Awesome
chainer-LSGAN
An implementation of Mao et al., "Least Squares Generative Adversarial Networks" 2017 using the Chainer framework.
Disclaimer: PFN provides no warranty or support for this implementation. Use it at your own risk. See license for details.
Results
CIFAR10 & MNIST for 100 epochs
<p align="center"> <img src="images/CIFAR10_epoch100.png" height="480" width="480" alt="CIFAR10"/> <img src="images/MNIST_epoch100.png" height="480" width="480" alt="MNIST"/> </p>Usage
Tested using python 3.5.1. Install the requirements first:
pip install -r requirements.txt
Trains on the CIFAR10 dataset by default, and will generate an image of a sample batch from the network after each epoch. Run the following:
python train.py --device_id 0
to train. By default, an output folder will be created in your current working directory. Setting --device_id to -1 will run in CPU mode, whereas 0 will run on GPU number 0 etc. To train on MNIST, use the flag --mnist.
License
MIT License. Please see the LICENSE file for details.