def load(self): # 加载 embeddings self.__embeddings = tf.Variable(load.Embedding.load(), dtype=tf.float32, name='embeddings') self.__trainSet = load.Data(0.0, 0.64) # 按 0.64 的比例划分训练集 self.__valSet = load.Data(0.64, 0.8) # 按 0.16 的比例划分校验集 self.__testSet = load.Data(0.8) # 按 0.2 的比例划分测试集 self.__trainSize = self.__trainSet.getSize() self.__valSize = self.__valSet.getSize() self.__testSize = self.__testSet.getSize()
def load(self): self.__train_set_list[self.net_id] = load.Data(self.net_id, 0.0, 0.8, 'train', self.IMAGE_SHAPE) self.__val_set_list[self.net_id] = load.Data(self.net_id, 0.8, 1.0, 'validation', self.IMAGE_SHAPE) self.__train_size_list[self.net_id] = self.__train_set_list[ self.net_id].get_size() self.__val_size_list[self.net_id] = self.__val_set_list[ self.net_id].get_size()
def load(self): self.__train_set_list = [] self.__val_set_list = [] self.__train_size_list = [] self.__val_size_list = [] for i in range(self.NUM_PIG): self.__train_set_list.append(load.Data(i, 0.0, 0.8, 'train')) self.__val_set_list.append(load.Data(i, 0.8, 1.0, 'validation')) self.__train_size_list.append(self.__train_set_list[i].get_size()) self.__val_size_list.append(self.__val_set_list[i].get_size())
def load(self): self.__train_set_list[self.net_id] = load.Data(self.net_id, 0.0, self.TRAIN_DATA_RATIO, 'train', self.IMAGE_SHAPE) self.__val_set_list[self.net_id] = load.Data(self.net_id, self.TRAIN_DATA_RATIO, self.VAL_DATA_END_RATIO, 'validation', self.IMAGE_SHAPE) self.__train_size_list[self.net_id] = self.__train_set_list[ self.net_id].get_size() self.__val_size_list[self.net_id] = self.__val_set_list[ self.net_id].get_size()
def load_i(self, _id): self.__train_set_list[_id] = load.Data(_id, 0.0, 0.8, 'train') self.__val_set_list[_id] = load.Data(_id, 0.8, 1.0, 'validation') self.__train_size_list[_id] = self.__train_set_list[_id].get_size() self.__val_size_list[_id] = self.__val_set_list[_id].get_size()