Improved Wasserstein GAN model

Improved Wasserstein generative adverserial network (with gradient penalty) from the paper:

Details

https://arxiv.org/abs/1704.00028

and ported from the Keras (python) implementation:

https://github.com/eriklindernoren/Keras-GAN/blob/master/wgan_gp/wgan_gp.py

Arguments

inputImageSize

latentDimension

Details

$initialize instantiates a new class and builds the generator and critic. $buildGeneratorbuild generator. $buildGeneratorbuild critic.

Author

Tustison NJ

Examples

library( keras )
library( ANTsRNet )

keras::backend()$clear_session()
#> Error in py_discover_config(required_module, use_environment): Python specified in RETICULATE_PYTHON (/Users/ntustison/anaconda3/envs/antsx/bin/python3) does not exist

# Let's use the mnist data set.

mnist <- dataset_mnist()
#> Error in py_discover_config(required_module, use_environment): Python specified in RETICULATE_PYTHON (/Users/ntustison/anaconda3/envs/antsx/bin/python3) does not exist

numberOfTrainingData <- length( mnist$train$y )
#> Error in eval(expr, envir, enclos): object 'mnist' not found

inputImageSize <- c( dim( mnist$train$x[1,,] ), 1 )
#> Error in eval(expr, envir, enclos): object 'mnist' not found

x <- array( data = mnist$train$x / 255, dim = c( numberOfTrainingData, inputImageSize ) )
#> Error in array(data = mnist$train$x/255, dim = c(numberOfTrainingData,     inputImageSize)): object 'mnist' not found
y <- mnist$train$y
#> Error in eval(expr, envir, enclos): object 'mnist' not found

numberOfClusters <- length( unique( mnist$train$y ) )
#> Error in unique(mnist$train$y): object 'mnist' not found

# Instantiate the WGAN model

ganModel <- ImprovedWassersteinGanModel$new(
   inputImageSize = inputImageSize,
   latentDimension = 100 )
#> Error in initialize(...): object 'inputImageSize' not found

if (FALSE) {
ganModel$train( x, numberOfEpochs = 2 )
}
tryCatch({tensorflow::tf$compat$v1$enable_eager_execution()},
silent = TRUE, error = function(e) {})
#> NULL