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!4865 Rewrite the REAMD.md in Wide&Deep model 

Merge pull request !4865 from huangxinjing/wide-deep-readme
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Recommendation Model # Contents
## Overview - [Wide&Deep Description](#widedeep-description)
This is an implementation of WideDeep as described in the [Wide & Deep Learning for Recommender System](https://arxiv.org/pdf/1606.07792.pdf) paper. - [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: usage: train.py [-h] [--device_target {Ascend,GPU}] [--data_path DATA_PATH]
* `--data_path` : The path of the data file. [--epochs EPOCHS] [--full_batch FULL_BATCH]
* `--dense_dim` : The number of your continues fields. [--batch_size BATCH_SIZE] [--eval_batch_size EVAL_BATCH_SIZE]
* `--slot_dim` : The number of your sparse fields, it can also be called category features. [--field_size FIELD_SIZE] [--vocab_size VOCAB_SIZE]
* `--threshold` : Word frequency below this value will be regarded as OOV. It aims to reduce the vocab size. [--emb_dim EMB_DIM]
* `--train_line_count`: The number of examples in your dataset. [--deep_layer_dim DEEP_LAYER_DIM [DEEP_LAYER_DIM ...]]
* `--skip_id_convert`: 0 or 1. If set 1, the code will skip the id convert, regarding the original id as the final id. [--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:
## Dataset --device_target {Ascend,GPU} device where the code will be implemented. (Default:Ascend)
The common used benchmark datasets are used for model training and evaluation. --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 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 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: To train and evaluate the model, command as follows:
``` ```
python train_and_eval.py 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 # configure environment path before training
bash run_multinpu_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE 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 # configure environment path before training
bash run_auto_parallel_train.sh RANK_SIZE EPOCHS DATASET RANK_TABLE_FILE 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 bash start_cluster.sh CLUSTER_CONFIG_PATH EPOCH_SIZE VOCAB_SIZE EMB_DIM
DATASET ENV_SH RANK_TABLE_FILE MODE DATASET ENV_SH RANK_TABLE_FILE MODE
``` ```
### [Parameter Server](#contents)
To train and evaluate the model in parameter server mode, command as follows:''' To train and evaluate the model in parameter server mode, command as follows:'''
``` ```
# SERVER_NUM is the number of parameter servers for this task. # 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 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: To evaluate the model, command as follows:
``` ```
python eval.py 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).

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