def _init_model(self, param):
super(HomoNNClient, self)._init_model(param)
self.batch_size = param.batch_size
self.aggregate_every_n_epoch = 1
self.nn_define = param.nn_define
self.config_type = param.config_type
self.optimizer = param.optimizer
self.loss = param.loss
self.metrics = param.metrics
self.data_converter = nn_model.get_data_converter(self.config_type)
self.model_builder = nn_model.get_nn_builder(config_type=self.config_type)
def _init_model(self, param: HomoNNParam):
super()._init_model(param=param)
self.batch_size = param.batch_size
self.aggregate_every_n_epoch = param.aggregate_every_n_epoch
self.nn_define = param.nn_define
self.config_type = param.config_type
self.optimizer = param.optimizer
self.loss = param.loss
self.metrics = param.metrics
self.encode_label = param.encode_label
self.data_converter = nn_model.get_data_converter(self.config_type)
self.model_builder = nn_model.get_nn_builder(config_type=self.config_type)
def client_set_params(self, param):
self.nn_model = None
self._summary = dict(loss_history=[], is_converged=False)
self._header = []
self._label_align_mapping = None
self.param = param
self.enable_secure_aggregate = param.secure_aggregate
self.max_aggregate_iteration_num = param.max_iter
self.batch_size = param.batch_size
self.aggregate_every_n_epoch = param.aggregate_every_n_epoch
self.nn_define = param.nn_define
self.config_type = param.config_type
self.optimizer = param.optimizer
self.loss = param.loss
self.metrics = param.metrics
self.encode_label = param.encode_label
self.data_converter = nn_model.get_data_converter(self.config_type)
self.model_builder = nn_model.get_nn_builder(config_type=self.config_type)