def _get_job_words(self, alpha, work, job, neu1):
if self.sg:
return sum(
train_sentence_sg(self, sentence, alpha, work)
for sentence in job)
else:
return sum(
train_sentence_cbow(self, sentence, alpha, work, neu1)
for sentence in job)
def worker_train():
"""Train the model, lifting lists of sentences from the jobs queue."""
work = zeros(self.layer1_size, dtype=REAL) # each thread must have its own work memory
neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL)
while True:
job = jobs.get()
if job is None: # data finished, exit
break
# update the learning rate before every job
alpha = max(self.min_alpha, self.alpha * (1 - 1.0 * word_count[0] / total_words))
# how many words did we train on? out-of-vocabulary (unknown) words do not count
if self.sg:
job_words = sum(train_sentence_sg(self, sentence, alpha, work) for sentence in job)
else:
job_words = sum(train_sentence_cbow(self, sentence, alpha, work, neu1) for sentence in job)
with lock:
word_count[0] += job_words
elapsed = time.time() - start
if elapsed >= next_report[0]:
logger.info("PROGRESS: at %.2f%% words, alpha %.05f, %.0f words/s" %
(100.0 * word_count[0] / total_words, alpha, word_count[0] / elapsed if elapsed else 0.0))
next_report[0] = elapsed + 1.0 # don't flood the log, wait at least a second between progress reports
def _get_job_words(self, alpha, work, job, neu1):
if self.sg:
return sum(train_sentence_sg(self, sentence, alpha, work) for sentence in job)
else:
return sum(train_sentence_cbow(self, sentence, alpha, work, neu1) for sentence in job)