Cnn_learner
Build a convnet style learner from 'dls' and 'arch'
cnn_learner( dls, arch, loss_func = NULL, pretrained = TRUE, cut = NULL, splitter = NULL, y_range = NULL, config = NULL, n_out = NULL, normalize = TRUE, opt_func = Adam(), lr = 0.001, 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) )
dls |
data loader object |
arch |
a model architecture |
loss_func |
loss function |
pretrained |
pre-trained or not |
cut |
cut |
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). |
y_range |
y_range |
config |
configuration |
n_out |
the number of out |
normalize |
normalize |
opt_func |
The function used to create the optimizer |
lr |
learning rate |
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. |
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. |
learner object
## Not run: URLs_MNIST_SAMPLE() # transformations tfms = aug_transforms(do_flip = FALSE) path = 'mnist_sample' bs = 20 #load into memory data = ImageDataLoaders_from_folder(path, batch_tfms = tfms, size = 26, bs = bs) learn = cnn_learner(data, resnet18(), metrics = accuracy, path = getwd()) ## End(Not run)
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