def restore_best_model():
"""Load bestmodel file from eval directory, add variables for adagrad, and save to train directory"""
tf.logging.info("Restoring bestmodel for training...")
# Initialize all vars in the model
sess = tf.Session(config=util.get_config())
print("Initializing all variables...")
sess.run(tf.initialize_all_variables())
# Restore the best model from eval dir
saver = tf.train.Saver(
[v for v in tf.all_variables() if "Adagrad" not in v.name])
print("Restoring all non-adagrad variables from best model in eval dir...")
curr_ckpt = util.load_ckpt(saver, sess, "eval")
print("Restored %s." % curr_ckpt)
# Save this model to train dir and quit
new_model_name = curr_ckpt.split("/")[-1].replace("bestmodel", "model")
new_fname = os.path.join(FLAGS.log_root, "train", new_model_name)
print("Saving model to %s..." % (new_fname))
new_saver = tf.train.Saver(
) # this saver saves all variables that now exist, including Adagrad variables
new_saver.save(sess, new_fname)
print("Saved.")
exit()
def convert_to_coverage_model():
"""Load non-coverage checkpoint, add initialized extra variables for coverage, and save as new checkpoint"""
tf.logging.info("converting non-coverage model to coverage model..")
# initialize an entire coverage model from scratch
sess = tf.Session(config=util.get_config())
print("initializing everything...")
sess.run(tf.global_variables_initializer())
# load all non-coverage weights from checkpoint
saver = tf.train.Saver([
v for v in tf.global_variables()
if "coverage" not in v.name and "Adagrad" not in v.name
])
print("restoring non-coverage variables...")
curr_ckpt = util.load_ckpt(saver, sess)
print("restored.")
# save this model and quit
new_fname = curr_ckpt + '_cov_init'
print("saving model to %s..." % (new_fname))
new_saver = tf.train.Saver(
) # this one will save all variables that now exist
new_saver.save(sess, new_fname)
print("saved.")
exit()
def setup_training(model, batcher):
"""Does setup before starting training (run_training)"""
train_dir = os.path.join(FLAGS.log_root, "train")
if not os.path.exists(train_dir): os.makedirs(train_dir)
model.build_graph() # build the graph
if FLAGS.convert_to_coverage_model:
assert FLAGS.coverage, "To convert your non-coverage model to a coverage model, run with convert_to_coverage_model=True and coverage=True"
convert_to_coverage_model()
if FLAGS.restore_best_model:
restore_best_model()
saver = tf.train.Saver(max_to_keep=3) # keep 3 checkpoints at a time
sv = tf.train.Supervisor(
logdir=train_dir,
is_chief=True,
saver=saver,
summary_op=None,
save_summaries_secs=60, # save summaries for tensorboard every 60 secs
save_model_secs=60, # checkpoint every 60 secs
global_step=model.global_step)
summary_writer = sv.summary_writer
tf.logging.info("Preparing or waiting for session...")
sess_context_manager = sv.prepare_or_wait_for_session(
config=util.get_config())
tf.logging.info("Created session.")
try:
run_training(
model, batcher, sess_context_manager, sv,
summary_writer) # this is an infinite loop until interrupted
except KeyboardInterrupt:
tf.logging.info(
"Caught keyboard interrupt on worker. Stopping supervisor...")
sv.stop()
def __init__(self, model, batcher, vocab):
"""Initialize decoder.
Args:
model: a Seq2SeqAttentionModel object.
batcher: a Batcher object.
vocab: Vocabulary object
"""
self._model = model
self._model.build_graph()
self._batcher = batcher
self._vocab = vocab
self._saver = tf.train.Saver(
) # we use this to load checkpoints for decoding
self._sess = tf.Session(config=util.get_config())
# Load an initial checkpoint to use for decoding
ckpt_path = util.load_ckpt(self._saver, self._sess)
self._decode_dir = FLAGS.outputs_dir
#if FLAGS.single_pass:
# # Make a descriptive decode directory name
# ckpt_name = "ckpt-" + ckpt_path.split('-')[-1] # this is something of the form "ckpt-123456"
# self._decode_dir = os.path.join(FLAGS.log_root, get_decode_dir_name(ckpt_name))
# if os.path.exists(self._decode_dir):
# shutil.rmtree(self._decode_dir)
# #raise Exception("single_pass decode directory %s should not already exist" % self._decode_dir)
#else: # Generic decode dir name
# #self._decode_dir = os.path.join(FLAGS.log_root, "decode")
# self._decode_dir = os.path.join(FLAGS.outputs_dir, get_decode_dir_name(ckpt_name))
# Make the decode dir if necessary
if not os.path.exists(self._decode_dir): os.mkdir(self._decode_dir)
def run_eval(model, batcher, vocab):
"""Repeatedly runs eval iterations, logging to screen and writing summaries. Saves the model with the best loss seen so far."""
model.build_graph() # build the graph
saver = tf.train.Saver(
max_to_keep=3) # we will keep 3 best checkpoints at a time
sess = tf.Session(config=util.get_config())
eval_dir = os.path.join(
FLAGS.log_root, "eval") # make a subdir of the root dir for eval data
bestmodel_save_path = os.path.join(
eval_dir,
'bestmodel') # this is where checkpoints of best models are saved
summary_writer = tf.summary.FileWriter(eval_dir)
running_avg_loss = 0 # the eval job keeps a smoother, running average loss to tell it when to implement early stopping
best_loss = None # will hold the best loss achieved so far
while True:
_ = util.load_ckpt(saver, sess) # load a new checkpoint
batch = batcher.next_batch() # get the next batch
# run eval on the batch
t0 = time.time()
results = model.run_eval_step(sess, batch)
t1 = time.time()
tf.logging.info('seconds for batch: %.2f', t1 - t0)
# print the loss and coverage loss to screen
loss = results['loss']
tf.logging.info('loss: %f', loss)
if FLAGS.coverage:
coverage_loss = results['coverage_loss']
tf.logging.info("coverage_loss: %f", coverage_loss)
# add summaries
summaries = results['summaries']
train_step = results['global_step']
summary_writer.add_summary(summaries, train_step)
# calculate running avg loss
running_avg_loss = calc_running_avg_loss(np.asscalar(loss),
running_avg_loss,
summary_writer, train_step)
# If running_avg_loss is best so far, save this checkpoint (early stopping).
# These checkpoints will appear as bestmodel-<iteration_number> in the eval dir
if best_loss is None or running_avg_loss < best_loss:
tf.logging.info(
'Found new best model with %.3f running_avg_loss. Saving to %s',
running_avg_loss, bestmodel_save_path)
saver.save(sess,
bestmodel_save_path,
global_step=train_step,
latest_filename='checkpoint_best')
best_loss = running_avg_loss
# flush the summary writer every so often
if train_step % 100 == 0:
summary_writer.flush()