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Least Squares Generative Adversarial Network implemented in Chainer
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images adding images to readme Mar 10, 2017 Update Mar 14, 2017 Update for Chainer v2 Jul 4, 2017


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.


CIFAR10 & MNIST for 100 epochs



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 --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.


MIT License. Please see the LICENSE file for details.

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