Basic DeepLearning Models

I build it for study deep learning model with pytorch

Usage

All code started with train.py, we use config file to differentiate the model we used.

Just like: python train -c model_config.json

For your owner useage:

• write your owner dataSet under ./data_loader and add it to ./data_loader/data_loaders.py
• write your owner config file under ./configs to choose model and set parameters
• python train.py -c ./configs/config.json

For each task of your owner, you should build dataloader in ./data_loader and config the json file in ./configs

Sometimes you will get tensor type error between long\float\int, all you need is to change your dataset file __getitem__

Example

For Factorization Machine:

• write criteo_dataset.py under ./data_loader
• add it to ./data_loader/data_loaders.py

class CriteoDataLoader(BaseDataLoader):
    def __init__(self, data_dir, batch_size, shuffle=True, validation_split=0.0, num_workers=1):
        self.data_dir = data_dir
        self.dataset = CriteoDataset(self.data_dir)
        super().__init__(self.dataset, batch_size, shuffle, validation_split, num_workers)

• write config_fm.json under ./configs
• run python train.py -c ./configs/config.json

CTR Models

| Model | Reference | | ------ | ------ | | Factorization Machine | S Rendle, Factorization Machines, 2010. | | Field-aware Factorization Machine | Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015. | | DeepFM|H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.| | Wide&Deep | HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016. | | Deep Cross Network | R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017. | | xDeepFM | J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018. |

NLP Models

| Model | Reference | | ------ | ------ | | fastText | Bag of Tricks for Efficient Text Classification| | TextCNN | Convolutional Neural Networks for Sentence Classification

DataSet

|ModelType|DataSet|Source| |---|---|---| |CTR Prediction| CriteoDataset|criteo| |NLP Classify| ThucnewsDataset|THUCNews|

Performance

| Model | acc | loss | | ------ | ------ | ------ | | FM| 0.854| 0.68| | FastText| 0.998| 0.02| | TextCNN| 0.954| 0.18|

Tensorboard

You can also see the tensorboard at localhost:6006 by running tensorboard --logdir='./saved/log/fm'

Reference

Pytorch template based on: pytorch-template

Rec based on：pytorch-fm