def create_or_restore_model(session, buckets, forward_only, beam_search,
beam_size):
"""Create model and initialize or load parameters"""
model = seq2seq_model.Seq2SeqModel(
source_vocab_size=config.MAX_ENC_VOCABULARY,
target_vocab_size=config.MAX_DEC_VOCABULARY,
buckets=buckets,
size=config.LAYER_SIZE,
num_layers=config.NUM_LAYERS,
max_gradient_norm=config.MAX_GRADIENT_NORM,
batch_size=config.BATCH_SIZE,
learning_rate=config.LEARNING_RATE,
learning_rate_decay_factor=config.LEARNING_RATE_DECAY_FACTOR,
beam_search=beam_search,
attention=True,
forward_only=forward_only,
beam_size=beam_size)
print("model initialized")
ckpt = tf.train.get_checkpoint_state(config.GENERATED_DIR)
# the checkpoint filename has changed in recent versions of tensorflow
checkpoint_suffix = ".index"
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path +
checkpoint_suffix):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.global_variables_initializer())
return model
def create_model(session, args, forward_only=True):
"""Create translation model and initialize or load parameters in session."""
model = seq2seq_model.Seq2SeqModel(
source_vocab_size=args.vocab_size,
target_vocab_size=args.vocab_size,
buckets=args.buckets,
size=args.size,
num_layers=args.num_layers,
max_gradient_norm=args.max_gradient_norm,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
learning_rate_decay_factor=args.learning_rate_decay_factor,
forward_only=forward_only,
)
# for tensorboard
if args.en_tfboard:
summary_writer = tf.train.SummaryWriter(args.tf_board_dir,
session.graph)
ckpt = tf.train.get_checkpoint_state(args.model_dir)
# if ckpt and gfile.Exists(ckpt.model_checkpoint_path):
if ckpt and ckpt.model_checkpoint_path:
print("Reading model parameters from %s @ %s" %
(ckpt.model_checkpoint_path, datetime.now()))
model.saver.restore(session, ckpt.model_checkpoint_path)
print("Model reloaded @ %s" % (datetime.now()))
else:
print("Created model with fresh parameters.")
session.run(tf.global_variables_initializer())
return model
def create_or_restore_model(session, buckets, forward_only):
"""Create model and initialize or load parameters"""
model = seq2seq_model.Seq2SeqModel(config.MAX_ENC_VOCABULARY,
config.MAX_DEC_VOCABULARY,
buckets,
config.LAYER_SIZE,
config.NUM_LAYERS,
config.MAX_GRADIENT_NORM,
config.BATCH_SIZE,
config.LEARNING_RATE,
config.LEARNING_RATE_DECAY_FACTOR,
forward_only=forward_only)
print("model initialized")
ckpt = tf.train.get_checkpoint_state(config.GENERATED_DIR)
# the checkpoint filename has changed in recent versions of tensorflow
checkpoint_suffix = ""
if tf.__version__ > "0.12":
checkpoint_suffix = ".index"
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path +
checkpoint_suffix):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.initialize_all_variables())
return model
def self_test():
"""Test the translation model."""
with tf.Session() as sess:
print("Self-test for neural translation model.")
# Create model with vocabularies of 10, 2 small buckets, 2 layers of 32.
model = seq2seq_model.Seq2SeqModel(10,
10, [(3, 3), (6, 6)],
32,
2,
5.0,
32,
0.3,
0.99,
num_samples=8)
sess.run(tf.global_variables_initializer())
# Fake data set for both the (3, 3) and (6, 6) bucket.
data_set = ([([1, 1], [2, 2]), ([3, 3], [4]),
([5], [6])], [([1, 1, 1, 1, 1], [2, 2, 2, 2, 2]),
([3, 3, 3], [5, 6])])
for _ in xrange(5): # Train the fake model for 5 steps.
bucket_id = random.choice([0, 1])
encoder_inputs, decoder_inputs, target_weights = model.get_batch(
data_set, bucket_id)
model.step(sess, encoder_inputs, decoder_inputs, target_weights,
bucket_id, False)
def create_model(session, args):
"""Create translation model and initialize or load parameters in session."""
model = seq2seq_model.Seq2SeqModel(
source_vocab_size=args.vocab_size,
target_vocab_size=args.vocab_size,
buckets=args.buckets,
size=args.size,
num_layers=args.num_layers,
max_gradient_norm=args.max_gradient_norm,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
learning_rate_decay_factor=args.learning_rate_decay_factor,
use_lstm=False,
)
# for tensorboard
if args.en_tfboard:
summary_writer = tf.train.SummaryWriter(args.tf_board_dir,
session.graph)
ckpt = tf.train.get_checkpoint_state(args.model_dir)
if ckpt and gfile.Exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.initialize_all_variables())
return model
def create_model(session, forward_only):
model = seq2seq_model.Seq2SeqModel(
source_vocab_size=FLAGS.vocab_size,
target_vocab_size=FLAGS.vocab_size,
buckets=BUCKETS,
size=FLAGS.size,
num_layers=FLAGS.num_layers,
max_gradient_norm=FLAGS.max_gradient_norm,
batch_size=FLAGS.batch_size,
learning_rate=FLAGS.learning_rate,
learning_rate_decay_factor=FLAGS.learning_rate_decay_factor,
use_lstm=True,
forward_only=forward_only)
ckpt = tf.train.get_checkpoint_state(FLAGS.model_dir)
if ckpt:
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.initialize_all_variables())
return model
def create_model(session, forward_only, train_dir=None):
"""Create translation model and initialize or load parameters in session.
"""
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
model = seq2seq_model.Seq2SeqModel(FLAGS.from_vocab_size,
FLAGS.to_vocab_size,
_buckets,
FLAGS.size,
FLAGS.num_layers,
FLAGS.max_gradient_norm,
FLAGS.batch_size,
FLAGS.learning_rate,
FLAGS.learning_rate_decay_factor,
forward_only=forward_only,
dtype=dtype)
train_dir = train_dir or FLAGS.train_dir
ckpt = tf.train.get_checkpoint_state(train_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.global_variables_initializer())
return model
