The **generator-discriminator principle** is central to the functioning of Generative Adversarial Networks (GANs).   

## Generator 
This network generates **new data instances**. The goal of the generator is to produce data that is indistinguishable from real data. It takes random noise as input and transforms it into a data sample that mimics the real data distribution.
```python
# Define discriminator
def build_discriminator(in_shape=(28, 28, 1)):
    input_img = Input(shape=in_shape)

    x = Conv2D(32, (3, 3), padding='same', activation='elu')(input_img)
    x = MaxPooling2D((2, 2))(x)

    x = Conv2D(64, (3, 3), padding='same', activation='elu')(x)
    x = MaxPooling2D((2, 2))(x)

    x = Conv2D(128, (3, 3), padding='same', activation='elu')(x)
    x = MaxPooling2D((2, 2))(x)

    x = Flatten()(x)
    x = Dense(256)(x)

    x = Dense(1, activation='sigmoid')(x)

    return Model(inputs=input_img, outputs=x)
```

## Discriminator
This network **evaluates the authenticity of the data**. It takes a data instance as input and outputs a probability that indicates whether the input data is real (from the true data distribution) or fake (generated by the generator).
```python
# Define generator
def build_generator(latent_dim):
    input_noise = Input(shape=(latent_dim,))

    x = Dense(7 * 7 * 1024, activation = 'elu')(input_noise)
    x = Reshape((7, 7, 1024))(x)

    x = Conv2D(512, (2, 2), padding='same', activation = 'elu')(x)
    x = Conv2D(256, (2, 2), padding='same', activation = 'elu')(x)
    x = UpSampling2D()(x)

    x = Conv2D(128, (3, 3), padding='same', activation = 'elu')(x)
    x = Conv2D(64, (2, 2), padding='same', activation = 'elu')(x)
    x = UpSampling2D()(x)

    x = Conv2D(32, (4, 4), padding='same', activation = 'elu')(x)

    x = Conv2D(1, (3, 3), padding='same', activation='sigmoid')(x)

    return Model(inputs=input_noise, outputs=x)
```

During training, the generator and discriminator are updated in alternation. Typically, for each iteration:

- the discriminator is updated by maximizing the probability of correctly classifying real and fake samples;
- the generator is updated by minimizing the probability that the discriminator correctly identifies its samples as fake.

This adversarial process continues until the generator produces data that is realistic enough that the **discriminator cannot reliably distinguish between real and fake data**, ideally achieving a balance where both networks are optimized.

Discover the artistry behind image generation using generative networks in our course. From theory to practice, delve into the world of generative networks, unlocking the secrets to crafting captivating images from data.

In this section, we'll start by understanding different types of generative models. We'll look into autoencoders, which transform data, GANs, which use a competitive approach, and diffusion networks, which spread out data. You'll learn how each model generates data differently, paving the way for further exploration in generative networks.

We will implement a simple Variational Autoencoder (VAE) using the MNIST dataset. We will build and train the VAE model, which includes designing the encoder and decoder networks, sampling from the latent space using the reparameterization trick, and optimizing the combined reconstruction and Kullback-Leibler divergence loss.

Now we will explore the generator-discriminator principle fundamental to GANs, implement a simple GAN model, and examine various GAN variations. Additionally, we will provide an overview of some existing state-of-the-art image generation models.

Image Synthesis Through Generative Networks

Generator - Discriminator Principle

Generator

Discriminator