This project implements a Generative Adversarial Network (GAN) using PyTorch, a widely-used deep learning framework. The GAN architecture consists of two competing neural networks: the Generator and the Discriminator. The Generator aims to create realistic synthetic data that mimics a given dataset, while the Discriminator's role is to distinguish between real and generated data. This adversarial process drives both networks to improve their performance over time.
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Data Generation: The Generator learns to produce high-quality images resembling those from the training dataset, specifically focusing on generating handwritten digits from the MNIST dataset.
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Discriminator Training: The Discriminator is trained to effectively classify real and generated images, enhancing its accuracy through adversarial training.
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Loss Function: The project utilizes Binary Cross-Entropy Loss to measure the performance of both networks, enabling effective learning via backpropagation.
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Optimizers: Adam optimizers are employed for both the Generator and the Discriminator, ensuring stable and efficient training dynamics.
The model is trained on the MNIST dataset, which consists of 70,000 images of handwritten digits (0-9) in grayscale format, each with a resolution of 28x28 pixels. The dataset is divided into training and testing sets, with the training set used for model training and the testing set for evaluation.
After training, the model generates synthetic images that can be visually evaluated using Matplotlib. This allows for a comparison between real and generated images, showcasing the effectiveness of the GAN model.