Prerequisites: Generative Adversarial Network
This article will demonstrate how to build an Auxiliary Generative Adversarial Network using the Keras and TensorFlow libraries. The dataset which is used is the MNIST Image dataset pre-loaded into Keras.
Step 1: Setting up the environment
Step 1 : Open Anaconda promt in Administrator mode. Step 2 : Create a virtual environment using the command : conda create --name acgan python=3.7 Step 3 : Then, activate the environment using the command : conda activate acgan Step 4 : Install the following libraries - 4.1 - Tensorflow --> pip install tensorflow==2.1 4.2 - Keras --> pip install keras==2.3.1
Step 2: Importing the required libraries
Step 3: Defining parameters to be used in later processes
Step 4: Defining a utility function to build the Generator
Step 5: Defining a utility function to build the Discriminator
Step 6: Defining a utility function to display the generated images
Step 7: Building and Training the AC-GAN
Step 8: Building the Generative Adversarial Network
Output (At every 2000 epoch interval):
On visually observing the progression of generated images, it can be concluded that the network is working at an acceptable level. The quality of images can be improved by training the network for more time or by tuning the parameters of the network. For any doubts/queries, comment below.