mindspore/model_zoo/official/recommend/wide_and_deep_multitable

Contents

• Wide&Deep Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
  • Script and Sample Code
  • Script Parameters
    • Training Script Parameters
  • Training Process
    • SingleDevice
    • Distribute Training
  • Evaluation Process
• Model Description
  • Performance
    • Training Performance
    • Evaluation Performance
• Description of Random Situation
• ModelZoo Homepage

Wide&Deep Description

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 paper.

Model Architecture

Wide&Deep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems.

Dataset

• [1] A dataset used in Click Prediction

Environment Requirements

• Hardware（Ascend or GPU）
  • Prepare hardware environment with Ascend processor. If you want to try Ascend , please send the application form to ascend@huawei.com. Once approved, you can get the resources.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API

Quick Start

1. Clone the Code

    git clone https://gitee.com/mindspore/mindspore.git
    cd mindspore/model_zoo/official/recommend/wide_and_deep_multitable

2. Download the Dataset

   Please refer to [1] to obtain the download link and data preprocess

3. Start Training Once the dataset is ready, the model can be trained and evaluated on the single device(Ascend) by the command as follows:

python train_and_eval.py --data_path=./data/mindrecord --data_type=mindrecord

To evaluate the model, command as follows:

python eval.py  --data_path=./data/mindrecord --data_type=mindrecord

Script Description

Script and Sample Code

└── wide_and_deep_multitable
    ├── eval.py
    ├── README.md
    ├── requirements.txt
    ├── script
    │   └── run_multinpu_train.sh
    ├── src
    │   ├── callbacks.py
    │   ├── config.py
    │   ├── datasets.py
    │   ├── __init__.py
    │   ├── metrics.py
    │   └── wide_and_deep.py
    ├── train_and_eval_distribute.py
    └── train_and_eval.py

Script Parameters

Training Script Parameters

The parameters is same for train_and_eval.py and train_and_eval_distribute.py

usage: train_and_eval.py [-h] [--data_path DATA_PATH] [--epochs EPOCHS]
                         [--batch_size BATCH_SIZE]
                         [--eval_batch_size EVAL_BATCH_SIZE]
                         [--deep_layers_dim DEEP_LAYERS_DIM [DEEP_LAYERS_DIM ...]]
                         [--deep_layers_act DEEP_LAYERS_ACT]
                         [--keep_prob KEEP_PROB] [--adam_lr ADAM_LR]
                         [--ftrl_lr FTRL_LR] [--l2_coef L2_COEF]
                         [--is_tf_dataset IS_TF_DATASET]
                         [--dropout_flag DROPOUT_FLAG]
                         [--output_path OUTPUT_PATH] [--ckpt_path CKPT_PATH]
                         [--eval_file_name EVAL_FILE_NAME]
                         [--loss_file_name LOSS_FILE_NAME]

WideDeep

optional arguments:
  --data_path DATA_PATH               This should be set to the same directory given to the
                                      data_download's data_dir argument
  --epochs                            Total train epochs. (Default:200)
  --batch_size                        Training batch size.(Default:131072)
  --eval_batch_size                   Eval batch size.(Default:131072)
  --deep_layers_dim                   The dimension of all deep layers.(Default:[1024,1024,1024,1024])
  --deep_layers_act                   The activation function of all deep layers.(Default:'relu')
  --keep_prob                         The keep rate in dropout layer.(Default:1.0)
  --adam_lr                           The learning rate of the deep part. (Default:0.003) 
  --ftrl_lr                           The learning rate of the wide part.(Default:0.1) 
  --l2_coef                           The coefficient of the L2 pernalty. (Default:0.0) 
  --is_tf_dataset IS_TF_DATASET       Whether the input is tfrecords. (Default:True)
  --dropout_flag                      Enable dropout.(Default:0)   
  --output_path OUTPUT_PATH           Deprecated
  --ckpt_path CKPT_PATH               The location of the checkpoint file.(Defalut:./checkpoints/)
  --eval_file_name EVAL_FILE_NAME     Eval output file.(Default:eval.og)
  --loss_file_name LOSS_FILE_NAME     Loss output file.(Default:loss.log)

Training Process

SingleDevice

To train and evaluate the model, command as follows:

python train_and_eval.py

Distribute Training

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 

Evaluation Process

To evaluate the model, command as follows:

python eval.py

Model Description

Performance

Training Performance

Parameters	Single Ascend	Data-Parallel-8P
Resource	Ascend 910	Ascend 910
Uploaded Date	08/21/2020 (month/day/year)	08/21/2020 (month/day/year)
MindSpore Version	1.0	1.0
Dataset	[1]	[1]
Training Parameters	Epoch=3, batch_size=131072	Epoch=8, batch_size=131072
Optimizer	FTRL,Adam	FTRL,Adam
Loss Function	SigmoidCrossEntroy	SigmoidCrossEntroy
AUC Score	0.7473	0.7464
MAP Score	0.6608	0.6590
Speed	284 ms/step	331 ms/step
Loss	wide:0.415,deep:0.415	wide:0.419, deep: 0.419
Parms(M)	349	349
Checkpoint for inference	1.1GB(.ckpt file)	1.1GB(.ckpt file)

All executable scripts can be found in here

Evaluation Performance

Parameters	Wide&Deep
Resource	Ascend 910
Uploaded Date	10/27/2020 (month/day/year)
MindSpore Version	1.0
Dataset	[1]
Batch Size	131072
Outputs	AUC，MAP
Accuracy	AUC=0.7473，MAP=0.7464

Description of Random Situation

There are three random situations:

• Shuffle of the dataset.
• Initialization of some model weights.
• Dropout operations.

ModelZoo Homepage

Please check the official homepage.