time,series1,series2
1,11.1,21.1
2,12.2,22.2
3,13.0,23.1
.
.
.
This file should be saved at 'asset/data/sample.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/log 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.
## 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.
Real time series data
Fake time series data
Decomposed time series data
## Other resources 1. [Original GAN tensorflow implementation](https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_gan.py) 1. [InfoGAN tensorflow implementation](https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/example/mnist_info_gan.py) 1. [EBGAN tensorflow implementation](https://github.com/buriburisuri/ebgan) # Authors Namju Kim (njkim@jamonglab.com) at Jamonglabs Co., Ltd.