Get a `Learner` using `dls`, with `metrics`, including a `TabularModel` created using the remaining params.
tabular_learner(
dls,
layers = NULL,
emb_szs = NULL,
config = NULL,
n_out = NULL,
y_range = NULL,
loss_func = NULL,
opt_func = Adam(),
lr = 0.001,
splitter = trainable_params(),
cbs = NULL,
metrics = NULL,
path = NULL,
model_dir = "models",
wd = NULL,
wd_bn_bias = FALSE,
train_bn = TRUE,
moms = list(0.95, 0.85, 0.95)
)It is a DataLoaders object.
layers
emb_szs
config
n_out
y_range
It can be any loss function you like.
It will be used to create an optimizer when Learner.fit is called.
It is learning rate.
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)
It is one or a list of Callbacks to pass to the Learner.
It is an optional list of metrics, that can be either functions or Metrics.
İt is used to save and/or load models.Often path will be inferred from dls, but you can override it or pass a Path object to model_dir. Make sure you can write in path/model_dir!
İt is used to save and/or load models.Often path will be inferred from dls, but you can override it or pass a Path object to model_dir. Make sure you can write in path/model_dir!
It is the default weight decay used when training the model.
It controls if weight decay is applied to BatchNorm layers and bias.
It controls if BatchNorm layers are trained even when they are supposed to be frozen according to the splitter.
The default momentums used in Learner.fit_one_cycle.
learner object