expert_sample_synthetic_data

A torch::nn_module with a Generator

g_net

The device on which synthetic data should be sampled (cpu or cuda)

device

Should dropout be applied during inference

eval_dropout

Provides a function that makes it easy to sample synthetic data from a Generator

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.

expert_sample_synthetic_data: Sample Synthetic Data with explicit noise input

Description

Usage

Arguments

Value