# Copyright 2017 Google, Inc. 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.
# ==============================================================================

"""Trains LSTM text classification model.

Model trains with adversarial or virtual adversarial training.

Computational time:
  6 hours to train 10000 steps without adversarial or virtual adversarial
    training, on 1 layer 1024 hidden units LSTM, 256 embeddings, 400 truncated
    BP, 64 minibatch and on single GPU.

  12 hours to train 10000 steps with adversarial or virtual adversarial
    training, with above condition.

To initialize embedding and LSTM cell weights from a pretrained model, set
FLAGS.pretrained_model_dir to the pretrained model's checkpoint directory.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

import graphs
import train_utils

flags = tf.app.flags
FLAGS = flags.FLAGS

flags.DEFINE_string('pretrained_model_dir', None,
                    'Directory path to pretrained model to restore from')


def main(_):
  """Trains LSTM classification model."""
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
    model = graphs.get_model()
    train_op, loss, global_step = model.classifier_training()
    train_utils.run_training(
        train_op,
        loss,
        global_step,
        variables_to_restore=model.pretrained_variables,
        pretrained_model_dir=FLAGS.pretrained_model_dir)


if __name__ == '__main__':
  tf.app.run()