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!5506 Add the REAMD.md in Wide&Deep model
Merge pull request !5506 from huangxinjing/wide-deep-multi-readme
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# Contents
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- [Wide&Deep Description](#widedeep-description)
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- [Model Architecture](#model-architecture)
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- [Dataset](#dataset)
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- [Environment Requirements](#environment-requirements)
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- [Quick Start](#quick-start)
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Script Parameters](#script-parameters)
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- [Training Script Parameters](#training-script-parameters)
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- [Training Process](#training-process)
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- [SingleDevice](#singledevice)
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- [Distribute Training](#distribute-training)
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- [Evaluation Process](#evaluation-process)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Training Performance](#training-performance)
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- [Evaluation Performance](#evaluation-performance)
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- [Description of Random Situation](#description-of-random-situation)
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- [ModelZoo Homepage](#modelzoo-homepage)
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# [Wide&Deep Description](#contents)
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Wide&Deep model is a classical model in Recommendation and Click Prediction area. This is an implementation of Wide&Deep as described in the [Wide & Deep Learning for Recommender System](https://arxiv.org/pdf/1606.07792.pdf) paper.
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# [Model Architecture](#contents)
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Wide&Deep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems.
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# [Dataset](#contents)
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- [1] A dataset used in Click Prediction
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# [Environment Requirements](#contents)
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- Hardware（Ascend or GPU）
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- Prepare hardware environment with Ascend 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.
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- Framework
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- [MindSpore](https://gitee.com/mindspore/mindspore)
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- For more information, please check the resources below：
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- [MindSpore tutorials](https://www.mindspore.cn/tutorial/en/master/index.html)
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- [MindSpore API](https://www.mindspore.cn/api/en/master/index.html)
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# [Quick Start](#contents)
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1. Clone the Code
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```bash
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git clone https://gitee.com/mindspore/mindspore.git
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cd mindspore/model_zoo/official/recommend/wide_and_deep_multitable
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```
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2. Download the Dataset
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> Please refer to [1] to obtain the download link and data preprocess
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3. Start Training
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Once the dataset is ready, the model can be trained and evaluated on the single device(Ascend) by the command as follows:
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```bash
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python train_and_eval.py --data_path=./data/mindrecord --data_type=mindrecord
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```
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To evaluate the model, command as follows:
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```bash
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python eval.py --data_path=./data/mindrecord --data_type=mindrecord
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```
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# [Script Description](#contents)
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## [Script and Sample Code](#contents)
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```
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└── wide_and_deep_multitable
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├── eval.py
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├── README.md
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├── requirements.txt
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├── script
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│ └── run_multinpu_train.sh
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├── src
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│ ├── callbacks.py
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│ ├── config.py
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│ ├── datasets.py
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│ ├── __init__.py
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│ ├── metrics.py
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│ └── wide_and_deep.py
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├── train_and_eval_distribute.py
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└── train_and_eval.py
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```
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## [Script Parameters](#contents)
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### [Training Script Parameters](#contents)
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The parameters is same for ``train_and_eval.py`` and ``train_and_eval_distribute.py``
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```
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usage: train_and_eval.py [-h] [--data_path DATA_PATH] [--epochs EPOCHS]
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[--batch_size BATCH_SIZE]
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[--eval_batch_size EVAL_BATCH_SIZE]
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[--deep_layers_dim DEEP_LAYERS_DIM [DEEP_LAYERS_DIM ...]]
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[--deep_layers_act DEEP_LAYERS_ACT]
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[--keep_prob KEEP_PROB] [--adam_lr ADAM_LR]
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[--ftrl_lr FTRL_LR] [--l2_coef L2_COEF]
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[--is_tf_dataset IS_TF_DATASET]
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[--dropout_flag DROPOUT_FLAG]
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[--output_path OUTPUT_PATH] [--ckpt_path CKPT_PATH]
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[--eval_file_name EVAL_FILE_NAME]
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[--loss_file_name LOSS_FILE_NAME]
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WideDeep
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optional arguments:
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--data_path DATA_PATH This should be set to the same directory given to the
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data_download's data_dir argument
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--epochs Total train epochs. (Default:200)
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--batch_size Training batch size.(Default:131072)
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--eval_batch_size Eval batch size.(Default:131072)
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--deep_layers_dim The dimension of all deep layers.(Default:[1024,1024,1024,1024])
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--deep_layers_act The activation function of all deep layers.(Default:'relu')
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--keep_prob The keep rate in dropout layer.(Default:1.0)
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--adam_lr The learning rate of the deep part. (Default:0.003)
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--ftrl_lr The learning rate of the wide part.(Default:0.1)
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--l2_coef The coefficient of the L2 pernalty. (Default:0.0)
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--is_tf_dataset IS_TF_DATASET Whether the input is tfrecords. (Default:True)
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--dropout_flag Enable dropout.(Default:0)
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--output_path OUTPUT_PATH Deprecated
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--ckpt_path CKPT_PATH The location of the checkpoint file.(Defalut:./checkpoints/)
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--eval_file_name EVAL_FILE_NAME Eval output file.(Default:eval.og)
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--loss_file_name LOSS_FILE_NAME Loss output file.(Default:loss.log)
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```
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## [Training Process](#contents)
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### [SingleDevice](#contents)
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To train and evaluate the model, command as follows:
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```
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python train_and_eval.py
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```
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### [Distribute Training](#contents)
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To train the model in data distributed training, command as follows:
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```
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# configure environment path before training
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bash run_multinpu_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE
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```
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## [Evaluation Process](#contents)
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To evaluate the model, command as follows:
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```
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python eval.py
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```
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# [Model Description](#contents)
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## [Performance](#contents)
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### Training Performance
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| Parameters | Single <br />Ascend | Data-Parallel-8P |
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| ------------------------ | ------------------------------- | ------------------------------- |
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| Resource | Ascend 910 | Ascend 910 |
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| Uploaded Date | 08/21/2020 (month/day/year) | 08/21/2020 (month/day/year) |
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| MindSpore Version | 0.7.0-beta | 0.7.0-beta |
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| Dataset | [1] | [1] |
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| Training Parameters | Epoch=3,<br />batch_size=131072 | Epoch=8,<br />batch_size=131072 |
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| Optimizer | FTRL,Adam | FTRL,Adam |
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| Loss Function | SigmoidCrossEntroy | SigmoidCrossEntroy |
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| AUC Score | 0.7473 | 0.7464 |
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| MAP Score | 0.6608 | 0.6590 |
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| Speed | 284 ms/step | 331 ms/step |
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| Loss | wide:0.415,deep:0.415 | wide:0.419, deep: 0.419 |
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| Parms(M) | 349 | 349 |
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| Checkpoint for inference | 1.1GB(.ckpt file) | 1.1GB(.ckpt file) |
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All executable scripts can be found in [here](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/recommend/wide_and_deep_multitable/script)
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### Evaluation Performance
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| Parameters | Wide&Deep |
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| ----------------- | --------------------------- |
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| Resource | Ascend 910 |
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| Uploaded Date | 08/21/2020 (month/day/year) |
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| MindSpore Version | 0.7.0-beta |
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| Dataset | [1] |
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| Batch Size | 131072 |
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| Outputs | AUC，MAP |
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| Accuracy | AUC=0.7473，MAP=0.7464 |
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# [Description of Random Situation](#contents)
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There are three random situations:
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- Shuffle of the dataset.
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- Initialization of some model weights.
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- Dropout operations.
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# [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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