def load(self, preprocessor_file, json_file, weights_file, custom_objects=None):
"""load ner application
Args:
preprocessor_file: path to load preprocessor
json_file: path to load model architecture
weights_file: path to load model weights
custom_objects: Optional dictionary mapping names (strings) to custom classes or
functions to be considered during deserialization. Must provided when
using custom layer.
"""
self.preprocessor = NERPreprocessor.load(preprocessor_file)
logging.info('Load preprocessor from {}'.format(preprocessor_file))
custom_objects = custom_objects or {}
custom_objects.update(get_custom_objects())
with open(json_file, 'r') as reader:
self.model = model_from_json(reader.read(), custom_objects=custom_objects)
logging.info('Load model architecture from {}'.format(json_file))
self.model.load_weights(weights_file)
logging.info('Load model weight from {}'.format(weights_file))
self.trainer = NERTrainer(self.model, self.preprocessor)
self.predictor = NERPredictor(self.model, self.preprocessor)
def load(self,
preprocessor_file: str,
json_file: str,
weights_file: str,
custom_objects: Optional[Dict[str, Any]] = None) -> None:
"""Load ner application from disk.
There are 3 things in total that we need to load: 1) preprocessor, which stores the
vocabulary and embedding matrix built during pre-processing and helps us prepare feature
input for ner model; 2) model architecture, which describes the framework of our ner model;
3) model weights, which stores the value of ner model's parameters.
Args:
preprocessor_file: path to load preprocessor
json_file: path to load model architecture
weights_file: path to load model weights
custom_objects: Optional dictionary mapping names (strings) to custom classes or
functions to be considered during deserialization. We will
automatically add all the custom layers of this project to
custom_objects. So you can ignore this argument in most cases unlesss
you use your own custom layer.
"""
self.preprocessor = NERPreprocessor.load(preprocessor_file)
logging.info('Load preprocessor from {}'.format(preprocessor_file))
custom_objects = custom_objects or {}
custom_objects.update(get_custom_objects())
with open(json_file, 'r') as reader:
self.model = tf.keras.models.model_from_json(
reader.read(), custom_objects=custom_objects)
logging.info('Load model architecture from {}'.format(json_file))
self.model.load_weights(weights_file)
logging.info('Load model weight from {}'.format(weights_file))
self.trainer = NERTrainer(self.model, self.preprocessor)
self.predictor = NERPredictor(self.model, self.preprocessor)