Wgan — GANLearner_wgan • fastai

Create a WGAN from `data`, `generator` and `critic`.

GANLearner_wgan(
  dls,
  generator,
  critic,
  switcher = NULL,
  clip = 0.01,
  switch_eval = FALSE,
  gen_first = FALSE,
  show_img = TRUE,
  cbs = NULL,
  metrics = NULL,
  opt_func = Adam(),
  lr = 0.001,
  splitter = trainable_params,
  path = NULL,
  model_dir = "models",
  wd = NULL,
  wd_bn_bias = FALSE,
  train_bn = TRUE,
  moms = list(0.95, 0.85, 0.95)
)

Arguments

dls: dataloader
generator: generator
critic: critic
switcher: switcher
clip: clip value
switch_eval: switch evaluation
gen_first: generator first
show_img: show image or not
cbs: callbacks
metrics: metrics
opt_func: optimization function
lr: learning rate
splitter: splitter
path: path
model_dir: model directory
wd: weight decay
wd_bn_bias: weight decay bn bias
train_bn: It controls if BatchNorm layers are trained even when they are supposed to be frozen according to the splitter.
moms: momentums

Value

None

Examples

if (FALSE) {

learn = GANLearner_wgan(dls, generator, critic, opt_func = partial(Adam(), mom=0.))

}