def main(_):
"""Trains Language 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.language_model_training()
train_utils.run_training(train_op, loss, global_step)
def main(_):
"""Trains Language 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.language_model_training()
train_utils.run_training(train_op, loss, global_step)
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)
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)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
tf.gfile.MakeDirs(FLAGS.eval_dir)
tf.logging.info('Building eval graph...')
output = graphs.get_model().eval_graph(FLAGS.eval_data)
eval_ops, moving_averaged_variables = output
saver = tf.train.Saver(moving_averaged_variables)
summary_writer = tf.summary.FileWriter(FLAGS.eval_dir,
graph=tf.get_default_graph())
while True:
run_eval(eval_ops, summary_writer, saver)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
tf.gfile.MakeDirs(FLAGS.eval_dir)
tf.logging.info('Building eval graph...')
output = graphs.get_model().eval_graph(FLAGS.eval_data)
eval_ops, moving_averaged_variables = output
saver = tf.train.Saver(moving_averaged_variables)
summary_writer = tf.summary.FileWriter(
FLAGS.eval_dir, graph=tf.get_default_graph())
while True:
run_eval(eval_ops, summary_writer, saver)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
