Collab_learner
Create a Learner for collaborative filtering on 'dls'.
collab_learner( dls, n_factors = 50, use_nn = FALSE, emb_szs = NULL, layers = NULL, config = 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) )
dls |
a data loader object |
n_factors |
The number of factors |
use_nn |
use_nn |
emb_szs |
embedding size |
layers |
list of layers |
config |
configuration |
y_range |
y_range |
loss_func |
It can be any loss function you like. It needs to be one of fastai's if you want to use Learn.predict or Learn.get_preds, or you will have to implement special methods (see more details after the BaseLoss documentation). |
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). |
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_MOVIE_LENS_ML_100k()
c(user,item,title) %<-% list('userId','movieId','title')
ratings = fread('ml-100k/u.data', col.names = c(user,item,'rating','timestamp'))
movies = fread('ml-100k/u.item', col.names = c(item, 'title', 'date', 'N', 'url',
paste('g',1:19,sep = '')))
rating_movie = ratings[movies[, .SD, .SDcols=c(item,title)], on = item]
dls = CollabDataLoaders_from_df(rating_movie, seed = 42, valid_pct = 0.1, bs = 64,
item_name=title, path='ml-100k')
learn = collab_learner(dls, n_factors = 40, y_range=c(0, 5.5))
learn %>% fit_one_cycle(1, 5e-3, wd = 1e-1)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.