- [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)
# [DeepFM Description](#contents)
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.
[Paper](https://arxiv.org/abs/1703.04247): Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
@@ -35,27 +34,24 @@ The FM and deep component share the same input raw feature vector, which enables
# [Dataset](#contents)
- [1] A dataset used in Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[J]. 2017.
# [Environment Requirements](#contents)
- Hardware（Ascend/GPU）
- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
- Hardware（Ascend/GPU/CPU）
- Prepare hardware environment with Ascend, GPU, or CPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
├─mindspore_hub_conf.md # config for mindspore hub
├─scripts
├─scripts
├─run_standalone_train.sh # launch standalone training(1p) in Ascend or GPU
├─run_distribute_train.sh # launch distributed training(8p) in Ascend
├─run_distribute_train_gpu.sh # launch distributed training(8p) in GPU
@@ -138,7 +153,8 @@ After installing MindSpore via the official website, you can start training and
Parameters for both training and evaluation can be set in config.py
- train parameters
```
```help
optional arguments:
-h, --help show this help message and exit
--dataset_path DATASET_PATH
@@ -153,8 +169,10 @@ Parameters for both training and evaluation can be set in config.py
--device_target DEVICE_TARGET
Ascend or GPU. Default: Ascend
```
- eval parameters
```
```help
optional arguments:
-h, --help show this help message and exit
--checkpoint_path CHECKPOINT_PATH
@@ -165,14 +183,13 @@ Parameters for both training and evaluation can be set in config.py
Ascend or GPU. Default: Ascend
```
## [Training Process](#contents)
### Training
### Training
- running on Ascend
```
```shell
python train.py \
--dataset_path='dataset/train' \
--ckpt_path='./checkpoint' \
@@ -181,36 +198,36 @@ Parameters for both training and evaluation can be set in config.py
--device_target='Ascend' \
--do_eval=True > ms_log/output.log 2>&1 &
```
The python command above will run in the background, you can view the results through the file `ms_log/output.log`.
After training, you'll get some checkpoint files under `./checkpoint` folder by default. The loss value are saved in loss.log file.
```
```log
2020-05-27 15:26:29 epoch: 1 step: 41257, loss is 0.498953253030777
2020-05-27 15:32:32 epoch: 2 step: 41257, loss is 0.45545706152915955
...
```
The model checkpoint will be saved in the current directory.
The model checkpoint will be saved in the current directory.
- running on GPU
To do.
### Distributed Training
- running on Ascend
```
```shell
sh scripts/run_distribute_train.sh 8 /dataset_path /rank_table_8p.json
```
The above shell script will run distribute training in the background. You can view the results through the file `log[X]/output.log`. The loss value are saved in loss.log file.
- running on GPU
To do.
To do.
## [Evaluation Process](#contents)
@@ -219,8 +236,8 @@ Parameters for both training and evaluation can be set in config.py
- evaluation on dataset when running on Ascend
Before running the command below, please check the checkpoint path used for evaluation.
```
```shell
python eval.py \
--dataset_path='dataset/test' \
--checkpoint_path='./checkpoint/deepfm.ckpt' \
@@ -228,22 +245,22 @@ Parameters for both training and evaluation can be set in config.py
OR
sh scripts/run_eval.sh 0 Ascend /dataset_path /checkpoint_path/deepfm.ckpt
```
The above python command will run in the background. You can view the results through the file "eval_output.log". The accuracy is saved in auc.log file.
zhaoting
5 years ago