https://github.com/jeanjerome/semisupervised_timeseries_infogan

A tensorflow implementation of informative generative adversarial network (InfoGAN ) to one dimensional ( 1D ) time series data with a supervised loss function. So it's called semisupervised Info GAN.
https://github.com/jeanjerome/semisupervised_timeseries_infogan

data-augmentation gan python tensorflow time-series

Last synced: 4 months ago
JSON representation

A tensorflow implementation of informative generative adversarial network (InfoGAN ) to one dimensional ( 1D ) time series data with a supervised loss function. So it's called semisupervised Info GAN.

• Host: GitHub
• URL: https://github.com/jeanjerome/semisupervised_timeseries_infogan
• Owner: jeanjerome
• License: mit
• Created: 2018-10-20T21:07:02.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-10-21T09:08:36.000Z (over 6 years ago)
• Last Synced: 2023-08-17T12:17:53.735Z (over 1 year ago)
• Topics: data-augmentation, gan, python, tensorflow, time-series
• Language: Python
• Homepage:
• Size: 3.25 MB
• Stars: 24
• Watchers: 3
• Forks: 13
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-of-time-series-augmentation - semisupervised_timeseries_infogan

README

        
# semisupervised_timeseries_infogan

A tensorflow implementation of Informative Generative Adversarial Network (**InfoGAN** ) to one dimensional ( **1D** ) time series data with a **supervised loss** function.

So it's called semisupervised Info GAN. 

Refer to

1. InfoGAN ([https://arxiv.org/abs/1606.03657](https://arxiv.org/abs/1606.03657) ) 

1. AC-GAN ( Auxiliary Classifier GAN )([https://arxiv.org/abs/1610.09585](https://arxiv.org/abs/1610.09585))

This work is intended to be useful for **time series data augmentation**.

## Dependencies

1. tensorflow = 1.0.1 

1. [sugartensor](https://github.com/buriburisuri/sugartensor) = 1.0.0.2

1. matplotlib = 2.2.3

## Sample Data

You can use any csv formatted time series data as following.



time,serie,target

1,11.1,0

2,12.2,2

3,13.0,1

4,14,0,2

     .

     .

     .



This file should be saved at 'asset/data/serie_target.csv' before you train the network. 

## Training the network

Execute



python train.py



to train the network. You can see the result ckpt files and log files in the 'asset/train' directory.

Launch tensorboard --logdir asset/train/run-MMDD-HHmm to monitor training process.


## Generating sample time series data

 

Execute



python generate.py



to generate sample time series data.  The graph image of generated time series data will be saved in the 'asset/train' directory.


## Generating sample time series data

 

Execute



python generate.py



to generate sample time series data.  The graph image of generated time series data will be saved in the 'asset/train' directory.


## Generated time series data sample

This graph of time series was generated by InfoGAN network. 

You may know that it's difficult to discriminate generated time series data from real time series data.

 



  
Fake time series data

  

  



  
Decomposed time series data (depending on your data and labels)

  

  

  


## Based on 

1. [timeseries_gan](https://github.com/buriburisuri/timeseries_gan)

1. [supervised_infogan](https://github.com/buriburisuri/supervised_infogan)