The discriminator network is a CNN. Let's implement the discriminator network in the Keras framework.

Perform the following steps to implement the discriminator network:

1. Start by creating two input layers, as our discriminator network will process two inputs:

# Specify hyperparameters
# Input image shape
input_shape = (64, 64, 3)
# Input conditioning variable shape
label_shape = (6,)

# Two input layers
image_input = Input(shape=input_shape)
label_input = Input(shape=label_shape)

2. Next, add a 2-D convolution block (Conv2D + Activation function) with the following configuration:
   • Filters = 64
   • Kernel size: 3
   • Strides: 2
   • Padding: same
   • Activation: LeakyReLU with alpha equal to 0.2:

x = Conv2D(64, kernel_size=3, strides=2, padding='same')(image_input)
x = LeakyReLU(alpha=0.2)(x)

3. Next, expand label_input so that it has a shape of (32, 32, 6) :

label_input1 = Lambda(expand_label_input)(label_input)

The expand_label_input function is as follows:

# The expand_label_input function
def expand_label_input(x):
    x = K.expand_dims(x, axis=1)
    x = K.expand_dims(x, axis=1)
    x = K.tile(x, [1, 32, 32, 1])
    return x

The preceding function will transform a tensor with a dimension of (6, ) to a tensor with a dimension of (32, 32, 6). 

4. Next, concatenate the transformed label tensor and the output of the last convolution layer along the channel dimension, as shown here:

x = concatenate([x, label_input1], axis=3)

5. Add a convolution block (2D convolution layer + batch normalization + activation function) with the following configuration:
   • Filters: 128
   • Kernel size: 3
   • Strides: 2
   • Padding: same
   • Batch normalization: Yes
   • Activation: LeakyReLU with alpha equal to 0.2:

x = Conv2D(128, kernel_size=3, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)

6. Next, add two more convolution blocks, as follows:

x = Conv2D(256, kernel_size=3, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)

x = Conv2D(512, kernel_size=3, strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)

7. Next, add a flatten layer:

x = Flatten()(x)

8. Next, add a dense layer (classification layer) that outputs a probability:

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

9. Finally, create a Keras model and specify the inputs and outputs for the discriminator network:

model = Model(inputs=[image_input, label_input], outputs=[x])

The entire code for the discriminator network looks as follows:

def build_discriminator():
    """
    Create a Discriminator Model with hyperparameters values defined as follows
    :return: Discriminator model
    """
    input_shape = (64, 64, 3)
    label_shape = (6,)
    image_input = Input(shape=input_shape)
    label_input = Input(shape=label_shape)

    x = Conv2D(64, kernel_size=3, strides=2, padding='same')(image_input)
    x = LeakyReLU(alpha=0.2)(x)

    label_input1 = Lambda(expand_label_input)(label_input)
    x = concatenate([x, label_input1], axis=3)

    x = Conv2D(128, kernel_size=3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)

    x = Conv2D(256, kernel_size=3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)

    x = Conv2D(512, kernel_size=3, strides=2, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)

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

    model = Model(inputs=[image_input, label_input], outputs=[x])
    return model

We have now successfully created the encoder, the generator, and the discriminator networks. In the next section, we will assemble everything and train the network.