modify readme

model_zoo/wide_and_deep/README.md

@@ -1,20 +1,14 @@
-recommendation Model
+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.
 
 WideDeep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems.
 
 ## Dataset
-The [Criteo datasets](http://labs.criteo.com/2014/02/download-kaggle-display-advertising-challenge-dataset/) are used for model training and evaluation.
+The Criteo datasets are used for model training and evaluation.
 
 ## Running Code
 
-### Download and preprocess dataset
-To download the dataset, please install Pandas package first. Then issue the following command:
-```
-bash download.sh
-```
-
 ### Code Structure
 The entire code structure is as following:
 ```
@@ -26,13 +20,15 @@ The entire code structure is as following:
     |--- 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"
         WideDeep.py              "Model structure"
         callbacks.py             "Callback class for training and evaluation"
         metrics.py               "Metric class"
 ```
 
 ### Train and evaluate model
-To train and evaluate the model, issue the following command:
+To train and evaluate the model, command as follows:
 ```
 python train_and_test.py
 ```
@@ -51,7 +47,7 @@ Arguments:
   * `--eval_file_name` : Eval output file.
   * `--loss_file_name` :  Loss output file.
 
-To train the model, issue the following command:
+To train the model in one device, command as follows:
 ```
 python train.py
 ```
@@ -70,7 +66,13 @@ Arguments:
   * `--eval_file_name` : Eval output file.
   * `--loss_file_name` :  Loss output file.
 
-To evaluate the model, issue the following command:
+To train the model in distributed, command as follows:
+```
+# configure environment path, RANK_TABLE_FILE, RANK_SIZE, MINDSPORE_HCCL_CONFIG_PATH before training
+bash run_multinpu_train.sh
+```
+
+To evaluate the model, command as follows:
 ```
 python test.py
 ```
@@ -90,4 +92,3 @@ Arguments:
   * `--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.
-