Welcome to the GANs tutorial! This guide will walk you through building a Generative Adversarial Network using PyTorch. GANs are a class of deep learning algorithms used to generate new data that resembles the training data, such as images, text, or audio.

🧠 What is a GAN?

A GAN consists of two neural networks:

  • Generator: Creates synthetic data (e.g., images) from random noise.
  • Discriminator: Judges whether the data is real or fake.

They compete in a zero-sum game, improving together through adversarial training.

GAN_Structure

🛠️ Step-by-Step Implementation

  1. Install PyTorch: Get started with PyTorch
  2. Define Networks:
    • Use nn.Module for custom architectures.
    • Example: Convolutional Neural Network for image generation.
  3. Training Loop:
    • Alternate between generator and discriminator updates.
    • Optimize using Adam optimizer and BCELoss for binary classification.

📜 Example Code

import torch
from torch import nn

class Generator(nn.Module):
    def __init__(self):
        super().__init__()
        self.model = nn.Sequential(
            nn.Linear(100, 256),
            nn.ReLU(),
            nn.Linear(256, 512),
            nn.ReLU(),
            nn.Linear(512, 784),
            nn.Tanh()
        )
    def forward(self, z):
        return self.model(z)

📈 Visualizing Results

  • After training, generate samples using the Generator network.
  • Plot the loss curves to monitor convergence:
    Loss_Function_Curve

📘 Further Reading

For advanced topics like StyleGAN or CycleGAN, check out:
/tools/pytorch/advanced_topics

Let me know if you need help with training tips or dataset preparation! 🚀