def aspectExtractor(sentence):
# create instance of config
config = Config()
# build model
model = ASPECTModel(config)
model.build()
model.restore_session(config.dir_model)
# create dataset
test = CoNLLDataset(config.filename_test, config.processing_word,
config.processing_tag, config.max_iter)
# evaluate and interact
model.evaluate(test)
preds=interactive_shell(model , sentence)
return preds
def init():
"""
This function is to load the trained Keras model.
:return - None
"""
# load the pre-trained tensorflow model
global model, graph
# create instance of config
config = Config()
# build model
model = ASPECTModel(config)
model.build()
model.restore_session(config.dir_model)
graph = tf.get_default_graph()
def main():
# create instance of config
config = Config()
# build model
model = ASPECTModel(config)
model.build()
model.restore_session(config.dir_model)
# create dataset
test = GloveDataset(config.filename_test, config.processing_word,
config.processing_tag, config.max_iter)
# evaluate and interact
model.evaluate(test)
# saver = tf.train.Saver()
# save_path = saver.save(sess, "/export/model.ckpt")
# print("Model saved")
interactive_shell(model)
def main():
# create instance of config
config = Config()
# build model
model = ASPECTModel(config)
model.build()
# model.restore_session("results/crf/model.weights/") # optional, restore weights
# model.reinitialize_weights("proj")
# create datasets
dev = CoNLLDataset(config.filename_dev, config.processing_word,
config.processing_tag, config.max_iter)
train = CoNLLDataset(config.filename_train, config.processing_word,
config.processing_tag, config.max_iter)
# train model
model.train(train, dev)