I implemented GANs(Generative Adversarial Networks) because I want to learn about GANs with TensorFlow.
I referred to here.
And this is my code implemented GANs. This code almost same what I referred.
My GANs repeatedly learn with MNIST handwritten image 100k times.
These are the learned images 0, 1k, 50k, 100k steps.
The 0 step image is just noise.
The 1k steps image is just noise yet.
The 5k steps image look like a handwritten image, but it is a little bit noisy.
50k and 100k steps
The 50k and 100k steps image pretty look like a handwritten image.
Above implementation is the vanilla GAN. So by using extended GAN like DCGAN would be able to increase the precision of generated handwritten image. And I'll try to use some other training data.
However, I am going to implement GAN on iOS with CoreML before I use DCGAN and other training images.