Generative adversarial networks (GANs) are a powerful type of machine learning model that can be used to generate new, synthetic data that is similar to a training datasets. They consist of two networks: a generator network and a discriminator network. The generator network is responsible for generating new data, while the discriminator network is responsible for determining whether the data is real or generated.
Source :Google Search Image Following are the steps you need to understand before jumping directly to the coding of the GAN network: This is just a high-level overview of the process for implementing a GAN using PyTorch. There are many additional details and considerations involved, such as preprocessing the data, handling dependencies, and debugging the code
First, you will need to install PyTorch and any other necessary libraries. Next, you will need to define the generator and discriminator networks. This will typically involve defining the architecture of the networks, such as the number of layers and the type of layers, as well as the input and output dimensions. Once the networks are defined, you will need to define the loss function and optimizers for each network. The loss function is used to measure the performance of the networks, while the optimizers are used to update the weights of the networks based on the loss. Next, you will need to define the training loop. This will involve iterating through the training data, feeding it to the networks, and updating the weights based on the loss and optimizers. Finally, you can use the trained generator network to generate new data by providing it with a random noise vector as input.