Add README for multi-table of Wide&Deep

model_zoo/official/recommend/wide_and_deep_multitable/README.md

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