Intelligent Mobile Projects with TensorFlow

Building and training GAN models with TensorFlow

In general, a GAN model has two neural networks: G for the generator and D for the discriminator. x is some real data input from the training set, and z is random input noise. During the training, D(x) is the probability of x being real and D tries to make D(x) close to 1; G(z) is the generated output with the random input z, and D tries to make D(G(z)) close to 0, but at the same time, G tries to make D(G(z)) close to 1. Now, let's first see how we can build a basic GAN model in TensorFlow and Python that can write or generate handwritten digits.