def train(self, sess, progress, summary_writer):
heading = lambda s: utils.heading(s, '(' + self._config.model_name +
')')
trained_on_sentences = 0
start_time = time.time()
unsupervised_loss_total, unsupervised_loss_count = 0, 0
supervised_loss_total, supervised_loss_count = 0, 0
for mb in self._get_training_mbs(progress.unlabeled_data_reader):
if mb.task_name != 'unlabeled':
loss = self._model.train_labeled(sess, mb)
print('train loss', loss)
supervised_loss_total += loss
supervised_loss_count += 1
if mb.task_name == 'unlabeled':
self._model.run_teacher(sess, mb)
loss = self._model.train_unlabeled(sess, mb)
unsupervised_loss_total += loss
unsupervised_loss_count += 1
mb.teacher_predictions.clear()
trained_on_sentences += mb.size
global_step = self._model.get_global_step(sess)
if global_step % self._config.print_every == 0:
supervised_loss_reported = supervised_loss_total / max(
1, supervised_loss_count)
utils.log(
'step {:} - '
'supervised loss: {:.3f} - '
'unsupervised loss: {:.3f} - '
'{:.1f} sentences per second'.format(
global_step, supervised_loss_reported,
unsupervised_loss_total /
max(1, unsupervised_loss_count),
trained_on_sentences / (time.time() - start_time)))
unsupervised_loss_total, unsupervised_loss_count = 0, 0
supervised_loss_total, supervised_loss_count = 0, 0
summary_writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='loss',
simple_value=supervised_loss_reported)
]), global_step)
if global_step % self._config.eval_dev_every == 0:
heading('EVAL ON DEV')
self.evaluate_all_tasks(sess, summary_writer, progress.history)
progress.save_if_best_dev_model(sess, global_step)
utils.log()
if global_step % self._config.eval_train_every == 0:
heading('EVAL ON TRAIN')
self.evaluate_all_tasks(sess, summary_writer, progress.history,
True)
utils.log()
if global_step % self._config.save_model_every == 0:
heading('CHECKPOINTING MODEL')
progress.write(sess, global_step)
utils.log()
def main():
utils.heading('SETUP')
config = configure.Config(mode=FLAGS.mode, model_name=FLAGS.model_name)
config.write()
with tf.Graph().as_default() as graph:
model_trainer = trainer.Trainer(config)
summary_writer = tf.summary.FileWriter(config.summaries_dir)
checkpoints_saver = tf.train.Saver(max_to_keep=1)
best_model_saver = tf.train.Saver(max_to_keep=1)
init_op = tf.global_variables_initializer()
graph.finalize()
with tf.Session() as sess:
sess.run(init_op)
progress = training_progress.TrainingProgress(
config, sess, checkpoints_saver, best_model_saver,
config.mode == 'train')
utils.log()
if config.mode == 'train':
utils.heading('START TRAINING ({:})'.format(config.model_name))
model_trainer.train(sess, progress, summary_writer)
elif config.mode == 'eval':
utils.heading('RUN EVALUATION ({:})'.format(config.model_name))
progress.best_model_saver.restore(
sess, tf.train.latest_checkpoint(config.checkpoints_dir))
model_trainer.evaluate_all_tasks(sess, summary_writer, None)
else:
raise ValueError('Mode must be "train" or "eval"')
def main():
utils.heading('SETUP')
config = configure.Config(mode=FLAGS.mode, model_name=FLAGS.model_name)
config.write()
if config.mode == 'encode':
word_vocab = embeddings.get_word_vocab(config)
sentence = "Squirrels , for example , would show up , look for the peanut , go away .".split()
sentence = ([word_vocab[embeddings.normalize_word(w)] for w in sentence])
print(sentence)
return
if config.mode == 'decode':
word_vocab_reversed = embeddings.get_word_vocab_reversed(config)
sentence = "25709 33 42 879 33 86 304 92 33 676 42 32 13406 33 273 445 34".split()
sentence = ([word_vocab_reversed[int(w)] for w in sentence])
print(sentence)
return
if config.mode == 'encode-vi':
word_vocab_vi = embeddings.get_word_vocab_vi(config)
print(len(word_vocab_vi))
sentence = "Mỗi_một khoa_học_gia đều thuộc một nhóm nghiên_cứu , và mỗi nhóm đều nghiên_cứu rất nhiều đề_tài đa_dạng .".split()
sentence = ([word_vocab_vi[embeddings.normalize_word(w)] for w in sentence])
print(sentence)
return
if config.mode == 'decode-vi':
word_vocab_reversed_vi = embeddings.get_word_vocab_reversed_vi(config)
sentence = "8976 32085 129 178 17 261 381 5 7 195 261 129 381 60 37 2474 1903 6".split()
sentence = ([word_vocab_reversed_vi[int(w)] for w in sentence])
print(sentence)
return
if config.mode == 'embed':
word_embeddings = embeddings.get_word_embeddings(config)
word = 50
embed = word_embeddings[word]
print(' '.join(str(x) for x in embed))
return
if config.mode == 'embed-vi':
word_embeddings_vi = embeddings.get_word_embeddings_vi(config)
word = 50
embed = word_embeddings_vi[word]
print(' '.join(str(x) for x in embed))
return
with tf.Graph().as_default() as graph:
model_trainer = trainer.Trainer(config)
summary_writer = tf.summary.FileWriter(config.summaries_dir)
checkpoints_saver = tf.train.Saver(max_to_keep=1)
best_model_saver = tf.train.Saver(max_to_keep=1)
init_op = tf.global_variables_initializer()
graph.finalize()
with tf.Session() as sess:
sess.run(init_op)
progress = training_progress.TrainingProgress(
config, sess, checkpoints_saver, best_model_saver,
config.mode == 'train')
utils.log()
if config.mode == 'train':
#summary_writer.add_graph(sess.graph)
utils.heading('START TRAINING ({:})'.format(config.model_name))
model_trainer.train(sess, progress, summary_writer)
elif config.mode == 'eval-train':
utils.heading('RUN EVALUATION ({:})'.format(config.model_name))
progress.best_model_saver.restore(sess, tf.train.latest_checkpoint(
config.checkpoints_dir))
model_trainer.evaluate_all_tasks(sess, summary_writer, None, train_set=True)
elif config.mode == 'eval-dev':
utils.heading('RUN EVALUATION ({:})'.format(config.model_name))
progress.best_model_saver.restore(sess, tf.train.latest_checkpoint(
config.checkpoints_dir))
model_trainer.evaluate_all_tasks(sess, summary_writer, None, train_set=False)
elif config.mode == 'infer':
utils.heading('START INFER ({:})'.format(config.model_name))
progress.best_model_saver.restore(sess, tf.train.latest_checkpoint(
config.checkpoints_dir))
model_trainer.infer(sess)
elif config.mode == 'translate':
utils.heading('START TRANSLATE ({:})'.format(config.model_name))
progress.best_model_saver.restore(sess, tf.train.latest_checkpoint(
config.checkpoints_dir))
model_trainer.translate(sess)
elif config.mode == 'eval-translate-train':
utils.heading('RUN EVALUATION ({:})'.format(config.model_name))
progress.best_model_saver.restore(sess, tf.train.latest_checkpoint(
config.checkpoints_dir))
model_trainer.evaluate_all_tasks(sess, summary_writer, None, train_set=True, is_translate=True)
elif config.mode == 'eval-translate-dev':
utils.heading('RUN EVALUATION ({:})'.format(config.model_name))
progress.best_model_saver.restore(sess, tf.train.latest_checkpoint(
config.checkpoints_dir))
model_trainer.evaluate_all_tasks(sess, summary_writer, None, train_set=False, is_translate=True)
else:
raise ValueError('Mode must be "train" or "eval"')