Hierarchical-GAN

License

This code is licensed under the MIT license. Feel free to use all or portions for your research or related projects so long as you provide the following citation information:

Chen W, Fuge M. Synthesizing Designs With Inter-Part Dependencies Using Hierarchical Generative Adversarial Networks. ASME. J. Mech. Des. 2019. (Accepted)

@article{chen2019hgan,
    author={Chen, Wei and Fuge, Mark},
    title={Synthesizing Designs with Inter-part Dependencies Using Hierarchical Generative Adversarial Networks},
    journal={Journal of Mechanical Design},
    volume={141},
    number={11},
    pages={111403},
    year={2019},
    publisher={American Society of Mechanical Engineers}
}

Required packages

• tensorflow-1.6.0
• numpy
• matplotlib

Usage Example

Generate the dataset of AHH:

cd AHH
python build_data.py

Train/evaluate HGAN:

python run_<n>parts.py

positional arguments:

mode	startover or evaluate
data	dataset

optional arguments:

-h, --help            	show this help message and exit
--sample_size		sample size
--save_interval 	number of intervals for saving the trained model and plotting results

Example: train HGAN on AHH:

python run_3parts.py startover AHH --sample_size=10000 --save_interval=500

Train/evaluate InfoGAN:

python run_infogan.py

positional arguments:

mode	startover or evaluate
data	dataset

optional arguments:

-h, --help            	show this help message and exit
--sample_size		sample size
--save_interval 	number of intervals for saving the trained model and plotting results