diff --git a/model_zoo/official/recommend/wide_and_deep/README.md b/model_zoo/official/recommend/wide_and_deep/README.md index 1cc86e29bceeeb840463578795b261e913dc0efd..4644603279538708a1f92e1f9b64c13d458af02f 100644 --- a/model_zoo/official/recommend/wide_and_deep/README.md +++ b/model_zoo/official/recommend/wide_and_deep/README.md @@ -8,7 +8,7 @@ - [Script and Sample Code](#script-and-sample-code) - [Script Parameters](#script-parameters) - [Training Script Parameters](#training-script-parameters) - - [Preprocess Scripts Parameters](#preprocess-script-parameters) + - [Preprocess Script 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) @@ -159,7 +159,7 @@ 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 Scripts Parameters](#contents) +### [Preprocess Script Parameters](#contents) ``` usage: generate_synthetic_data.py [-h] [--output_file OUTPUT_FILE] [--label_dim LABEL_DIM] @@ -197,9 +197,6 @@ 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 @@ -319,6 +316,11 @@ 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) diff --git a/model_zoo/official/recommend/wide_and_deep_multitable/README.md b/model_zoo/official/recommend/wide_and_deep_multitable/README.md new file mode 100644 index 0000000000000000000000000000000000000000..77223d2a283f154dea4dc185f6fd1b50759d1b77 --- /dev/null +++ b/model_zoo/official/recommend/wide_and_deep_multitable/README.md @@ -0,0 +1,198 @@ +# 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) + - [Training Process](#training-process) + - [SingleDevice](#singledevice) + - [Distribute Training](#distribute-training) + - [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) + + +# [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. + +# [Dataset](#contents) + +- [1] A dataset used in Click Prediction + +# [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 +```bash +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: + +```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_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](#contents) + +### [Training Script Parameters](#contents) + +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](#contents) + +### [SingleDevice](#contents) + +To train and evaluate the model, command as follows: +``` +python train_and_eval.py +``` + + +### [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 +``` +## [Evaluation Process](#contents) +To evaluate the model, command as follows: +``` +python eval.py +``` + +# [Model Description](#contents) + +## [Performance](#contents) + +### 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 | 0.7.0-beta | 0.7.0-beta | +| 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](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/recommend/wide_and_deep_multitable/script) + +### Evaluation Performance + +| Parameters | Wide&Deep | +| ----------------- | --------------------------- | +| Resource | Ascend 910 | +| Uploaded Date | 08/21/2020 (month/day/year) | +| MindSpore Version | 0.7.0-beta | +| Dataset | [1] | +| Batch Size | 131072 | +| Outputs | AUC，MAP | +| Accuracy | AUC=0.7473，MAP=0.7464 | + +# [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). \ No newline at end of file