def load(self, model_folder, use_keras_loadings=False):
if use_keras_loadings:
serialisation_path = self.get_keras_saved_path(model_folder)
print('Loading model from {}'.format(serialisation_path))
self.model.load_weights(serialisation_path)
else:
utils.load(tf.keras.backend.get_session(),
model_folder,
cp_name=self.name,
scope=self.name)
def load(self, model_folder, use_keras_loadings=False):
"""Loads TFT weights.
Args:
model_folder: Folder containing serialized models.
use_keras_loadings: Whether to load from Keras checkpoint.
Returns:
"""
if use_keras_loadings:
# Loads temporary Keras model saved during training.
serialisation_path = self.get_keras_saved_path(model_folder)
print('Loading model from {}'.format(serialisation_path))
self.model.load_weights(serialisation_path)
else:
# Loads tensorflow graph for optimal models.
utils.load(tf.keras.backend.get_session(),
model_folder,
cp_name=self.name,
scope=self.name)
