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# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Runs training for CVT text models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import bisect
import time
import numpy as np
import tensorflow as tf
from base import utils
from model import multitask_model
from task_specific import task_definitions
class Trainer(object):
def __init__(self, config):
self._config = config
self.tasks = [task_definitions.get_task(self._config, task_name)
for task_name in self._config.task_names]
utils.log('Loading Pretrained Embeddings')
pretrained_embeddings = utils.load_cpickle(self._config.word_embeddings)
utils.log('Building Model')
self._model = multitask_model.Model(
self._config, pretrained_embeddings, self.tasks)
utils.log()
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)
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:
utils.log('step {:} - '
'supervised loss: {:.2f} - '
'unsupervised loss: {:.2f} - '
'{:.1f} sentences per second'.format(
global_step,
supervised_loss_total / max(1, supervised_loss_count),
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
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 evaluate_all_tasks(self, sess, summary_writer, history, train_set=False):
for task in self.tasks:
results = self._evaluate_task(sess, task, summary_writer, train_set)
if history is not None:
results.append(('step', self._model.get_global_step(sess)))
history.append(results)
if history is not None:
utils.write_cpickle(history, self._config.history_file)
def _evaluate_task(self, sess, task, summary_writer, train_set):
scorer = task.get_scorer()
data = task.train_set if train_set else task.val_set
for i, mb in enumerate(data.get_minibatches(self._config.test_batch_size)):
loss, batch_preds = self._model.test(sess, mb)
scorer.update(mb.examples, batch_preds, loss)
results = scorer.get_results(task.name +
('_train_' if train_set else '_dev_'))
utils.log(task.name.upper() + ': ' + scorer.results_str())
write_summary(summary_writer, results,
global_step=self._model.get_global_step(sess))
return results
def _get_training_mbs(self, unlabeled_data_reader):
datasets = [task.train_set for task in self.tasks]
weights = [np.sqrt(dataset.size) for dataset in datasets]
thresholds = np.cumsum([w / np.sum(weights) for w in weights])
labeled_mbs = [dataset.endless_minibatches(self._config.train_batch_size)
for dataset in datasets]
unlabeled_mbs = unlabeled_data_reader.endless_minibatches()
while True:
dataset_ind = bisect.bisect(thresholds, np.random.random())
yield next(labeled_mbs[dataset_ind])
if self._config.is_semisup:
yield next(unlabeled_mbs)
def write_summary(writer, results, global_step):
for k, v in results:
if 'f1' in k or 'acc' in k or 'loss' in k:
writer.add_summary(tf.Summary(
value=[tf.Summary.Value(tag=k, simple_value=v)]), global_step)
writer.flush()
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