GAN Learner from learners — GANLearner_from

Create a GAN from `learn_gen` and `learn_crit`.

GANLearner_from_learners(
  gen_learn,
  crit_learn,
  switcher = NULL,
  weights_gen = NULL,
  gen_first = FALSE,
  switch_eval = TRUE,
  show_img = TRUE,
  clip = NULL,
  cbs = NULL,
  metrics = NULL,
  loss_func = 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

gen_learn: generator learner
crit_learn: discriminator learner
switcher: switcher
weights_gen: weights generator
gen_first: generator first
switch_eval: switch evaluation
show_img: show image or not
clip: clip value
cbs: Cbs is one or a list of Callbacks to pass to the Learner.
metrics: It is an optional list of metrics, that can be either functions or Metrics.
loss_func: loss function
opt_func: The function used to create the optimizer
lr: learning rate
splitter: It is a function that takes self.model and returns a list of parameter groups (or just one parameter group if there are no different parameter groups).
path: The folder where to work
model_dir: Path and model_dir are used to save and/or load models.
wd: It is the default weight decay used when training the model.
wd_bn_bias: It controls if weight decay is applied to BatchNorm layers and bias.
train_bn: It controls if BatchNorm layers are trained even when they are supposed to be frozen according to the splitter.
moms: The default momentums used in Learner$fit_one_cycle.

Value

None