GAN Examples - Code Samples 🚀

Introduction to GANs

Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to generate new data that resembles the training data. This section provides code examples for implementing GANs using various architectures and techniques.

Code Structure

Below is a typical GAN implementation framework in Python using TensorFlow/Keras:

import tensorflow as tf
from tensorflow.keras import layers

# Generator model
generator = tf.keras.Sequential([
    layers.Dense(256, input_shape=(100,), activation="relu"),
    layers.Dense(512, activation="relu"),
    layers.Dense(784, activation="tanh")
])

# Discriminator model
discriminator = tf.keras.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(512, activation="relu"),
    layers.Dense(256, activation="relu"),
    layers.Dense(1, activation="sigmoid")
])

# Combined GAN model
gan = tf.keras.Sequential([generator, discriminator])

Training Process

1. Initialize models: Define generator and discriminator networks
2. Compile models: Use appropriate loss functions (e.g., binary crossentropy)
3. Train loop:
   • Generate noise samples
   • Create fake images
   • Train discriminator with real/fake data
   • Train generator to fool discriminator
4. Monitor progress: Visualize generated images periodically

Application Scenarios

• Image generation: Create realistic images from random noise
• Data augmentation: Generate synthetic data for training models
• Style transfer: Convert images between different artistic styles

Related Resources

For more information about GAN theory and implementation:
GAN Theory Documentation