampleFreq=-1,
datasets=['../data/online-train.pkl', '../data/train_data_v1.txt'],
valid_datasets=['../data/online-test.pkl', '../data/test_data_v1.txt'],
dictionaries=['../data/dictionary.txt'],
valid_output=['./result/valid_decode_result.txt'],
valid_result=['./result/valid.wer'],
use_dropout=params['use-dropout'][0])
return validerr
def main(job_id, params):
print 'Anything printed here will end up in the output directory for job #%d' % job_id
print params
trainerr, validerr, testerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words-src'][0],
decay_c=params['decay-c'][0],
alpha_c=params['alpha-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
encoder='gru',
decoder='gru_cond', #'gru_cond_simple',
maxlen=30,
batch_size=128,
valid_batch_size=128,
validFreq=1000,
dispFreq=1,
saveFreq=500,
sampleFreq=500,
dataset='trans_enhi',
dictionary='/data/lisatmp3/chokyun/transliteration/TranslitDataset/vocab.hi.pkl',
dictionary_src='/data/lisatmp3/chokyun/transliteration/TranslitDataset/vocab.en.pkl',
use_dropout=True if params['use-dropout'][0] else False)
return validerr
def main(job_id, params):
print params
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=1000,
maxlen=50,
batch_size=32,
valid_batch_size=32,
validFreq=100,
dispFreq=10,
saveFreq=100,
sampleFreq=100,
datasets=['/veu4/usuaris29/mruiz/tfg-imagenes/train/train.un.zh',
'/veu4/usuaris29/mruiz/tfg-imagenes/train/train.un.es'],
valid_datasets=['/veu4/usuaris29/mruiz/tfg-imagenes/dev/dev.un.zh',
'/veu4/usuaris29/mruiz/tfg-imagenes/dev/dev.un.es'],
dictionaries=['/veu4/usuaris29/mruiz/tfg-imagenes/train/vocab.zh.pkl',
'/veu4/usuaris29/mruiz/tfg-imagenes/train/vocab.es.pkl'],
use_dropout=params['use-dropout'][0])
return validerr
def main(job_id, params):
print(params)
validerr = train(
datasets=['data/all.en.concat.shuf.gz', 'data/all.fr.concat.shuf.gz'],
valid_datasets=[
'data/newstest2011.en.tok', 'data/newstest2011.fr.tok'
],
dictionaries=[
'data/all.en.concat.gz.pkl', 'data/all.fr.concat.gz.pkl'
],
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=50,
batch_size=32,
valid_batch_size=32,
validFreq=5000,
dispFreq=10,
saveFreq=5000,
sampleFreq=1000,
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print params
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=1000,
maxlen=50,
batch_size=32,
valid_batch_size=32,
validFreq=100,
dispFreq=10,
saveFreq=100,
sampleFreq=100,
datasets=['../data/hal/train/tok/en', '../data/hal/train/tok/fr'],
valid_datasets=['../data/hal/dev/tok/en', '../data/hal/dev/tok/fr'],
dictionaries=[
'../data/hal/train/tok/en.pkl', '../data/hal/train/tok/fr.pkl'
],
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print params
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=50,
batch_size=32,
valid_batch_size=32,
datasets=['/ichec/home/users/%s/data/europarl-v7.fr-en.en.tok'%os.environ['USER'],
'/ichec/home/users/%s/data/europarl-v7.fr-en.fr.tok'%os.environ['USER']],
valid_datasets=['/ichec/home/users/%s/data/newstest2011.en.tok'%os.environ['USER'],
'/ichec/home/users/%s/data/newstest2011.fr.tok'%os.environ['USER']],
dictionaries=['/ichec/home/users/%s/data/europarl-v7.fr-en.en.tok.pkl'%os.environ['USER'],
'/ichec/home/users/%s/data/europarl-v7.fr-en.fr.tok.pkl'%os.environ['USER']],
validFreq=5000,
dispFreq=10,
saveFreq=5000,
sampleFreq=1000,
use_dropout=params['use-dropout'][0])
return validerr
def main(job_id, params):
print params
basedir = '/data/lisatmp3/firatorh/nmt/europarlv7'
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=15,
batch_size=32,
valid_batch_size=32,
datasets=[
'%s/europarl-v7.fr-en.fr.tok' % basedir,
'%s/europarl-v7.fr-en.en.tok' % basedir
],
valid_datasets=[
'%s/newstest2011.fr.tok' % basedir,
'%s/newstest2011.en.tok' % basedir
],
dictionaries=[
'%s/europarl-v7.fr-en.fr.tok.pkl' % basedir,
'%s/europarl-v7.fr-en.en.tok.pkl' % basedir
],
validFreq=500000,
dispFreq=1,
saveFreq=100,
sampleFreq=50,
use_dropout=params['use-dropout'][0])
return validerr
def main(job_id, params):
print params
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim_enc=params['dim_enc'], # multi layer
dim_dec=params['dim_dec'][0],
dim_coverage=params['dim_coverage'][0],
down_sample=params['down_sample'],
dim_target=params['dim_target'][0],
dim_feature=params['dim_feature'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
gamma=params['alphas-gamma'][0],
optimizer=params['optimizer'][0],
patience=15,
maxlen=params['maxlen'][0],
batch_size=8,
valid_batch_size=8,
validFreq=-1,
dispFreq=100,
saveFreq=-1,
sampleFreq=-1,
datasets=['../data/online-train.pkl',
'../data/train_caption.txt',
'../data/align-online-train.pkl'],
valid_datasets=['../data/online-test.pkl',
'../data/test_caption.txt'],
dictionaries=['../data/dictionary.txt'],
use_dropout=params['use-dropout'][0])
return validerr
def test_DPM(self):
"""
Verifies that the correct cost is calculated for DPM.
:return: 0 on success
"""
logging.info("Starting Test for DPM..")
working_dir = DATA_DIR + "DPM/"
prepare_base_model(working_dir)
cost, prepared_rewards, prepared_word_propensities, reweigh_sum = \
train(saveto="%smodel.npz" % working_dir,
reload_=True,
shuffle_each_epoch=False,
datasets=DATA_SETS,
dictionaries=DICTIONARIES,
objective='CL',
cl_deterministic=True,
cl_log=LOG_PREFIX + ".json",
unittest=True)
true_cost = -0.7047157462492611
self.assertAlmostEqual(true_cost, cost)
true_prepared_rewards = numpy.array([
1., 0.8222672, 1., 0.8274377, 0.7813821, 0.7813821, 0.6673543, 1.,
0.6093617, 1.
])
numpy.testing.assert_almost_equal(true_prepared_rewards,
prepared_rewards)
true_prepared_word_propensities = numpy.zeros(shape=(22, 10))
numpy.testing.assert_almost_equal(true_prepared_word_propensities,
prepared_word_propensities)
true_reweigh_sum = 0.0
numpy.testing.assert_almost_equal(true_reweigh_sum, reweigh_sum)
shutil.rmtree(working_dir)
logging.info("Finished Test for DPM..")
return 0
def test_DPM_T_OSL(self):
"""
Verifies that the correct cost is calculated for DPM+T+OSL
:return: 0 on success
"""
logging.info("Starting Test for DPM+T+OSL..")
working_dir = DATA_DIR + "DPM_T_OSL/"
prepare_base_model(working_dir)
cost, prepared_rewards, prepared_word_propensities, reweigh_sum = \
train(saveto="%smodel.npz" % working_dir,
reload_=True,
shuffle_each_epoch=False,
datasets=DATA_SETS,
dictionaries=DICTIONARIES,
objective='CL',
cl_deterministic=True,
cl_log=LOG_PREFIX + ".json",
cl_external_reward=WORD_REWARD,
cl_reweigh=True,
cl_word_rewards=True,
unittest=True)
true_cost = -1.1665413125898798
self.assertAlmostEqual(true_cost, cost)
true_prepared_rewards = numpy.array(
[[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 0., 1., 0., 1.],
[1., 1., 1., 1., 0., 0., 0., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 0., 1., 1., 1., 1., 1., 1., 0., 1.],
[1., 1., 1., 0., 1., 1., 0., 0., 0., 0.],
[1., 1., 1., 1., 1., 1., 0., 0., 0., 0.],
[1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],
[1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],
[1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],
[1., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
numpy.testing.assert_almost_equal(true_prepared_rewards,
prepared_rewards)
true_prepared_word_propensities = numpy.zeros(shape=(22, 10))
numpy.testing.assert_almost_equal(true_prepared_word_propensities,
prepared_word_propensities)
true_reweigh_sum = 0.826496987098
numpy.testing.assert_almost_equal(true_reweigh_sum, reweigh_sum)
shutil.rmtree(working_dir)
logging.info("Finished Test for DPM+T+OSL..")
return 0
def main(job_id, params):
print params
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim_relation=params['dim_relation'][0],
dim_enc=params['dim_enc'], # multi layer
dim_dec=params['dim_dec'][0],
dim_coverage=params['dim_coverage'][0],
down_sample=params['down_sample'],
dim_attention=params['dim_attention'][0],
dim_reattention=params['dim_reattention'][0],
dim_target=params['dim_target'][0],
dim_retarget=params['dim_retarget'][0],
dim_feature=params['dim_feature'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
la=params['lambda-align'][0],
lb=params['lambda-realign'][0],
optimizer=params['optimizer'][0],
patience=12,
max_xlen=params['max_xlen'][0],
max_ylen=params['max_ylen'][0],
batch_size=8,
valid_batch_size=8,
validFreq=-1,
validStart=-10,
dispFreq=100,
saveFreq=-1,
sampleFreq=-1,
datasets=[
root_path + '9feature-train-dis-0.005-revise-pad-v5.pkl',
root_path + '9feature-train-dis-0.005-revise-pad-v5-mask.pkl',
root_path + 'train-label-r1.pkl',
root_path + 'align-train-dis-0.005-revise-pad-v5-r1.pkl',
root_path + 'related-align-train-dis-0.005-revise-pad-v5-r1.pkl'
],
valid_datasets=[
root_path + '9feature-valid-dis-0.005-revise-pad-v5.pkl',
root_path + '9feature-valid-dis-0.005-revise-pad-v5-mask.pkl',
root_path + 'test-label-r1.pkl',
root_path + 'align-test-dis-0.005-revise-pad-v5-r1.pkl',
root_path + 'related-align-test-dis-0.005-revise-pad-v5-r1.pkl'
],
dictionaries=[
root_path + 'dictionary.txt',
root_path + '6relation_dictionary.txt',
],
valid_output=[
'./result/symbol_relation/', './result/alignment/',
'./result/relation_alignment/'
],
valid_result=['./result/valid.cer'],
use_dropout=params['use-dropout'][0])
return validerr
def main(job_id, params):
re_load = False
save_file_name = 'bpe2char_biscale_decoder_attc_adam'
source_dataset = params['train_data_path'] + params['source_dataset']
target_dataset = params['train_data_path'] + params['target_dataset']
valid_source_dataset = params['dev_data_path'] + params[
'valid_source_dataset']
valid_target_dataset = params['dev_data_path'] + params[
'valid_target_dataset']
source_dictionary = params['train_data_path'] + params['source_dictionary']
target_dictionary = params['train_data_path'] + params['target_dictionary']
print params, params['save_path'], save_file_name
validerr = train(
max_epochs=int(params['max_epochs']),
patience=int(params['patience']),
dim_word=int(params['dim_word']),
dim_word_src=int(params['dim_word_src']),
save_path=params['save_path'],
save_file_name=save_file_name,
re_load=re_load,
enc_dim=int(params['enc_dim']),
dec_dim=int(params['dec_dim']),
n_words=int(params['n_words']),
n_words_src=int(params['n_words_src']),
decay_c=float(params['decay_c']),
lrate=float(params['learning_rate']),
optimizer=params['optimizer'],
maxlen=int(params['maxlen']),
maxlen_trg=int(params['maxlen_trg']),
maxlen_sample=int(params['maxlen_sample']),
batch_size=int(params['batch_size']),
valid_batch_size=int(params['valid_batch_size']),
sort_size=int(params['sort_size']),
validFreq=int(params['validFreq']),
dispFreq=int(params['dispFreq']),
saveFreq=int(params['saveFreq']),
sampleFreq=int(params['sampleFreq']),
clip_c=int(params['clip_c']),
datasets=[source_dataset, target_dataset],
valid_datasets=[valid_source_dataset, valid_target_dataset],
dictionaries=[source_dictionary, target_dictionary],
use_dropout=int(params['use_dropout']),
source_word_level=int(params['source_word_level']),
target_word_level=int(params['target_word_level']),
layers=layers,
save_every_saveFreq=1,
use_bpe=1,
init_params=init_params,
build_model=build_model,
build_sampler=build_sampler,
gen_sample=gen_sample,
)
return validerr
def main(job_id, params):
re_load = False
save_file_name = 'bpe2bpe_two_layer_gru_decoder_adam'
source_dataset = params['train_data_path'] + params['source_dataset']
target_dataset = params['train_data_path'] + params['target_dataset']
valid_source_dataset = params['dev_data_path'] + params['valid_source_dataset']
valid_target_dataset = params['dev_data_path'] + params['valid_target_dataset']
source_dictionary = params['train_data_path'] + params['source_dictionary']
target_dictionary = params['train_data_path'] + params['target_dictionary']
print params, params['save_path'], save_file_name
validerr = train(
max_epochs=int(params['max_epochs']),
patience=int(params['patience']),
dim_word=int(params['dim_word']),
dim_word_src=int(params['dim_word_src']),
save_path=params['save_path'],
save_file_name=save_file_name,
re_load=re_load,
enc_dim=int(params['enc_dim']),
dec_dim=int(params['dec_dim']),
n_words=int(params['n_words']),
n_words_src=int(params['n_words_src']),
decay_c=float(params['decay_c']),
lrate=float(params['learning_rate']),
optimizer=params['optimizer'],
maxlen=int(params['maxlen']),
maxlen_trg=int(params['maxlen_trg']),
maxlen_sample=int(params['maxlen_sample']),
batch_size=int(params['batch_size']),
valid_batch_size=int(params['valid_batch_size']),
sort_size=int(params['sort_size']),
validFreq=int(params['validFreq']),
dispFreq=int(params['dispFreq']),
saveFreq=int(params['saveFreq']),
sampleFreq=int(params['sampleFreq']),
clip_c=int(params['clip_c']),
datasets=[source_dataset, target_dataset],
valid_datasets=[valid_source_dataset, valid_target_dataset],
dictionaries=[source_dictionary, target_dictionary],
use_dropout=int(params['use_dropout']),
source_word_level=int(params['source_word_level']),
target_word_level=int(params['target_word_level']),
layers=layers,
save_every_saveFreq=1,
use_bpe=1,
init_params=init_params,
build_model=build_model,
build_sampler=build_sampler,
gen_sample=gen_sample
)
return validerr
def main(job_id, params):
print params
validerr = train(
saveto=params['model'][0],
bn_saveto=params['bn_model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim_dec=params['dim_dec'][0],
dim_attention=params['dim_attention'][0],
dim_coverage=params['dim_coverage'][0],
kernel_coverage=params['kernel_coverage'],
kernel_conv1=params['kernel_conv1'],
stride_conv1=params['stride_conv1'],
channel_conv1=params['channel_conv1'][0],
GrowthRate=params['GrowthRate'][0],
DenseBlock=params['DenseBlock'],
Bottleneck=params['Bottleneck'][0],
Transition=params['Transition'][0],
dim_target=params['dim_target'][0],
input_channels=params['input_channels'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=15,
maxlen=params['maxlen'][0],
maxImagesize=params['maxImagesize'][0],
batch_Imagesize=500000,
valid_batch_Imagesize=500000,
batch_size=16,
valid_batch_size=16,
validFreq=-1,
dispFreq=100,
saveFreq=-1,
sampleFreq=-1,
datasets=['../data/offline-train.pkl', '../data/train_data_v1.txt'],
valid_datasets=[
'../data/offline-test.pkl', '../data/test_data_v1.txt'
],
dictionaries=['../data/dictionary.txt'],
valid_output=['./result/valid_decode_result.txt'],
valid_result=['./result/valid.wer'],
use_dropout=params['use-dropout'][0])
return validerr
def main(job_id, params):
print params
trainerr, validerr, testerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=50,
batch_size=4,
valid_batch_size=4,
validFreq=5000,
dispFreq=10,
saveFreq=5000,
sampleFreq=10,
use_dropout=params['use-dropout'][0])
return validerr
def main(job_id, params):
print params
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim_chunk=params['dim_chunk'][0],
dim_chunk_hidden=params['dim_chunk_hidden'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=1000,
batch_size=2,
valid_batch_size=2,
validFreq=3,
dispFreq=10,
saveFreq=10,
sampleFreq=10,
maxlen_chunk=30, # maximum length of the description
maxlen_chunk_words=50, # maximum length of the description
datasets=[
'/home/zhouh/workspace/python/nmtdata/small.ch',
'/home/zhouh/workspace/python/nmtdata/small.en.chunked'
],
valid_datasets=[
'/home/zhouh/workspace/python/nmtdata/small.ch',
'/home/zhouh/workspace/python/nmtdata/small.en.chunked'
],
dictionaries=[
'/home/zhouh/workspace/python/nmtdata/small.ch.pkl',
'/home/zhouh/workspace/python/nmtdata/small.en.chunked.pkl'
],
dictionary_chunk=
'/home/zhouh/workspace/python/nmtdata/small.en.chunked.chunktag.pkl',
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main():
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
root_log_dir = "./logs/"
exp_name = "encDecAtt_%s" % timestamp
train_err, valid_err, test_err = train(
dim_word=dim_word,
dim=dim,
encoder=encoder,
decoder=decoder,
hiero=None, # 'gru_hiero', # or None
patience=patience,
max_epochs=max_epochs,
dispFreq=dispFreq,
decay_c=0.,
alpha_c=0.,
diag_c=0.,
lrate=0.01,
n_words_src=n_words_src,
n_words=n_words,
maxlen=maxlen,
optimizer=optimizer,
batch_size=batch_size,
valid_batch_size=valid_batch_size,
saveto=saveto,
validFreq=validFreq,
saveFreq=saveFreq,
sampleFreq=sampleFreq,
dataset=dataset,
dictionary=dictionary,
dictionary_src=dictionary,
use_dropout=False,
reload_=reload_,
correlation_coeff=0.1,
clip_c=1.,
dataset_=dataset_,
use_context=use_context,
dim_context=dim_context,
dataset_size=dataset_size)
def main(job_id, params):
print(params)
data_name = '../../data'
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=40,
batch_size=32,
valid_batch_size=32,
datasets=[
data_name + '/q_train.txt', data_name + '/r_train.txt',
data_name + '/s_train.txt'
],
valid_datasets=[
data_name + '/q_val.txt', data_name + '/r_val.txt',
data_name + '/s_val.txt'
],
dictionaries=[data_name + '/dict.pkl', data_name + '/dict.pkl'],
validFreq=100,
dispFreq=100,
saveFreq=100,
sampleFreq=100,
use_dropout=params['use-dropout'][0],
overwrite=True,
max_epochs=30,
senti_num=2,
senti_dim=64,
weight_d=1.,
weight_h=1.,
style_class=True,
style_adv=False,
adv_thre=1,
patience=10000)
return validerr
def main(job_id, params):
print params
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=1000,
maxlen=50,
batch_size=32,
valid_batch_size=32,
validFreq=100,
dispFreq=100,
saveFreq=1000,
sampleFreq=1000,
datasets=[
'/home/chenhd/data/zh2en/tree/corpus.ch',
'/home/chenhd/data/zh2en/tree/corpus.en'
],
valid_datasets=[
'/home/chenhd/data/zh2en/devntest/MT02/MT02.src',
'/home/chenhd/data/zh2en/devntest/MT02/reference0'
],
dictionaries=[
'/home/chenhd/data/zh2en/tree/corpus.ch.pkl',
'/home/chenhd/data/zh2en/tree/corpus.en.pkl'
],
treeset=[
'/home/chenhd/data/zh2en/tree/corpus.ch.tree',
'/home/chenhd/data/zh2en/devntest/MT02/MT02.ce.tree'
],
use_dropout=params['use-dropout'][0],
# shuffle_each_epoch=True,
overwrite=False)
return validerr
def main(job_id, params):
print('timestamp {} {}'.format(
'running', time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())))
print(params)
validerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=1000,
maxlen=50,
batch_size=80,
validFreq_fine=5000,
validFreq=5000,
val_burn_in=20000,
val_burn_in_fine=90000,
dispFreq=20,
saveFreq=2000,
sampleFreq=200,
datasets=[
'/home/ycli/resource/hw/ch.txt.shuffle',
'/home/ycli/resource/hw/en.txt.shuffle'
],
valid_datasets=[
'/home/ycli/resource/hw/valid/valid_src',
'/home/ycli/resource/hw/valid/valid_trg',
'./data/valid_out'
],
dictionaries=[
'/home/ycli/resource/hw/vocab/vocab_src.pkl',
'/home/ycli/resource/hw/vocab/vocab_trg.pkl'
],
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print(params)
validerr = train(
saveto=params['model'][0],
bn_saveto=params['bn_model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
kernel_Convenc=params['kernel_Convenc'],
dim_ConvBlock=params['dim_ConvBlock'],
layersNum_block=params['layersNum_block'],
dim_dec=params['dim_dec'][0],
dim_attention=params['dim_attention'][0],
dim_coverage=params['dim_coverage'][0],
kernel_coverage=params['kernel_coverage'],
dim_target=params['dim_target'][0],
input_channels=params['input_channels'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=15,
maxlen=params['maxlen'][0],
maxImagesize=params['maxImagesize'][0],
batch_Imagesize=500000,
valid_batch_Imagesize=500000,
batch_size=8,
valid_batch_size=8,
validFreq=-1,
dispFreq=100,
saveFreq=-1,
sampleFreq=-1,
datasets=['../data/offline-train.pkl', '../data/train_caption.txt'],
valid_datasets=[
'../data/offline-test.pkl', '../data/test_caption.txt'
],
dictionaries=['../data/dictionary.txt'],
valid_output=['./result/valid_decode_result.txt'],
valid_result=['./result/valid.wer'],
use_dropout=params['use-dropout'][0])
return validerr
def main(job_id, params):
print params
validerr = train(
saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim_chunk=params['dim_chunk'][0],
dim_chunk_hidden=params['dim_chunk_hidden'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
clip_c=params['clip-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
patience=10000,
batch_size=32,
valid_batch_size=32,
validFreq=100,
dispFreq=10,
saveFreq=1000,
sampleFreq=100,
maxlen_chunk_words=50, # maximum length of the description
datasets=[
'/home/zhouh/Data/nmt/hms.ch.filter',
'/home/zhouh/Data/nmt/hms.en.filter.chunked'
],
valid_datasets=[
'/home/zhouh/Data/nmt/devntest/MT02/MT02.src',
'/home/zhouh/Data/nmt/devntest/MT02/reference0.tag.chunked.chunked'
],
dictionaries=[
'/home/zhouh/Data/nmt/hms.ch.filter.pkl',
'/home/zhouh/Data/nmt/hms.en.filter.chunked.pkl'
],
dictionary_chunk=
'/home/zhouh/Data/nmt/hms.en.filter.chunked.chunktag.pkl',
use_dropout=params['use-dropout'][0],
overwrite=False)
return validerr
def main(job_id, params):
print 'Anything printed here will end up in the output directory for job #%d' % job_id
print params
trainerr, validerr, testerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words'][0],
decay_c=params['decay-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=20,
batch_size=160,
valid_batch_size=16,
validFreq=1000,
dispFreq=1,
saveFreq=1000,
sampleFreq=1000,
dataset='mydata',
dictionary='v_dst_wi.pkl',
dictionary_src = 'v_src_wi.pkl',
use_dropout=True if params['use-dropout'][0] else False)
return validerr
def main():
logging.basicConfig(format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO)
args = setup_args()
logging.info(args)
validerr = train(
saveto=args.model + ".npz",
reload_=False,
dim=args.dimhidden,
dim_word=args.dimword,
n_words=args.targetwords,
n_words_src=args.srcwords,
decay_c=args.decay,
clip_c=args.clipc,
alpha_c=args.alphac,
lrate=args.lr,
optimizer="adam",
patience=1000,
maxlen=args.maxlen,
batch_size=args.batch,
valid_batch_size=args.batch,
validFreq=args.validfreq,
dispFreq=args.dispfreq,
saveFreq=args.savefreq,
sampleFreq=args.samplefreq,
baseDir=args.basedir,
word2vecFile=args.wordvec,
datasets=["train_src.txt", "train_target.txt"],
valid_datasets=["valid_src.txt", "valid_target.txt"],
# dictionaries=['src.txt.pkl', 'target.txt.pkl'],
dictionaries=["all.txt.pkl"],
use_dropout=False,
overwrite=True,
)
logging.info("FINAL Validation error: " + str(validerr))
def main(job_id, params):
print 'Anything printed here will end up in the output directory for job #%d' % job_id
print params
trainerr, validerr, testerr = train(saveto=params['model'][0],
reload_=params['reload'][0],
dim_word=params['dim_word'][0],
dim=params['dim'][0],
n_words=params['n-words'][0],
n_words_src=params['n-words-src'][0],
decay_c=params['decay-c'][0],
alpha_c=params['alpha-c'][0],
lrate=params['learning-rate'][0],
optimizer=params['optimizer'][0],
maxlen=20,
batch_size=16,
valid_batch_size=16,
validFreq=1000,
dispFreq=1,
saveFreq=500,
sampleFreq=10,
dataset='iwslt14zhen',
dictionary='/data/lisatmp3/firatorh/nmt/zh-en_lm/trainedModels/unionFinetuneRnd/union_dict.pkl',
use_dropout=True if params['use-dropout'][0] else False)
return validerr
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
trainerr, validerr, testerr = train(saveto=modelName,
reload_=False,
dim_word=dim_word,
dim=dim_model,
encoder='gru',
decoder='gru_cond_double',
# decoder='gru_cond',
hiero=None, #'gru_hiero', # or None
max_epochs=100,
n_words_src=n_words_src,
n_words=n_words_trg,
optimizer='adadelta',
decay_c=0.,
alpha_c=0.,
diag_c=0.,# not used with adadelta
lrate=lr,
patience=10,
maxlen=50,
batch_size=batch_size,
valid_batch_size=batch_size,
validFreq=nb_batch_epoch, # freq in batch of computing cost for train, valid and test
dispFreq=nb_batch_epoch, # freq of diplaying the cost of one batch (e.g.: 1 is diplaying the cost of each batch)
saveFreq=nb_batch_epoch, # freq of saving the model per batch
sampleFreq=nb_batch_epoch, # freq of sampling per batch
dataset=dataset,
dictionary=dictionary_trg,
dictionary_src=dictionary_src,
use_dropout=False,
clip_c=1.)