def _translate(seq):
use_noise.set_value(0.)
# sample given an input sequence and obtain scores
sample, score = gen_sample(tparams,
f_init,
f_next,
numpy.array(seq).reshape([len(seq), 1]),
options,
trng=trng,
k=k,
maxlen=500,
stochastic=False,
argmax=False)
# normalize scores according to sequence lengths
if normalize:
lengths = numpy.array([len(s) for s in sample])
score = score / lengths
sidx = numpy.argmin(score)
return sample[sidx]
def _translate(seq):
use_noise.set_value(0.)
# sample given an input sequence and obtain scores
# NOTE : if seq length too small, do something about it
sample, score = gen_sample(tparams,
f_init,
f_next,
numpy.array(seq).reshape([len(seq), 1]),
options,
trng=trng,
k=k,
maxlen=500,
stochastic=False,
argmax=False)
# normalize scores according to sequence lengths
if normalize:
lengths = numpy.array([len(s) for s in sample])
score = score / lengths
# sidx = numpy.argmin(score)
sidx = random.randint(0, len(sample) - 1)
return sample[sidx], score[sidx]