From 907b2145a60b35f6ab0f10ea6dcf56eecbf6bac0 Mon Sep 17 00:00:00 2001 From: overlordmax <515704170@qq.com> Date: Tue, 7 Jul 2020 14:35:35 +0800 Subject: [PATCH] fix readme.md and config.yaml --- README.md | 1 + README_CN.md | 1 + models/rank/fibinet/config.yaml | 12 ++++++------ models/rank/flen/config.yaml | 12 ++++++------ 4 files changed, 14 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 3a2d2f8d..b5939cc1 100644 --- a/README.md +++ b/README.md @@ -56,6 +56,7 @@ | Rank | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) | | Rank | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) | | Rank | [DIEN](models/rank/dien/model.py) | ✓ | x | ✓ | x | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423) | + | Rank | [AutoInt](models/rank/AutoInt/model.py) | ✓ | x | ✓ | x | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf) | | Rank | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) | | Rank | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) | | Rank | [Fibinet](models/rank/fibinet/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) | diff --git a/README_CN.md b/README_CN.md index aadf9449..de3440dd 100644 --- a/README_CN.md +++ b/README_CN.md @@ -61,6 +61,7 @@ | 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ | x | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023) | | 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ | x | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823) | | 排序 | [DIEN](models/rank/dien/model.py) | ✓ | x | ✓ | x | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://www.aaai.org/ojs/index.php/AAAI/article/view/4545/4423) | + | Rank | [AutoInt](models/rank/AutoInt/model.py) | ✓ | x | ✓ | x | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/pdf/1810.11921.pdf) | | 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ | x | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454) | | 排序 | [FGCNN](models/rank/fgcnn/model.py) | ✓ | ✓ | ✓ | ✓ | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1904.04447.pdf) | | 排序 | [Fibinet](models/rank/fibinet/model.py) | ✓ | ✓ | ✓ | ✓ | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf) | diff --git a/models/rank/fibinet/config.yaml b/models/rank/fibinet/config.yaml index eed0fbe8..091915e6 100644 --- a/models/rank/fibinet/config.yaml +++ b/models/rank/fibinet/config.yaml @@ -59,8 +59,8 @@ runner: device: cpu save_checkpoint_interval: 2 # save model interval of epochs save_inference_interval: 4 # save inference - save_checkpoint_path: "increment_model" # save checkpoint path - save_inference_path: "inference" # save inference path + save_checkpoint_path: "increment_model_fibinet" # save checkpoint path + save_inference_path: "inference_fibinet" # save inference path save_inference_feed_varnames: [] # feed vars of save inference save_inference_fetch_varnames: [] # fetch vars of save inference init_model_path: "" # load model path @@ -75,8 +75,8 @@ runner: device: gpu save_checkpoint_interval: 1 # save model interval of epochs save_inference_interval: 4 # save inference - save_checkpoint_path: "increment_model" # save checkpoint path - save_inference_path: "inference" # save inference path + save_checkpoint_path: "increment_model_fibinet" # save checkpoint path + save_inference_path: "inference_fibinet" # save inference path save_inference_feed_varnames: [] # feed vars of save inference save_inference_fetch_varnames: [] # fetch vars of save inference init_model_path: "" # load model path @@ -87,14 +87,14 @@ runner: class: infer # device to run training or infer device: cpu - init_model_path: "increment_model" # load model path + init_model_path: "increment_model_fibinet" # load model path phases: [phase2] - name: single_gpu_infer class: infer # device to run training or infer device: gpu - init_model_path: "increment_model" # load model path + init_model_path: "increment_model_fibinet" # load model path phases: [phase2] # runner will run all the phase in each epoch diff --git a/models/rank/flen/config.yaml b/models/rank/flen/config.yaml index 237d3be4..a2dad399 100644 --- a/models/rank/flen/config.yaml +++ b/models/rank/flen/config.yaml @@ -57,8 +57,8 @@ runner: device: cpu save_checkpoint_interval: 1 # save model interval of epochs save_inference_interval: 4 # save inference - save_checkpoint_path: "increment_model" # save checkpoint path - save_inference_path: "inference" # save inference path + save_checkpoint_path: "increment_model_flen" # save checkpoint path + save_inference_path: "inference_flen" # save inference path save_inference_feed_varnames: [] # feed vars of save inference save_inference_fetch_varnames: [] # fetch vars of save inference init_model_path: "" # load model path @@ -73,8 +73,8 @@ runner: device: gpu save_checkpoint_interval: 1 # save model interval of epochs save_inference_interval: 4 # save inference - save_checkpoint_path: "increment_model" # save checkpoint path - save_inference_path: "inference" # save inference path + save_checkpoint_path: "increment_model_flen" # save checkpoint path + save_inference_path: "inference_flen" # save inference path save_inference_feed_varnames: [] # feed vars of save inference save_inference_fetch_varnames: [] # fetch vars of save inference init_model_path: "" # load model path @@ -85,14 +85,14 @@ runner: class: infer # device to run training or infer device: cpu - init_model_path: "increment_model" # load model path + init_model_path: "increment_model_flen" # load model path phases: [phase2] - name: single_gpu_infer class: infer # device to run training or infer device: gpu - init_model_path: "increment_model" # load model path + init_model_path: "increment_model_flen" # load model path phases: [phase2] # runner will run all the phase in each epoch -- GitLab