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| # PLE | ||
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| 以下是本例的简要目录结构及说明： | ||
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| ``` | ||
| ├── data # 文档 | ||
| ├── train #训练数据 | ||
| ├── train_data.txt | ||
| ├── test #测试数据 | ||
| ├── test_data.txt | ||
| ├── run.sh | ||
| ├── data_preparation.py | ||
| ├── __init__.py | ||
| ├── config.yaml #配置文件 | ||
| ├── census_reader.py #数据读取文件 | ||
| ├── model.py #模型文件 | ||
| ``` | ||
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| 注：在阅读该示例前，建议您先了解以下内容： | ||
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| [paddlerec入门教程](https://github.com/PaddlePaddle/PaddleRec/blob/master/README.md) | ||
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| ## 内容 | ||
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| - [模型简介](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/ple#模型简介) | ||
| - [数据准备](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/ple#数据准备) | ||
| - [运行环境](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/ple#运行环境) | ||
| - [快速开始](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/ple#快速开始) | ||
| - [论文复现](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/ple#论文复现) | ||
| - [进阶使用](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/ple#进阶使用) | ||
| - [FAQ](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/ple#FAQ) | ||
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| ## 模型简介 | ||
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| 多任务模型通过学习不同任务的联系和差异，可提高每个任务的学习效率和质量。但在多任务场景中经常出现跷跷板现象，即有些任务表现良好，有些任务表现变差。 论文[《Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations》](https://dl.acm.org/doi/abs/10.1145/3383313.3412236 ) ，论文提出了Progressive Layered Extraction (简称PLE)，来解决多任务学习的跷跷板现象。 | ||
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| 我们在Paddlepaddle定义PLE的网络结构，在开源数据集Census-income Data上验证模型效果。 | ||
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| 若进行精度验证，请参考[论文复现](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/ple#论文复现)部分。 | ||
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| 本项目支持功能 | ||
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| 训练：单机CPU、单机单卡GPU、单机多卡GPU、本地模拟参数服务器训练、增量训练，配置请参考 [启动训练](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/train.md) | ||
| 预测：单机CPU、单机单卡GPU ；配置请参考[PaddleRec 离线预测](https://github.com/PaddlePaddle/PaddleRec/blob/master/doc/predict.md) | ||
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| ## 数据准备 | ||
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| 数据地址： [Census-income Data](https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census.tar.gz ) | ||
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| 生成的格式以逗号为分割点 | ||
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| ``` | ||
| 0,0,73,0,0,0,0,1700.09,0,0 | ||
| ``` | ||
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| 完整的大数据参考论文复现部分。 | ||
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| ## 运行环境 | ||
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| PaddlePaddle>=1.7.2 | ||
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| python 2.7/3.5/3.6/3.7 | ||
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| PaddleRec >=0.1 | ||
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| os : windows/linux/macos | ||
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| ## 快速开始 | ||
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| ### 单机训练 | ||
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| CPU环境 | ||
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| 在config.yaml文件中设置好设备，epochs等。 | ||
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| ``` | ||
| dataset: | ||
| - name: dataset_train | ||
| batch_size: 5 | ||
| type: QueueDataset | ||
| data_path: "{workspace}/data/train" | ||
| data_converter: "{workspace}/census_reader.py" | ||
| - name: dataset_infer | ||
| batch_size: 5 | ||
| type: QueueDataset | ||
| data_path: "{workspace}/data/train" | ||
| data_converter: "{workspace}/census_reader.py" | ||
| ``` | ||
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| ### 单机预测 | ||
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| CPU环境 | ||
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| 在config.yaml文件中设置好epochs、device等参数。 | ||
| ``` | ||
| - name: infer_runner | ||
| class: infer | ||
| init_model_path: "increment/0" | ||
| device: cpu | ||
| ``` | ||
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| ## 论文复现 | ||
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| ## 进阶使用 | ||
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| ## FAQ |
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| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. |
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| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| from __future__ import print_function | ||
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| from paddlerec.core.reader import ReaderBase | ||
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| class Reader(ReaderBase): | ||
| def init(self): | ||
| pass | ||
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| def generate_sample(self, line): | ||
| """ | ||
| Read the data line by line and process it as a dictionary | ||
| """ | ||
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| def reader(): | ||
| """ | ||
| This function needs to be implemented by the user, based on data format | ||
| """ | ||
| l = line.strip().split(',') | ||
| l = list(map(float, l)) | ||
| label_income = [] | ||
| label_marital = [] | ||
| data = l[2:] | ||
| if int(l[1]) == 0: | ||
| label_income = [1, 0] | ||
| elif int(l[1]) == 1: | ||
| label_income = [0, 1] | ||
| if int(l[0]) == 0: | ||
| label_marital = [1, 0] | ||
| elif int(l[0]) == 1: | ||
| label_marital = [0, 1] | ||
| # label_income = np.array(label_income) | ||
| # label_marital = np.array(label_marital) | ||
| feature_name = ["input", "label_income", "label_marital"] | ||
| yield zip(feature_name, [data] + [label_income] + [label_marital]) | ||
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| return reader |
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| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| workspace: "models/multitask/ple" | ||
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| dataset: | ||
| - name: dataset_train | ||
| batch_size: 5 # or big data set 32 | ||
| type: DataLoader # or QueueDataset | ||
| data_path: "{workspace}/data/train" | ||
| data_converter: "{workspace}/census_reader.py" | ||
| - name: dataset_infer | ||
| batch_size: 5 # or big data set 32 | ||
| type: DataLoader # or QueueDataset | ||
| data_path: "{workspace}/data/train" | ||
| data_converter: "{workspace}/census_reader.py" | ||
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| hyper_parameters: | ||
| feature_size: 499 | ||
| task_num: 2 | ||
| shared_num: 2 | ||
| exp_per_task: 3 | ||
| level_number: 1 | ||
| expert_size: 16 | ||
| tower_size: 8 | ||
| optimizer: | ||
| class: adam | ||
| learning_rate: 0.001 | ||
| strategy: async | ||
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| mode: [train_runner, infer_runner] | ||
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| runner: | ||
| - name: train_runner | ||
| class: train | ||
| device: cpu # or gpu | ||
| selected_gpus: "0" | ||
| epochs: 10 | ||
| save_checkpoint_interval: 1 | ||
| save_inference_interval: 4 | ||
| save_checkpoint_path: "increment_ple" | ||
| save_inference_path: "inference" | ||
| print_interval: 1 # big data set 10 | ||
| phases: [train] | ||
| - name: infer_runner | ||
| class: infer | ||
| init_model_path: "increment_ple/1" | ||
| device: cpu # or gpu | ||
| phases: [infer] | ||
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| phase: | ||
| - name: train | ||
| model: "{workspace}/model.py" | ||
| dataset_name: dataset_train | ||
| thread_num: 1 | ||
| - name: infer | ||
| model: "{workspace}/model.py" | ||
| dataset_name: dataset_infer | ||
| thread_num: 1 |
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