This project implements a Deep Convolutional Generative Adversarial Network (DCGAN) designed to generate realistic handwritten digits. By leveraging a generator-discriminator architecture, the DCGAN learns to produce digit images resembling those in datasets like MNIST.
A dataset containing 70,000 grayscale images of handwritten digits, each of size 28x28 pixels. It is divided into 60,000 training images and 10,000 test images, with labels ranging from 0 to 9
- Resized the images
- Converted to Tensors
- Normalized the mean and standard deviation of each channel
- Built the Discriminator
- Built the Generator
- Initialized weights to prevent mode collapse and weight explosion
- Preprocessed the Images
- Built a custom train loop
