def create_model(session,
gen_config,
vocab_size,
forward_only,
name_scope,
initializer=None):
"""Create translation model and initialize or load parameters in session."""
with tf.variable_scope(name_or_scope=name_scope, initializer=initializer):
model = seq2seq_model.Seq2SeqModel(gen_config,
vocab_size=vocab_size,
name_scope=name_scope,
forward_only=forward_only)
gen_ckpt_dir = os.path.abspath(
os.path.join(gen_config.train_dir, "checkpoints"))
ckpt = tf.train.get_checkpoint_state(gen_ckpt_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("Reading Gen model parameters from %s" %
ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created Gen model with fresh parameters.")
gen_global_variables = [
gv for gv in tf.global_variables() if name_scope in gv.name
]
session.run(tf.variables_initializer(gen_global_variables))
return model
def create_model(session, gen_config, initializer=None, name="gen_model"):
"""Create translation model and initialize or load parameters in session."""
with tf.variable_scope(name_or_scope=name, initializer=initializer):
model = seq2seq_model.Seq2SeqModel(gen_config)
gen_ckpt_dir = os.path.abspath(os.path.join(gen_config.data_dir, "checkpoints"))
ckpt = tf.train.get_checkpoint_state(gen_ckpt_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("Reading Gen model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created Gen model with fresh parameters.")
session.run(tf.global_variables_initializer())
return model
def create_model(session,
gen_config,
forward_only,
name_scope,
word2id,
initializer=None):
"""Create translation model and initialize or load parameters in session."""
with tf.variable_scope(name_or_scope=name_scope, initializer=initializer):
model = seq2seq_model.Seq2SeqModel(gen_config,
name_scope=name_scope,
forward_only=forward_only)
if not gen_config.adv:
gen_ckpt_dir = os.path.abspath(
os.path.join(gen_config.data_dir, 'gen_model', "checkpoints"))
else:
gen_ckpt_dir = os.path.abspath(
os.path.join(
gen_config.model_dir, 'gen_model',
"data-{}_pre_embed-{}_exp-{}".format(
gen_config.data_id, gen_config.pre_embed, 1)))
# gen_config.continue_train==True will overwrite the previous gen_ckpt_dir and continue adv-training
if gen_config.continue_train:
gen_ckpt_dir = os.path.abspath(
os.path.join(
gen_config.model_dir, 'gen_model',
"data-{}_pre_embed-{}_ent-{}_exp-{}_teacher-{}".format(
gen_config.data_id, gen_config.pre_embed,
gen_config.ent_weight, gen_config.exp_id,
gen_config.teacher_forcing)))
print("check model path: %s" % gen_ckpt_dir)
ckpt = tf.train.get_checkpoint_state(gen_ckpt_dir)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("Reading Gen model parameters from %s" %
ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
load_embeddings_generator(session, name_scope, word2id,
gen_config.word_embedding, True)
# reset_lr = model.learning_rate.assign(gen_config.learning_rate)
# session.run(reset_lr)
else:
print("Create Gen model with fresh parameters.")
gen_global_variables = [
gv for gv in tf.global_variables() if name_scope in gv.name
]
session.run(tf.variables_initializer(gen_global_variables))
print("Finished Creating Gen model with fresh parameters.")
if gen_config.pre_embed:
load_embeddings_generator(session, name_scope, word2id,
gen_config.word_embedding, False)
return model
def create_model(session, gen_config, forward_only):
"""Create translation model and initialize or load parameters in session."""
model = seq2seq_model.Seq2SeqModel(
gen_config.vocab_size, gen_config.vocab_size, _buckets,
gen_config.size, gen_config.num_layers, gen_config.max_gradient_norm, gen_config.batch_size,
gen_config.learning_rate, gen_config.learning_rate_decay_factor, forward_only=forward_only)
ckpt = tf.train.get_checkpoint_state(gen_config.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 Gen_RNN model with fresh parameters.")
session.run(tf.global_variables_initializer())
return model
def create_model(session, gen_config, forward_only, name_scope, initializer=None):
"""Create translation model and initialize or load parameters in session.
if model exist then reuse it
"""
print('its ok here ===================================================================================')
with tf.variable_scope(name_or_scope=name_scope, initializer=initializer):
model = seq2seq_model.Seq2SeqModel(gen_config, name_scope=name_scope, forward_only=forward_only)
print('its Fine here ===================================================================================')
gen_ckpt_dir = os.path.abspath(os.path.join(gen_config.train_dir, "checkpoints"))
print('its good here ===================================================================================')
ckpt = tf.train.get_checkpoint_state(gen_ckpt_dir)
print('its all ok here ===================================================================================')
#if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("Reading Gen model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(session, ckpt.model_checkpoint_path)
# else:
# print("Created Gen model with fresh parameters.")
# gen_global_variables = [gv for gv in tf.global_variables() if name_scope in gv.name] # 只是因為在 adversrial 時候要看是去 initalize 哪一個 model
# session.run(tf.variables_initializer(gen_global_variables))
return model