A practical Approach to Handwritten Digit Generation with Generative Adversarial Networks on MNIST

Implementation of a Generative Adversarial Network (GAN) utilizing the MNIST (Modified National Institute of Standards and Technology database) dataset of handwritten digits, the GAN comprises a generator creating synthetic images from random noise and a discriminator to classifying these images as real or fake. The Generator utilizes leaky ReLU (rectified linear unit) activations, Batch normalization and reshaping to produce 28x28 grayscale images, while the Discriminator uses dense layers, leaky ReLU, and dropout to enhance classification accuracy. Both networks are trained using the Adam optimizer to improve stability and performance. The GAN is trained in an adversarial setup where the Generator seeks to create convincing images and the Discriminator aims to correctly classify them. Results demonstrate the GAN's ability to generate high-quality handwritten digit images, displaying its effectiveness and providing valuable insights into best practices for GAN implementation and training.