GAN_update_plot_image

The dimensions of the grid of images to be plotted

mfrow

The synthetic data (images) to be plotted

synth_data

Provides a function to send the output of a DataTransformer to
a torch tensor, so that it can be accessed during GAN training.

An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially
described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by
comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two
neural networks play an adversarial minimax game.
Built-in GAN models make the training of GANs in R possible in one line and make it easy to
experiment with different design choices (e.g. different network architectures, value functions, optimizers).
The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data.
Methods to post-process the output of GAN models to enhance the quality of samples are available.

GAN_update_plot_image: GAN_update_plot_image

Description

Usage

Arguments

Value