def _init_ner_model(self, session, ckpt_path):
"""Create ner Tagger model and initialize or load parameters in session."""
# initilize config
config = ner_model.get_config(self.name)
if config is None:
print("WARNING: Input model name %s has no configuration..." %
self.name)
config.batch_size = 1
config.num_steps = 1 # iterator one token per time
model_var_scope = get_model_var_scope(self.var_scope, self.name)
print("NOTICE: Input NER Model Var Scope Name '%s'" % model_var_scope)
# Check if self.model already exist
if self.model is None:
with tf.variable_scope(model_var_scope, reuse=tf.AUTO_REUSE):
self.model = ner_model.NERTagger(
is_training=True,
config=config) # save object after is_training
#else: # Model Graph Def already exist
# print ("DEBUG: Model Def already exists")
# update model parameters
if len(glob.glob(ckpt_path + '.data*')
) > 0: # file exist with pattern: 'ner.ckpt.data*'
print("NOTICE: Loading model parameters from %s" % ckpt_path)
all_vars = tf.global_variables()
model_vars = [
k for k in all_vars if model_var_scope in k.name.split("/")
] # e.g. ner_var_scope_zh
tf.train.Saver(model_vars).restore(session, ckpt_path)
else:
print(
"NOTICE: Model not found, Try to run method: deepnlp.download(module='ner', name='%s')"
% self.name)
print("NOTICE: Created with fresh parameters.")
session.run(tf.global_variables_initializer())
def _init_ner_model(self, session, ckpt_path):
"""Create ner Tagger model and initialize or load parameters in session."""
# initilize config
config = ner_model.get_config(self.lang)
config.batch_size = 1
config.num_steps = 1 # iterator one token per time
with tf.variable_scope("ner_var_scope"):
model = ner_model.NERTagger(is_training=True, config=config) # save object after is_training
if len(glob.glob(ckpt_path + '.data*')) > 0: # file exist with pattern: 'ner.ckpt.data*'
print("Loading model parameters from %s" % ckpt_path)
all_vars = tf.global_variables()
model_vars = [k for k in all_vars if k.name.startswith("ner_var_scope")]
tf.train.Saver(model_vars).restore(session, ckpt_path)
else:
print("Model not found, created with fresh parameters.")
session.run(tf.global_variables_initializer())
return model