(self, param_path, load_states=True)
Loads the model parameters (per layer weights, epochs run, optimizer
        states) saved in param_path from serialize().

        Arguments:
            param_path (str): File containing serialized python dict with layer
                              weights and states.
            load_states (bool):  if False, then only the weights will be loaded
                                 into a model in which the layers have already been
                                 created, otherwise will (re)create the layers from
                                 the serialized parameters and set the learning
                                 states as well
Loads the model parameters (per layer weights, epochs run, optimizer
        states) saved in param_path from serialize().
Loads the model parameters ( per layer weights epochs run optimizer states ) saved in param_path from serialize () .
def load_params(self, param_path, load_states=True):
        """
        Loads the model parameters (per layer weights, epochs run, optimizer
        states) saved in param_path from serialize().

        Arguments:
            param_path (str): File containing serialized python dict with layer
                              weights and states.
            load_states (bool):  if False, then only the weights will be loaded
                                 into a model in which the layers have already been
                                 created, otherwise will (re)create the layers from
                                 the serialized parameters and set the learning
                                 states as well
        """
        self.deserialize(load_obj(param_path), load_states=load_states)
        logger.info('Model weights loaded from %s', param_path)
def load_params ( self , param_path , load_states = True ) : self . deserialize ( load_obj ( param_path ) , load_states = load_states ) logger . info ( 'Model weights loaded from %s' , param_path )