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()