scalable_sage模型save_embedding导出向量全部都一模一样

### 数据说明
label: 长度25的multi-hot编码(大部分只有一个类别，为ont-hot)
特征： 长度100的 dense vector
Graph Load Finish! Node Count:523375 Edge Count:37921281

### 训练代码
```
utils_flags.set_defaults(
mode='train',
train_node_type=1,
data_dir='/data1/jwr/project/gcn/data/20191217',
max_id=523374,
label_idx=0,
label_dim=25,
feature_idx=3,
feature_dim=100,
dim=100,
fanouts=[10, 10],
aggregator='meanpool',
use_residual=False,
batch_size=256,
optimizer='adam',
learning_rate=0.008,
store_learning_rate=0.002,
num_epochs=2,
model_dir='ckpt/' + str(int(time.time())),
model='scalable_sage')
```

### 训练日志
```
INFO:tensorflow:f1 = 0.57615125, loss = 0.072720595, step = 1940 (0.170 sec)
INFO:tensorflow:f1 = 0.576606, loss = 0.06780708, step = 1960 (0.183 sec)
INFO:tensorflow:f1 = 0.57674795, loss = 0.07061373, step = 1980 (0.175 sec)
INFO:tensorflow:f1 = 0.57738733, loss = 0.067535296, step = 2000 (0.178 sec)
INFO:tensorflow:f1 = 0.5778596, loss = 0.06816011, step = 2020 (0.174 sec)
INFO:tensorflow:f1 = 0.5778974, loss = 0.07258644, step = 2040 (0.179 sec)
INFO:tensorflow:Saving checkpoints for 2044 into ckpt/1577072290/model.ckpt.
```
### 导出向量出问题
训练好之后，将mode='save_embedding'导出节点向量，**所有节点的embedding向量都一样**，更换过**batch_size, optimizer, learning_rate, aggregator**等参数，全部都没用，导出节点的embedding向量依然一样。

The report names no source files or tests. Start at the save_embedding mode and scalable_sage model using the supplied training configuration, then trace the export path and compare outputs across node IDs; done means exported embeddings are no longer all identical and the behavior is covered by a relevant test.

Written by the indexing model from the issue text.