diff --git a/model_zoo/official/recommend/wide_and_deep/README.md b/model_zoo/official/recommend/wide_and_deep/README.md
index 26d01ba351..1cc86e29bc 100644
--- a/model_zoo/official/recommend/wide_and_deep/README.md
+++ b/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
```
-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
+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
+```
+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
+ ├── 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``
+
```
-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.
+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]
-
-## Dataset
-The common used benchmark datasets are used for model training and evaluation.
+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
+```
+
+```
+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)
-### Generate the synthetic Data
-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]...".
+
+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]...".
```
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
```
-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
+2. 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
+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)
-## Running Code
-
-### Code Structure
-The entire code structure is as following:
-```
-|--- 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"
-```
-
-
-### 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
Ascend | Single
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,
batch_size=16000 | Epoch=15,
batch_size=16000 | Epoch=15,
batch_size=16000 | Epoch=15,
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).
\ No newline at end of file
diff --git a/model_zoo/official/recommend/wide_and_deep/src/config.py b/model_zoo/official/recommend/wide_and_deep/src/config.py
index 54d83e97b9..a7d1035a10 100644
--- a/model_zoo/official/recommend/wide_and_deep/src/config.py
+++ b/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/"