train_classifier.py

# 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:
  1.8 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 (Pascal Titan X, cuDNNv5).

  4 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

# Dependency imports

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()