!9786 fix wide&deep readme

From: @yao_yf
Reviewed-by: @zhunaipan,@stsuteng
Signed-off-by: @stsuteng

mindspore/ops/operations/array_ops.py

@@ -1888,7 +1888,7 @@ class UnsortedSegmentSum(PrimitiveWithInfer):
             output_min_shape = list(num_segments['min_value'])
         else:
             if isinstance(num_segments_type, type(mstype.tensor)):
-                raise ValueError("In dynamic shape scene, the num_segments should contains max_value and min_value")
+                raise ValueError("Num_segments only support int type when it is not a dynamic value")
             output_max_shape = [num_segments_v]
             output_min_shape = [num_segments_v]
         if 'max_shape' in x and 'min_shape' in x:

model_zoo/official/recommend/wide_and_deep/README.md

@@ -1,4 +1,6 @@
 # Contents
+
+- [Contents](#contents)
 - [Wide&Deep Description](#widedeep-description)
 - [Model Architecture](#model-architecture)
 - [Dataset](#dataset)
@@ -24,11 +26,12 @@
 - [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.
 
 Currently we support host-device mode with column partition and  parameter server mode.
@@ -38,6 +41,7 @@ Currently we support host-device mode with column partition and  parameter serve
 - [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
@@ -46,42 +50,50 @@ Currently we support host-device mode with column partition and  parameter serve
     - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
     - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/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
 ```
+
 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
 ```
 
 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
+python train_and_eval.py --data_path=./data/mindrecord --dataset_type=mindrecord
-```
-To evaluate the model, command as follows:
-```bash
-python eval.py  --data_path=./data/mindrecord --data_type=mindrecord
 ```
 
+To evaluate the model, command as follows:
+
+```bash
+python eval.py  --data_path=./data/mindrecord --dataset_type=mindrecord
+```
 
 # [Script Description](#contents)
+
 ## [Script and Sample Code](#contents)
-```
+
+```bash
 └── wide_and_deep
     ├── eval.py
     ├── README.md
@@ -121,8 +133,7 @@ python eval.py  --data_path=./data/mindrecord --data_type=mindrecord
 
 The parameters is same for ``train.py``,``train_and_eval.py`` ,``train_and_eval_distribute.py`` and ``train_and_eval_auto_parallel.py``
 
-
+```python
-```
 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]
@@ -164,8 +175,10 @@ optional arguments:
   --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 Script Parameters](#contents)
-```
+
+```python
 usage: generate_synthetic_data.py [-h] [--output_file OUTPUT_FILE]
                                   [--label_dim LABEL_DIM]
                                   [--number_examples NUMBER_EXAMPLES]
@@ -183,7 +196,7 @@ optional arguments:
   --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
 ```
 
-```
+```python
 usage: preprocess_data.py [-h]
                           [--data_path DATA_PATH] [--dense_dim DENSE_DIM]
                           [--slot_dim SLOT_DIM] [--threshold THRESHOLD]
@@ -203,28 +216,35 @@ usage: preprocess_data.py [-h]
 ### [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
 ```
 
 ### [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]...".
-```
+
+```bash
 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
 ```
 
 2. Preprocess the generated data
-```
+
+```python
 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
 ```
 
@@ -233,25 +253,30 @@ python src/preprocess_data.py --data_path=./syn_data/  --dense_dim=13 --slot_dim
 ### [SingleDevice](#contents)
 
 To train and evaluate the model, command as follows:
-```
+
+```python
 python train_and_eval.py
 ```
 
-
 ### [Distribute Training](#contents)
+
 To train the model in data distributed training, command as follows:
-```
+
+```bash
 # 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:
-```
+
+```bash
 # configure environment path before training
 bash run_auto_parallel_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE
 ```
 
 To train the model in clusters, command as follows:'''
-```
+
+```bash
 # deploy wide&deep script in clusters
 # CLUSTER_CONFIG is a json file, the sample is in script/.
 # EXECUTE_PATH is the scripts path after the deploy.
@@ -262,9 +287,12 @@ 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:'''
-```
+
+```bash
 # SERVER_NUM is the number of parameter servers for this task.
 # SCHED_HOST is the IP address of scheduler.
 # SCHED_PORT is the port of scheduler.
@@ -272,11 +300,11 @@ 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
 python eval.py
 ```
 
@@ -301,8 +329,6 @@ python eval.py
 | 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.
@@ -322,11 +348,11 @@ Note: The result of GPU is tested under the master version. The parameter server
 # [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).