The primary goal of the encoder network is to generate a latent vector of the provided images. Basically, it takes an image of a dimension of (64, 64, 3) and converts it into a 100-dimensional vector. The encoder network is a deep convolutional neural network. The network contains four convolutional blocks and two dense layers. Each convolutional block contains a convolutional layer, a batch normalization layer, and an activation function. In each convolutional block, each convolutional layer is followed by a batch normalization layer, except the first convolutional layer. The configuration of the encoder network will be covered in the Keras implementation of Age-cGAN section.