Building an Auxiliary GAN using Keras and Tensorflow

Generative Adversarial Networks (GANs) which can be built using Keras and Tensorflow have revolutionized the field of artificial intelligence by enabling the generation of realistic and high-quality synthetic data.

In this article, we delve into the world of GANs and explore the concept of an Auxiliary GAN. With the powerful combination of Keras and TensorFlow, we demonstrate how to construct an Auxiliary GAN that incorporates additional information to enhance the generation process.

Understanding GANs

Before diving into Auxiliary GANs, it's essential to understand the basics of GANs. GANs are composed of two neural networks: a generator and a discriminator. The generator aims to create realistic samples, such as images, while the discriminator's task is to differentiate between real and generated data. Both networks continuously improve their performance through adversarial training.

What is an Auxiliary GAN?

An Auxiliary GAN, also known as ACGAN, extends the traditional GAN architecture by adding an auxiliary classifier to the discriminator. This additional classifier provides an extra level of control over the generated output. ACGANs are particularly useful when the goal is to generate data based on specific labels or classes.

Benefits of Building an Auxiliary GAN

Below are the benefits of Building an Auxiliary GAN −

Improved Data Control − By incorporating an auxiliary classifier, an ACGAN allows for the generation of data based on specific labels or classes. This control is beneficial in various applications such as image synthesis, data augmentation, and conditional data generation.

Enhanced Data Diversity − The auxiliary classifier in an ACGAN encourages the generator to explore different classes, resulting in increased data diversity. This capability is valuable when dealing with imbalanced datasets or when generating diverse samples.

Steps to Build an Auxiliary GAN using Keras and Tensorflow

Before diving into the implementation, we need to set up our development environment. We will be using Keras, a high-level deep learning library, along with TensorFlow as the backend. Ensure you have both installed on your machine.

Below are the steps that we will follow to build an Auxiliary GAN using Keras and Tensorflow −

Step 1: Importing the Required Libraries

We need to import the necessary libraries. Import Keras and TensorFlow.

Step 2: Define the Generator Network

• The generator network takes a random noise vector of size 100 as input.
• It consists of three dense layers with ReLU activation functions.
• The output layer has 784 units with a sigmoid activation function to generate an image of size 28x28x1.
• The final layer reshapes the output into the desired image shape.

Step 3: Define the Discriminator Network

• The discriminator network takes an image of size 28x28x1 as input.

• It consists of a flatten layer to convert the image into a 1D vector.

• It is followed by a dense layer with 256 units and a ReLU activation function.

• To prevent overfitting, a dropout layer with a dropout rate of 0.3 is added.

• The final layer is a dense layer with a sigmoid activation function, which outputs the probability of the input image being real or fake.

Step 4: Define the Auxiliary Network

• The auxiliary network takes the same random noise vector as input.

• It consists of two dense layers with ReLU activation functions.

• The final layer has 10 units with a softmax activation function, representing the probabilities for each digit class (0-9).

Step 5: Define the GAN Model

• The GAN model takes the random noise vector as input.

• It passes the input through the generator network to generate an image.

• The generated image is then fed into the discriminator to determine its validity.

• Simultaneously, the input noise vector is passed through the auxiliary network to predict the digit label.

• The GAN model outputs both the validity of the generated image and the predicted label.

Step 6: Print the GAN Model Summary

• The summary provides an overview of the GAN model architecture.

• It shows each layer's type, output shape, and the number of parameters.

• The total number of trainable parameters in the model is displayed.

Example

Let’s look into a working example −

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Step 1: Define the generator network
generator = keras.Sequential([
   layers.Dense(128, input_dim=100, activation="relu"),
   layers.Dense(256, activation="relu"),
   layers.Dense(784, activation="sigmoid"),
   layers.Reshape((28, 28, 1))
])

# Step 2: Define the discriminator network
discriminator = keras.Sequential([
   layers.Flatten(input_shape=(28, 28, 1)),
   layers.Dense(256, activation="relu"),
   layers.Dropout(0.3),
   layers.Dense(1, activation="sigmoid")
])

# Step 3: Define the auxiliary network
auxiliary = keras.Sequential([
   layers.Dense(128, input_dim=100, activation="relu"),
   layers.Dense(10, activation="softmax")
])

# Step 4: Define the GAN model
latent_dim = 100
gan_input = keras.Input(shape=(latent_dim,))
generated_image = generator(gan_input)
validity = discriminator(generated_image)
label = auxiliary(gan_input)

gan = keras.models.Model(gan_input, [validity, label])

# Step 5: Print the GAN model summary
gan.summary()

Output

C:\Users\Tutorialspoint>python mtt.py
Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to
==================================================================================================
 input_1 (InputLayer)           [(None, 100)]        0           []

 sequential (Sequential)        (None, 28, 28, 1)    247440      ['input_1[0][0]']

 sequential_1 (Sequential)      (None, 1)            201217      ['sequential[0][0]']

 sequential_2 (Sequential)      (None, 10)           14218       ['input_1[0][0]']

==================================================================================================
Total params: 462,875
Trainable params: 462,875
Non-trainable params: 0

Conclusion

Building an Auxiliary GAN using Keras and TensorFlow opens up exciting possibilities in data generation tasks. The incorporation of an auxiliary classifier enhances data control and diversity, making ACGANs a valuable tool in various applications. By following the step-by-step guide provided in this article, you can start creating your own Auxiliary GANs and explore the creative potential of generative models.