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# 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()
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