gan_update_step: gan_update_step

In RGAN: Generative Adversarial Nets (GAN) in R

gan_update_step

Description

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

Usage

gan_update_step(
  data,
  batch_size,
  noise_dim,
  sample_noise,
  device = "cpu",
  g_net,
  d_net,
  g_optim,
  d_optim,
  value_function,
  weight_clipper
)

Arguments

data	Input a data set. Needs to be a matrix, array, torch::torch_tensor or torch::dataset.
batch_size	The number of training samples selected into the mini batch for training. Defaults to 50.
noise_dim	The dimensions of the GAN noise vector z. Defaults to 2.
sample_noise	A function to sample noise to a torch::tensor
device	Input on which device (e.g. "cpu" or "cuda") training should be done. Defaults to "cpu".
g_net	The generator network. Expects a neural network provided as torch::nn_module. Default is NULL which will create a simple fully connected neural network.
d_net	The discriminator network. Expects a neural network provided as torch::nn_module. Default is NULL which will create a simple fully connected neural network.
g_optim	The optimizer for the generator network. Expects a torch::optim_xxx function, e.g. torch::optim_adam(). Default is NULL which will setup torch::optim_adam(g_net$parameters, lr = base_lr).
d_optim	The optimizer for the generator network. Expects a torch::optim_xxx function, e.g. torch::optim_adam(). Default is NULL which will setup torch::optim_adam(g_net$parameters, lr = base_lr * ttur_factor).
value_function	The value function for GAN training. Expects a function that takes discriminator scores of real and fake data as input and returns a list with the discriminator loss and generator loss. For reference see: . For convenience three loss functions "original", "wasserstein" and "f-wgan" are already implemented. Defaults to "original".
weight_clipper	The wasserstein GAN puts some constraints on the weights of the discriminator, therefore weights are clipped during training.

Value

A function

RGAN documentation built on March 30, 2022, 1:07 a.m.