Two networks competing to create realistic data.

What are GANs?

Generator creates fake data, Discriminator judges if it's real.

**Idea**: They improve each other through competition!

How GANs Work

**Generator**: Creates fake images **Discriminator**: Detects real vs fake **Training**: Generator tries to fool Discriminator

Simple GAN Example

```python import tensorflow as tf from tensorflow import keras

Generator model def make_generator(): model = keras.Sequential([ keras.layers.Dense(256, activation='relu', input_dim=100), keras.layers.Dense(512, activation='relu'), keras.layers.Dense(1024, activation='relu'), keras.layers.Dense(784, activation='sigmoid') # 28x28 image ]) return model

Discriminator model def make_discriminator(): model = keras.Sequential([ keras.layers.Dense(1024, activation='relu', input_dim=784), keras.layers.Dropout(0.3), keras.layers.Dense(512, activation='relu'), keras.layers.Dropout(0.3), keras.layers.Dense(256, activation='relu'), keras.layers.Dense(1, activation='sigmoid') # Real or Fake ]) return model

generator = make_generator() discriminator = make_discriminator()

Compile discriminator.compile(optimizer='adam', loss='binary_crossentropy') ```

Training GANs

```python import numpy as np

Prepare real data (example with MNIST) (X_train, _), (_, _) = keras.datasets.mnist.load_data() X_train = X_train.reshape(-1, 784) / 255.0

batch_size = 128 epochs = 10000

for epoch in range(epochs): # Train Discriminator # 1. Real images idx = np.random.randint(0, X_train.shape[0], batch_size) real_images = X_train[idx] real_labels = np.ones((batch_size, 1)) # 2. Fake images noise = np.random.normal(0, 1, (batch_size, 100)) fake_images = generator.predict(noise) fake_labels = np.zeros((batch_size, 1)) # Train discriminator d_loss_real = discriminator.train_on_batch(real_images, real_labels) d_loss_fake = discriminator.train_on_batch(fake_images, fake_labels) # Train Generator noise = np.random.normal(0, 1, (batch_size, 100)) misleading_labels = np.ones((batch_size, 1)) # Fool discriminator! # Combined model discriminator.trainable = False combined = keras.Sequential([generator, discriminator]) combined.compile(optimizer='adam', loss='binary_crossentropy') g_loss = combined.train_on_batch(noise, misleading_labels) discriminator.trainable = True if epoch % 1000 == 0: print(f"Epoch {epoch}, D Loss: {(d_loss_real + d_loss_fake)/2:.4f}, G Loss: {g_loss:.4f}") ```

Types of GANs

**DCGAN**: Uses convolutional layers **StyleGAN**: High-quality face generation **CycleGAN**: Image-to-image translation **Pix2Pix**: Paired image translation

Applications

- Generate realistic faces - Create art and designs - Data augmentation - Image enhancement

Remember

- GANs are hard to train (unstable) - Require lots of data and compute - Generator and Discriminator must balance - Used for creative AI applications