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)