GAN Learner from learners
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) )
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. |
None
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