mindspore/model_zoo/wide_and_deep

Recommendation Model

Overview

This is an implementation of WideDeep as described in the Wide & Deep Learning for Recommender System paper.

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

Dataset

The Criteo datasets are used for model training and evaluation.

Running Code

Code Structure

The entire code structure is as following:

|--- wide_and_deep/
    train_and_test.py            "Entrance of Wide&Deep model training and evaluation"
    test.py                      "Entrance of Wide&Deep model evaluation"
    train.py                     "Entrance of Wide&Deep model training"
    train_and_test_multinpu.py   "Entrance of Wide&Deep model data parallel 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"
        WideDeep.py              "Model structure"
        callbacks.py             "Callback class for training and evaluation"
        metrics.py               "Metric class"
    |--- script/                 "run shell dir"
        run_multinpu_train.sh    "run data parallel"

Train and evaluate model

To train and evaluate the model, command as follows:

python train_and_test.py

Arguments:

• --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.

To train the model in one device, command as follows:

python train.py

Arguments:

• --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.

To train the model in distributed, command as follows:

# configure environment path, RANK_TABLE_FILE, RANK_SIZE, MINDSPORE_HCCL_CONFIG_PATH before training
bash run_multinpu_train.sh

To evaluate the model, command as follows:

python test.py

Arguments:

• --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.