Reschedules the outline

model_zoo/official/recommend/wide_and_deep/README.md

@@ -1,135 +1,248 @@
-Recommendation Model
-## Overview
-This is an implementation of WideDeep as described in the [Wide & Deep Learning for Recommender System](https://arxiv.org/pdf/1606.07792.pdf) paper.
+# 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)
+    - [Preprocess Scripts Parameters](#preprocess-script-parameters)
+  - [Dataset Preparation](#dataset-preparation)
+    - [Process the Real World Data](#process-the-real-world-data)
+    - [Generate and Process the Synthetic Data](#generate-and-process-the-synthetic-data)
+  - [Training Process](#training-process)
+    - [SingleDevice](#singledevice)
+    - [Distribute Training](#distribute-training)
+    - [Parameter Server](#parameter-server)
+  - [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)
 
-WideDeep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems.
 
-## Requirements
+# [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.
 
-- Install [MindSpore](https://www.mindspore.cn/install/en).
+# [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. 
 
-- Place the raw dataset under a certain path, such as: ./recommendation_dataset/origin_data, if you use [criteo dataset](https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz), please downlowd the dataset and unzip it to ./recommendation_dataset/origin_data.
+Currently we support host-device mode with column partition and  parameter server mode.
 
-- Convert the dataset to mindrecord, command as follows:
+# [Dataset](#contents)
+
+- [1] A dataset used in  Guo H , Tang R , Ye Y , et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[J]. 2017.
+
+# [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
+```
+git clone https://gitee.com/mindspore/mindspore.git
+cd mindspore/model_zoo/official/recommend/wide_and_deep
 ```
-python src/preprocess_data.py --data_path=./recommendation_dataset --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0
+2. Download the Dataset
 
+  > Please refer to [1] to obtain the download link
+```bash
+mkdir -p data/origin_data && cd data/origin_data
+wget DATA_LINK
+tar -zxvf dac.tar.gz 
+```
+3. Use this script to preprocess the data. This may take about one hour and the generated mindrecord data is under data/mindrecord.
+```bash
+python src/preprocess_data.py  --data_path=./data/ --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0
 ```
-Arguments:
-  * `--data_path` : The path of the data file.
-  * `--dense_dim` : The number of your continues fields.
-  * `--slot_dim` : The number of your sparse fields, it can also be called category features.
-  * `--threshold` : Word frequency below this value will be regarded as OOV. It aims to reduce the vocab size.
-  * `--train_line_count`: The number of examples in your dataset.
-  * `--skip_id_convert`: 0 or 1. If set 1, the code will skip the id convert, regarding the original id as the final id.
 
-   
-## Dataset
-The common used benchmark datasets are used for model training and evaluation.
+4. 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
+```
 
 
-### Generate the synthetic Data
+# [Script Description](#contents)
+## [Script and Sample Code](#contents)
+```
+└── wide_and_deep
+    ├── eval.py
+    ├── README.md
+    ├── script
+    │   ├── cluster_32p.json
+    │   ├── common.sh
+    │   ├── deploy_cluster.sh
+    │   ├── run_auto_parallel_train_cluster.sh
+    │   ├── run_auto_parallel_train.sh
+    │   ├── run_multigpu_train.sh
+    │   ├── run_multinpu_train.sh
+    │   ├── run_parameter_server_train_cluster.sh
+    │   ├── run_parameter_server_train.sh
+    │   ├── run_standalone_train_for_gpu.sh
+    │   └── start_cluster.sh
+    ├── src
+    │   ├── callbacks.py
+    │   ├── config.py
+    │   ├── datasets.py
+    │   ├── generate_synthetic_data.py
+    │   ├── __init__.py
+    │   ├── metrics.py
+    │   ├── preprocess_data.py
+    │   ├── process_data.py
+    │   └── wide_and_deep.py
+    ├── train_and_eval_auto_parallel.py
+    ├── train_and_eval_distribute.py
+    ├── train_and_eval_parameter_server.py
+    ├── train_and_eval.py
+    └── train.py
+```
+
+## [Script Parameters](#contents)
+
+### [Training Script Parameters](#contents)
+
+The parameters is same for ``train.py``,``train_and_eval.py`` ,``train_and_eval_distribute.py`` and ``train_and_eval_auto_parallel.py`` 
+
 
-The following command will generate 40 million lines of click data, in the format of "label\tdense_feature[0]\tdense_feature[1]...\tsparse_feature[0]\tsparse_feature[1]...". 
 ```
-mkdir -p syn_data/origin_data
-python src/generate_synthetic_data.py --output_file=syn_data/origin_data/train.txt --number_examples=40000000 --dense_dim=13 --slot_dim=51 --vocabulary_size=2000000000 --random_slot_values=0
+usage: train.py [-h] [--device_target {Ascend,GPU}] [--data_path DATA_PATH]
+                [--epochs EPOCHS] [--full_batch FULL_BATCH]
+                [--batch_size BATCH_SIZE] [--eval_batch_size EVAL_BATCH_SIZE]
+                [--field_size FIELD_SIZE] [--vocab_size VOCAB_SIZE]
+                [--emb_dim EMB_DIM]
+                [--deep_layer_dim DEEP_LAYER_DIM [DEEP_LAYER_DIM ...]]
+                [--deep_layer_act DEEP_LAYER_ACT] [--keep_prob KEEP_PROB]
+                [--dropout_flag DROPOUT_FLAG] [--output_path OUTPUT_PATH]
+                [--ckpt_path CKPT_PATH] [--eval_file_name EVAL_FILE_NAME]
+                [--loss_file_name LOSS_FILE_NAME]
+                [--host_device_mix HOST_DEVICE_MIX]
+                [--dataset_type DATASET_TYPE]
+                [--parameter_server PARAMETER_SERVER]
+
+optional arguments:
+  --device_target {Ascend,GPU}        device where the code will be implemented. (Default:Ascend)
+  --data_path DATA_PATH               This should be set to the same directory given to the
+                                      data_download's data_dir argument
+  --epochs EPOCHS                     Total train epochs. (Default:15)
+  --full_batch FULL_BATCH             Enable loading the full batch. (Default:False)
+  --batch_size BATCH_SIZE             Training batch size.(Default:16000)
+  --eval_batch_size                   Eval batch size.(Default:16000)
+  --field_size                        The number of features.(Default:39)
+  --vocab_size                        The total features of dataset.(Default:200000)
+  --emb_dim                           The dense embedding dimension of sparse feature.(Default:80)
+  --deep_layer_dim                    The dimension of all deep layers.(Default:[1024,512,256,128])
+  --deep_layer_act                    The activation function of all deep layers.(Default:'relu')
+  --keep_prob                         The keep rate in dropout layer.(Default:1.0)
+  --dropout_flag                      Enable dropout.(Default:0)
+  --output_path                       Deprecated
+  --ckpt_path                         The location of the checkpoint file.(Defalut:./checkpoints/)
+  --eval_file_name                    Eval output file.(Default:eval.og)
+  --loss_file_name                    Loss output file.(Default:loss.log)
+  --host_device_mix                   Enable host device mode or not.(Default:0)
+  --dataset_type                      The data type of the training files, chosen from tfrecord/mindrecord/hd5.(Default:tfrecord)
+  --parameter_server                  Open parameter server of not.(Default:0)
+```
+### [Preprocess Scripts Parameters](#contents)
+```
+usage: generate_synthetic_data.py [-h] [--output_file OUTPUT_FILE]
+                                  [--label_dim LABEL_DIM]
+                                  [--number_examples NUMBER_EXAMPLES]
+                                  [--dense_dim DENSE_DIM]
+                                  [--slot_dim SLOT_DIM]
+                                  [--vocabulary_size VOCABULARY_SIZE]
+                                  [--random_slot_values RANDOM_SLOT_VALUES]
+optional arguments:
+  --output_file                        The output path of the generated file.(Default: ./train.txt)
+  --label_dim                          The label category. (Default:2)
+  --number_examples                    The row numbers of the generated file. (Default:4000000)
+  --dense_dim                          The number of the continue feature.(Default:13)
+  --slot_dim                           The number of the category features.(Default:26)
+  --vocabulary_size                    The vocabulary size of the total dataset.(Default:400000000)
+  --random_slot_values                 0 or 1. If 1, the id is generated by the random. If 0, the id is set by the row_index mod 										part_size, where part_size is the vocab size for each slot
 ```
-Arguments:
-  * `--output_file`: The output path of the generated file
-  * `--label_dim` : The label category
-  * `--number_examples`: The row numbers of the generated file
-  * `--dense_dim` : The number of the continue feature.
-  * `--slot_dim`: The number of the category features
-  * `--vocabulary_size`: The vocabulary size of the total dataset
-  * `--random_slot_values`: 0 or 1. If 1, the id is generated by the random. If 0, the id is set by the row_index mod part_size, where
-                        part_size is the vocab size for each slot
 
-Preprocess the generated data
 ```
-python src/preprocess_data.py --data_path=./syn_data/ --data_type=synthetic --dense_dim=13 --slot_dim=51 --threshold=0 --train_line_count=40000000 --skip_id_convert=1
+usage: preprocess_data.py [-h] 
+                          [--data_path DATA_PATH] [--dense_dim DENSE_DIM]
+                          [--slot_dim SLOT_DIM] [--threshold THRESHOLD]
+                          [--train_line_count TRAIN_LINE_COUNT]
+                          [--skip_id_convert {0,1}]
+
+  --data_path                         The path of the data file.
+  --dense_dim                         The number of your continues fields.(default: 13)
+  --slot_dim                          The number of your sparse fields, it can also be called category features.(default: 26)
+  --threshold                         Word frequency below this value will be regarded as OOV. It aims to reduce the vocab size.									  (default: 100)
+  --train_line_count                  The number of examples in your dataset.
+  --skip_id_convert                   0 or 1. If set 1, the code will skip the id convert, regarding the original id as the final id.(default: 0)
 ```
 
+## [Dataset Preparation](#contents)
+
+### [Process the Real World Data](#content)
+
+
+
 
+1. Download the Dataset and place the raw dataset under a certain path, such as: ./data/origin_data
+```bash
+mkdir -p data/origin_data && cd data/origin_data
+wget DATA_LINK
+tar -zxvf dac.tar.gz 
+```
+> Please refer to [1] to obtain the download link
+
+2. Use this script to preprocess the data
+```bash
+python src/preprocess_data.py  --data_path=./data/ --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0
+```
 
-## Running Code
+### [Generate and Process the Synthetic Data](#content)
+1. The following command will generate 40 million lines of click data, in the format of 
+> "label\tdense_feature[0]\tdense_feature[1]...\tsparse_feature[0]\tsparse_feature[1]...". 
+```
+mkdir -p syn_data/origin_data
+python src/generate_synthetic_data.py --output_file=syn_data/origin_data/train.txt --number_examples=40000000 --dense_dim=13 --slot_dim=51 --vocabulary_size=2000000000 --random_slot_values=0
+```
 
-### Code Structure
-The entire code structure is as following:
+2. Preprocess the generated data
 ```
-|--- wide_and_deep/
-    train_and_eval.py                  "Entrance of Wide&Deep model training and evaluation"
-    eval.py                            "Entrance of Wide&Deep model evaluation"
-    train.py                           "Entrance of Wide&Deep model training"
-    train_and_eval_multinpu.py         "Entrance of Wide&Deep model data parallel training and evaluation"
-    train_and_eval_auto_parallel.py
-    train_and_eval_parameter_server.py "Entrance of Wide&Deep model parameter server training and evaluation"
-    |--- src/                          "Entrance of training and evaluation"
-        config.py                      "Parameters configuration"
-        dataset.py                     "Dataset loader class"
-        process_data.py                "Process dataset"
-        preprocess_data.py             "Pre_process dataset"
-        wide_and_deep.py               "Model structure"
-        callbacks.py                   "Callback class for training and evaluation"
-        generate_synthetic_data.py     "Generate the synthetic data for benchmark"
-        metrics.py                     "Metric class"
-    |--- script/                       "Run shell dir"
-        run_multinpu_train.sh          "Run data parallel"
-        run_auto_parallel_train.sh     "Run auto parallel"
-        run_parameter_server_train.sh  "Run parameter server"
+python src/preprocess_data.py --data_path=./syn_data/  --dense_dim=13 --slot_dim=51 --threshold=0 --train_line_count=40000000 --skip_id_convert=1
 ```
 
+## [Training Process](#contents)
+
+### [SingleDevice](#contents)
 
-### Train and evaluate model
 To train and evaluate the model, command as follows:
 ```
 python train_and_eval.py
 ```
-Arguments:
-  * `--device_target`: Device where the code will be implemented (Default: Ascend).
-  * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
-  * `--epochs`: Total train epochs.
-  * `--batch_size`: Training batch size.
-  * `--eval_batch_size`: Eval batch size.
-  * `--field_size`: The number of features.
-  * `--vocab_size`： The total features of dataset.
-  * `--emb_dim`： The dense embedding dimension of sparse feature.
-  * `--deep_layers_dim`： The dimension of all deep layers.
-  * `--deep_layers_act`： The activation of all deep layers.
-  * `--dropout_flag`： Whether do dropout.
-  * `--keep_prob`： The rate to keep in dropout layer.
-  * `--ckpt_path`：The location of the checkpoint file.
-  * `--eval_file_name` : Eval output file.
-  * `--loss_file_name` :  Loss output file.
-  * `--dataset_type` :  tfrecord/mindrecord/hd5.
-
-To train the model in one device, command as follows:
-```
-python train.py
-```
-Arguments:
-  * `--device_target`: Device where the code will be implemented (Default: Ascend).
-  * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
-  * `--epochs`: Total train epochs.
-  * `--batch_size`: Training batch size.
-  * `--eval_batch_size`: Eval batch size.
-  * `--field_size`: The number of features.
-  * `--vocab_size`： The total features of dataset.
-  * `--emb_dim`： The dense embedding dimension of sparse feature.
-  * `--deep_layers_dim`： The dimension of all deep layers.
-  * `--deep_layers_act`： The activation of all deep layers.
-  * `--dropout_flag`： Whether do dropout.
-  * `--keep_prob`： The rate to keep in dropout layer.
-  * `--ckpt_path`：The location of the checkpoint file.
-  * `--eval_file_name` : Eval output file.
-  * `--loss_file_name` :  Loss output file.
-  * `--dataset_type` :  tfrecord/mindrecord/hd5.
-
-To train the model in distributed, command as follows:
+
+
+### [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 
 ```
+To train the model in model parallel training, commands as follows:
 ```
 # configure environment path before training
 bash run_auto_parallel_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE 
@@ -147,7 +260,7 @@ bash deploy_cluster.sh CLUSTER_CONFIG_PATH EXECUTE_PATH
 bash start_cluster.sh CLUSTER_CONFIG_PATH EPOCH_SIZE VOCAB_SIZE EMB_DIM
                       DATASET ENV_SH RANK_TABLE_FILE MODE
 ```
-
+### [Parameter Server](#contents)
 To train and evaluate the model in parameter server mode, command as follows:'''
 ```
 # SERVER_NUM is the number of parameter servers for this task.
@@ -157,24 +270,56 @@ To train and evaluate the model in parameter server mode, command as follows:'''
 bash run_parameter_server_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE SERVER_NUM SCHED_HOST SCHED_PORT
 ```
 
+
+
+## [Evaluation Process](#contents)
 To evaluate the model, command as follows:
 ```
 python eval.py
 ```
-Arguments:
-  * `--device_target`: Device where the code will be implemented (Default: Ascend).
-  * `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
-  * `--epochs`: Total train epochs.
-  * `--batch_size`: Training batch size.
-  * `--eval_batch_size`: Eval batch size.
-  * `--field_size`: The number of features.
-  * `--vocab_size`： The total features of dataset.
-  * `--emb_dim`： The dense embedding dimension of sparse feature.
-  * `--deep_layers_dim`： The dimension of all deep layers.
-  * `--deep_layers_act`： The activation of all deep layers.
-  * `--keep_prob`： The rate to keep in dropout layer.
-  * `--ckpt_path`：The location of the checkpoint file.
-  * `--eval_file_name` : Eval output file.
-  * `--loss_file_name` :  Loss output file.
-
-There are other arguments about models and training process. Use the `--help` or `-h` flag to get a full list of possible arguments with detailed descriptions.
+
+# [Model Description](#contents)
+
+## [Performance](#contents)
+
+### Training Performance 
+
+| Parameters               | Single <br />Ascend             | Single<br />GPU                 | Data-Parallel-8P                | Host-Device-mode-8P             |
+| ------------------------ | ------------------------------- | ------------------------------- | ------------------------------- | ------------------------------- |
+| Resource                 | Ascend 910                      | Tesla V100-PCIE 32G             | Ascend 910                      | Ascend 910                      |
+| Uploaded Date            | 08/21/2020 (month/day/year)     | 08/21/2020 (month/day/year)     | 08/21/2020 (month/day/year)     | 08/21/2020 (month/day/year)     |
+| MindSpore Version        | 0.6.0-beta                      | master                          | 0.6.0-beta                      | 0.6.0-beta                      |
+| Dataset                  | [1]                             | [1]                             | [1]                             | [1]                             |
+| Training Parameters      | Epoch=15,<br />batch_size=16000 | Epoch=15,<br />batch_size=16000 | Epoch=15,<br />batch_size=16000 | Epoch=15,<br />batch_size=16000 |
+| Optimizer                | FTRL,Adam                       | FTRL,Adam                       | FTRL,Adam                       | FTRL,Adam                       |
+| Loss Function            | SigmoidCrossEntroy              | SigmoidCrossEntroy              | SigmoidCrossEntroy              | SigmoidCrossEntroy              |
+| AUC Score                | 0.80937                         | 0.80971                         | 0.80862                         | 0.80834                         |
+| Speed                    | 20.906 ms/step                  | 24.465 ms/step                  | 27.388 ms/step                  | 236.506 ms/step                 |
+| Loss                     | wide:0.433,deep:0.444           | wide:0.444, deep:0.456          | wide:0.437, deep: 0.448         | wide:0.444, deep:0.444          |
+| Parms(M)                 | 75.84                           | 75.84                           | 75.84                           | 75.84                           |
+| Checkpoint for inference | 233MB(.ckpt file)               | 230MB(.ckpt)                    | 233MB(.ckpt file)               | 233MB(.ckpt file)               |
+
+
+
+All executable scripts can be found in [here](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/recommend/wide_and_deep/script)
+
+Note: The result of GPU is tested under the master version. The parameter server mode of the Wide&Deep model is still under development.
+
+### Evaluation Performance
+
+| Parameters        | Wide&Deep                   |
+| ----------------- | --------------------------- |
+| Resource          | Ascend 910                  |
+| Uploaded Date     | 08/21/2020 (month/day/year) |
+| MindSpore Version | 0.6.0-beta                  |
+| Dataset           | [1]                         |
+| Batch Size        | 16000                       |
+| Outputs           | AUC                         |
+| Accuracy          | AUC=0.809                   |
+
+# [Description of Random Situation](#contents)
+
+
+# [ModelZoo Homepage](#contents)
+
+Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

model_zoo/official/recommend/wide_and_deep/src/config.py

@@ -22,25 +22,28 @@ def argparse_init():
     parser = argparse.ArgumentParser(description='WideDeep')
     parser.add_argument("--device_target", type=str, default="Ascend", choices=["Ascend", "GPU"],
                         help="device where the code will be implemented. (Default: Ascend)")
-    parser.add_argument("--data_path", type=str, default="./test_raw_data/")
-    parser.add_argument("--epochs", type=int, default=15)
-    parser.add_argument("--full_batch", type=bool, default=False)
-    parser.add_argument("--batch_size", type=int, default=16000)
-    parser.add_argument("--eval_batch_size", type=int, default=16000)
-    parser.add_argument("--field_size", type=int, default=39)
-    parser.add_argument("--vocab_size", type=int, default=200000)
-    parser.add_argument("--emb_dim", type=int, default=80)
-    parser.add_argument("--deep_layer_dim", type=int, nargs='+', default=[1024, 512, 256, 128])
-    parser.add_argument("--deep_layer_act", type=str, default='relu')
-    parser.add_argument("--keep_prob", type=float, default=1.0)
-    parser.add_argument("--dropout_flag", type=int, default=0)
+    parser.add_argument("--data_path", type=str, default="./test_raw_data/",
+                        help="This should be set to the same directory given to the data_download's data_dir argument")
+    parser.add_argument("--epochs", type=int, default=15, help="Total train epochs")
+    parser.add_argument("--full_batch", type=bool, default=False, help="Enable loading the full batch ")
+    parser.add_argument("--batch_size", type=int, default=16000, help="Training batch size.")
+    parser.add_argument("--eval_batch_size", type=int, default=16000, help="Eval batch size.")
+    parser.add_argument("--field_size", type=int, default=39, help="The number of features.")
+    parser.add_argument("--vocab_size", type=int, default=200000, help="The total features of dataset.")
+    parser.add_argument("--emb_dim", type=int, default=80, help="The dense embedding dimension of sparse feature.")
+    parser.add_argument("--deep_layer_dim", type=int, nargs='+', default=[1024, 512, 256, 128],
+                        help="The dimension of all deep layers.")
+    parser.add_argument("--deep_layer_act", type=str, default='relu',
+                        help="The activation function of all deep layers.")
+    parser.add_argument("--keep_prob", type=float, default=1.0, help="The keep rate in dropout layer.")
+    parser.add_argument("--dropout_flag", type=int, default=0, help="Enable dropout")
     parser.add_argument("--output_path", type=str, default="./output/")
-    parser.add_argument("--ckpt_path", type=str, default="./checkpoints/")
-    parser.add_argument("--eval_file_name", type=str, default="eval.log")
-    parser.add_argument("--loss_file_name", type=str, default="loss.log")
-    parser.add_argument("--host_device_mix", type=int, default=0)
-    parser.add_argument("--dataset_type", type=str, default="tfrecord")
-    parser.add_argument("--parameter_server", type=int, default=0)
+    parser.add_argument("--ckpt_path", type=str, default="./checkpoints/", help="The location of the checkpoint file.")
+    parser.add_argument("--eval_file_name", type=str, default="eval.log", help="Eval output file.")
+    parser.add_argument("--loss_file_name", type=str, default="loss.log", help="Loss output file.")
+    parser.add_argument("--host_device_mix", type=int, default=0, help="Enable host device mode or not")
+    parser.add_argument("--dataset_type", type=str, default="tfrecord", help="tfrecord/mindrecord/hd5")
+    parser.add_argument("--parameter_server", type=int, default=0, help="Open parameter server of not")
     return parser
 
 
@@ -48,6 +51,7 @@ class WideDeepConfig():
     """
     WideDeepConfig
     """
+
     def __init__(self):
         self.device_target = "Ascend"
         self.data_path = "./test_raw_data/"