**Conditional GANs (cGANs)** extend the GAN framework by conditioning both the generator and discriminator on additional information. This auxiliary data can be anything from class labels, images, text, or any other contextual information the model can use to guide the generation process. By doing so, cGANs enable a more controlled and targeted generation of data.
## Generator 
The generator takes two inputs: a random noise vector and the conditioning information . These inputs are concatenated and fed into the generator network, which outputs the conditionaed generated data.
```python 
def build_generator(latent_dim, label_dim=10):
    # Input layers
    input_noise = Input(shape=(latent_dim,))
    input_label = Input(shape=(label_dim,))  # One-hot encoded label input

    # Project the noise vector to match the initial shape for convolutional operations
    x = Dense(7 * 7 * 1024, activation='elu')(input_noise)
    x = Reshape((7, 7, 1024))(x)  # Reshape to a 3D tensor (7, 7, 1024)

    # Concatenate the reshaped noise vector with the label input
    # This allows the generator to conditionally generate images based on the provided label
    x = Concatenate()([x, input_label])

    # Upsampling and convolutional layers for image generation
    x = Conv2D(512, (2, 2), padding='same', activation='elu')(x)
    x = Conv2D(256, (2, 2), padding='same', activation='elu')(x)
    x = UpSampling2D()(x)  # Upsample to (14, 14, 256)

    x = Conv2D(128, (3, 3), padding='same', activation='elu')(x)
    x = Conv2D(64, (2, 2), padding='same', activation='elu')(x)
    x = UpSampling2D()(x)  # Upsample to (28, 28, 64)

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

    # Final convolutional layer to generate the output image
    generated_image = Conv2D(1, (3, 3), padding='same', activation='sigmoid')(x)

    # Define model with noise and label inputs
    model = Model(inputs=[input_noise, input_label], outputs=generated_image)

    return model
```

## Discriminator
The discriminator also receives two inputs: the data (either real or generated) and the conditioning information. 
It processes both inputs to determine whether the data is real and if it matches the conditioning information.
```python
def build_discriminator(in_shape=(28, 28, 1), label_dim=10):
    input_img = Input(shape=in_shape)
    input_label = Input(shape=(label_dim,))  # One-hot encoded label input

    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)
    
    # Concatenate the flattened features with the label input
    x = Concatenate()([x, input_label])
    
    x = Dense(256)(x)
    x = Dense(1, activation='sigmoid')(x)

    # Define model with both image and label inputs
    model = Model(inputs=[input_img, input_label], outputs=x)
    return model
```
## Data generation
We pass a one-hot-encoded image label as an input of the trained generator network to create new data samples. 
```python 
num_images = 10
latent_dim = 100  # Example latent dimension
label_dim = 10  # Example label dimension

# Generate random noise and random conditional labels
noise = np.random.normal(0, 1, (num_images, latent_dim))
labels = np.random.randint(0, label_dim, num_images)  # Random labels for each image

# Convert labels to one-hot encoding
conditional_labels = np.zeros((num_images, label_dim))
conditional_labels[np.arange(num_images), labels] = 1

# Generate images using the conditional generator
gen_imgs = generator_model.predict([noise, conditional_labels])
````

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

Conditional GAN

Generator

Discriminator

Data generation