提交 9392647a 编写于 作者: F frankwhzhang

fix readme

上级 ed8b386c
......@@ -36,6 +36,17 @@
sh run.sh
```
原始的数据格式为13个dense部分特征+离散化特征,用'\t'切分
```
0 1 1 5 0 1382 4 15 2 181 1 2 2 68fd1e64 80e26c9b fb936136 7b4723c4 25c83c98 7e0ccccf de7995b8 1f89b562 a73ee510 a8cd5504 b2cb9c98 37c9c164 2824a5f6 1adce6ef 8ba8b39a 891b62e7 e5ba7672 f54016b9 21ddcdc9 b1252a9d 07b5194c 3a171ecb c5c50484 e8b83407 9727dd16
```
经过get_slot_data.py处理后,得到如下数据, dense_feature中的值会merge在一起,对应net.py中的self._dense_data_var, '1:715353'表示net.py中的self._sparse_data_var[1] = 715353
```
click:0 dense_feature:0.05 dense_feature:0.00663349917081 dense_feature:0.05 dense_feature:0.0 dense_feature:0.02159375 dense_feature:0.008 dense_feature:0.15 dense_feature:0.04 dense_feature:0.362 dense_feature:0.1 dense_feature:0.2 dense_feature:0.0 dense_feature:0.04 1:715353 2:817085 3:851010 4:833725 5:286835 6:948614 7:881652 8:507110 9:27346 10:646986 11:643076 12:200960 13:18464 14:202774 15:532679 16:729573 17:342789 18:562805 19:880474 20:984402 21:666449 22:26235 23:700326 24:452909 25:884722 26:787527
```
## 环境
PaddlePaddle 1.7.2
......@@ -98,7 +109,9 @@ python -m paddlerec.run -m paddlerec.models.rank.fibinet
## 复现论文&模型效果
用原论文的大数据复现论文效果需要在config.py中修改batch_size=1000, thread_num=8, epoch_num=4
用原论文的完整数据复现论文效果需要在config.py中修改batch_size=1000, thread_num=8, epoch_num=4
使用gpu单卡训练26h 测试auc:0.79
修改后运行有两种方案:
```
......@@ -110,8 +123,9 @@ python -m paddlerec.run -m paddlerec.models.rank.fibinet #运行
python -m paddlerec.run -m /home/your/dir/config.py #调试模式 直接指定本地config的绝对路径
```
## 结果展示
训练
样例数据训练结果展示
```
Running SingleStartup.
......@@ -133,7 +147,7 @@ batch: 1800, AUC: [0.85260467], BATCH_AUC: [0.92847032]
epoch 3 done, use time: 1618.1106688976288
```
预测
样例数据预测结果展示
```
load persistables from increment_model/3
......
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