GAN - Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

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All have been tested with python2.7+ and tensorflow1.0+ in linux. The final layer can be sigmoid(data: [0,1]) or tanh(data:[-1,1]), my codes all use sigmoid. Using weights_initializer=tf.random_normal_initializer(0, 0.02) will converge faster.

https://github.com/YadiraF/GAN

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