This is a simple implementation of a Generative Adversarial Network (GAN) to generate handwritten digits from the MNIST dataset. The GAN is implemented in PyTorch.
I just implemented the training loop and the generator and discriminator networks. By running the training loop, you could know the basic idea of how GAN works.
In the training process, I noticed that the loss of the generator are growing up slowly. I think it is because the discriminator is too strong.
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loss of generator:
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loss of discriminator:
But in the global view, the generator and discriminator are balanced. And the output image is good.
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output:
